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RJR: Recommended Bibliography 25 Jul 2025 at 01:39 Created:
Brain-Computer Interface
Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).
Created with PubMed® Query: (bci OR (brain-computer OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion
Citations The Papers (from PubMed®)
RevDate: 2025-07-24
Leveraging meaning-induced neural dynamics to detect covert cognition via EEG during natural language listening-a case series.
Frontiers in psychology, 16:1616963.
At least a quarter of adult patients with severe brain injury in a disorder of consciousness may have cognitive abilities that are hidden due to motor impairment. In this case series, we developed a tool that extracted acoustic and semantic processing biomarkers from electroencephalography recorded while participants listened to a story. We tested our method on two male adolescent survivors of severe brain injury and showed evidence of acoustic and semantic processing. Our method identifies cognitive processing while obviating demands on attention, memory, and executive function. This lays a foundation for graded assessments of cognition recovery across the spectrum of covert cognition.
Additional Links: PMID-40703721
PubMed:
Citation:
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@article {pmid40703721,
year = {2025},
author = {Alkhoury, L and O'Sullivan, J and Scanavini, G and Dou, J and Arora, J and Hamill, L and Patchell, A and Radanovic, A and Watson, WD and Lalor, EC and Schiff, ND and Hill, NJ and Shah, SA},
title = {Leveraging meaning-induced neural dynamics to detect covert cognition via EEG during natural language listening-a case series.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1616963},
pmid = {40703721},
issn = {1664-1078},
abstract = {At least a quarter of adult patients with severe brain injury in a disorder of consciousness may have cognitive abilities that are hidden due to motor impairment. In this case series, we developed a tool that extracted acoustic and semantic processing biomarkers from electroencephalography recorded while participants listened to a story. We tested our method on two male adolescent survivors of severe brain injury and showed evidence of acoustic and semantic processing. Our method identifies cognitive processing while obviating demands on attention, memory, and executive function. This lays a foundation for graded assessments of cognition recovery across the spectrum of covert cognition.},
}
RevDate: 2025-07-24
DTCNet: finger flexion decoding with three-dimensional ECoG data.
Frontiers in computational neuroscience, 19:1627819.
ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately decoding finger movement trajectories. Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. To address these challenges, this paper proposes a novel decoding method that transforms 2D ECoG data samples into 3D spatio-temporal spectrograms with time-stamped features via wavelet transform. The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. The proposed method achieved the best performance among three subjects in BCI Competition IV. Compared with existing studies, our method made the correlation coefficient between the predicted multi-finger motion trajectory and the actual multi-finger motion trajectory exceed 80% for the first time, with the highest correlation coefficient reaching 82%. This approach provides new insights and solutions for high-precision decoding of brain-machine signals, particularly in precise command control tasks, and advances the application of BCI systems in real-world neuroprosthetic control.
Additional Links: PMID-40703668
PubMed:
Citation:
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@article {pmid40703668,
year = {2025},
author = {Wang, F and Luo, Z and Lv, W and Zhu, X},
title = {DTCNet: finger flexion decoding with three-dimensional ECoG data.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1627819},
pmid = {40703668},
issn = {1662-5188},
abstract = {ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately decoding finger movement trajectories. Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. To address these challenges, this paper proposes a novel decoding method that transforms 2D ECoG data samples into 3D spatio-temporal spectrograms with time-stamped features via wavelet transform. The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. The proposed method achieved the best performance among three subjects in BCI Competition IV. Compared with existing studies, our method made the correlation coefficient between the predicted multi-finger motion trajectory and the actual multi-finger motion trajectory exceed 80% for the first time, with the highest correlation coefficient reaching 82%. This approach provides new insights and solutions for high-precision decoding of brain-machine signals, particularly in precise command control tasks, and advances the application of BCI systems in real-world neuroprosthetic control.},
}
RevDate: 2025-07-24
Editorial: Methods in brain-computer interfaces: 2023.
Frontiers in human neuroscience, 19:1647584.
Additional Links: PMID-40703402
PubMed:
Citation:
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@article {pmid40703402,
year = {2025},
author = {Borra, D and Ma, M and Martinez-Martin, E and Xia, L},
title = {Editorial: Methods in brain-computer interfaces: 2023.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1647584},
pmid = {40703402},
issn = {1662-5161},
}
RevDate: 2025-07-24
CmpDate: 2025-07-24
Meteorological, Socioeconomic, and Environmental Factors Influencing Human Brucellosis Occurrence in Yunnan, China, 2006-2021: A Bayesian Spatiotemporal Modeling Study.
Transboundary and emerging diseases, 2025:8872434.
Background: Brucellosis epidemics in Yunnan Province in southern China have increased and caused more impact in recent years. However, the epidemiological characteristics and driving factors for brucellosis have not been clearly described. The aim of this study was to analyze the spatiotemporal distribution and potential factors for human brucellosis (HB) in Yunnan Province, 2006-2021. Methods: HB data were obtained from the China National Notifiable Infectious Diseases Reporting Information System. Global spatial autocorrelation and spatial scanning statistics were used to analyze the spatial patterns of brucellosis. Zero-inflated negative binomial (ZINB) Bayesian spatiotemporal models were applied to the analysis of potential risk factors, including environmental, meteorological, and socioeconomic factors. Findings: Between 2006 and 2021, a total of 2794 brucellosis cases were reported. The central and western regions were the most severely affected. GDP showed a positive correlation with brucellosis risk when in the range 0-30.9 billion RMB, peaking with a relative risk (RR) of 13.64 (95% Bayesian credible interval [BCI]: 4.10, 49.10) at around 2.3 billion RMB. Conversely, a negative correlation was observed for GDP between 101 and 135 billion RMB, with the RR dropping to 0.14 (95% BCI: 0.01, 0.89) at 135 billion RMB. Brucellosis cases increased by 4.90% (95% BCI: 1.82%, 7.95%) per 1°C increase in temperature, while a 1° increase in slope reduced cases by 17.06% (95% BCI: 4.01%, 28.81%). Interpretation: Our findings suggest that socioeconomic factors play the greatest role in the occurrence of brucellosis in both northern and southern China; however, the effects of the environmental factors may be different between these areas. Differences in factors affecting each region need to be fully considered, and brucellosis prevention and control need to be adapted to these differences.
Additional Links: PMID-40703200
PubMed:
Citation:
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@article {pmid40703200,
year = {2025},
author = {Li, K and Zhang, J and Yu, B and Ward, MP and Liu, M and Liu, Y and Wang, Z and Chen, Z and Li, W and Wang, N and Zhao, Y and Yang, X and Yang, F and Wang, P and Zhang, Z},
title = {Meteorological, Socioeconomic, and Environmental Factors Influencing Human Brucellosis Occurrence in Yunnan, China, 2006-2021: A Bayesian Spatiotemporal Modeling Study.},
journal = {Transboundary and emerging diseases},
volume = {2025},
number = {},
pages = {8872434},
pmid = {40703200},
issn = {1865-1682},
mesh = {Humans ; China/epidemiology ; *Brucellosis/epidemiology ; Bayes Theorem ; Socioeconomic Factors ; Spatio-Temporal Analysis ; Risk Factors ; Meteorological Concepts ; Environment ; },
abstract = {Background: Brucellosis epidemics in Yunnan Province in southern China have increased and caused more impact in recent years. However, the epidemiological characteristics and driving factors for brucellosis have not been clearly described. The aim of this study was to analyze the spatiotemporal distribution and potential factors for human brucellosis (HB) in Yunnan Province, 2006-2021. Methods: HB data were obtained from the China National Notifiable Infectious Diseases Reporting Information System. Global spatial autocorrelation and spatial scanning statistics were used to analyze the spatial patterns of brucellosis. Zero-inflated negative binomial (ZINB) Bayesian spatiotemporal models were applied to the analysis of potential risk factors, including environmental, meteorological, and socioeconomic factors. Findings: Between 2006 and 2021, a total of 2794 brucellosis cases were reported. The central and western regions were the most severely affected. GDP showed a positive correlation with brucellosis risk when in the range 0-30.9 billion RMB, peaking with a relative risk (RR) of 13.64 (95% Bayesian credible interval [BCI]: 4.10, 49.10) at around 2.3 billion RMB. Conversely, a negative correlation was observed for GDP between 101 and 135 billion RMB, with the RR dropping to 0.14 (95% BCI: 0.01, 0.89) at 135 billion RMB. Brucellosis cases increased by 4.90% (95% BCI: 1.82%, 7.95%) per 1°C increase in temperature, while a 1° increase in slope reduced cases by 17.06% (95% BCI: 4.01%, 28.81%). Interpretation: Our findings suggest that socioeconomic factors play the greatest role in the occurrence of brucellosis in both northern and southern China; however, the effects of the environmental factors may be different between these areas. Differences in factors affecting each region need to be fully considered, and brucellosis prevention and control need to be adapted to these differences.},
}
MeSH Terms:
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Humans
China/epidemiology
*Brucellosis/epidemiology
Bayes Theorem
Socioeconomic Factors
Spatio-Temporal Analysis
Risk Factors
Meteorological Concepts
Environment
RevDate: 2025-07-24
CmpDate: 2025-07-24
Neuroimaging correlates of genetics in patients with Wilson's disease.
Cerebral cortex (New York, N.Y. : 1991), 35(7):.
Wilson's disease is an inherited disorder of copper metabolism. Despite significant advancements in neuroimaging studies, prior research into the pathological mechanism of Wilson's disease has ignored the crucial impact of mutation on the disease. This study examined brain imaging in relation to mutation in patients with Wilson's disease. A total of 57 Wilson's disease patients and 25 healthy controls were recruited in the current research. Patients were classified as having either the p.R778L or the p.P992L mutation (N = 43) or other mutations (N = 14). Utilizing the amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and voxel-based morphology, the brain function and structure of Wilson's disease were explored. Compared to healthy controls, Wilson's disease patients with the p.R778L or p.P992L mutation showed greater atrophy in the bilateral putamen, caudate, globus pallidus, thalamus, amygdala, insula, and hippocampus. And these patients showed altered spontaneous neural activity in many more brain regions than healthy controls in three frequency bands. Significant correlation was found between altered brain volume and Unified Wilson's Disease Rating Scale neurological subscale scores. These findings reveal the functional and structural characteristics of Wilson's disease and emphasize the importance of exploring the neuroimaging correlation of genetic mutations in Wilson's disease.
Additional Links: PMID-40702984
Publisher:
PubMed:
Citation:
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@article {pmid40702984,
year = {2025},
author = {Yu, Y and Wang, RM and Dong, Y and Jia, XZ and Wu, ZY},
title = {Neuroimaging correlates of genetics in patients with Wilson's disease.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf186},
pmid = {40702984},
issn = {1460-2199},
support = {81125009//National Natural Science Foundation of China/ ; 81701126//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholars of Zhejiang University/ ; },
mesh = {Humans ; *Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology ; Male ; Female ; Adult ; *Brain/pathology/diagnostic imaging/physiopathology ; Young Adult ; *Mutation/genetics ; Magnetic Resonance Imaging ; Neuroimaging ; Copper-Transporting ATPases/genetics ; Adolescent ; Middle Aged ; Atrophy ; },
abstract = {Wilson's disease is an inherited disorder of copper metabolism. Despite significant advancements in neuroimaging studies, prior research into the pathological mechanism of Wilson's disease has ignored the crucial impact of mutation on the disease. This study examined brain imaging in relation to mutation in patients with Wilson's disease. A total of 57 Wilson's disease patients and 25 healthy controls were recruited in the current research. Patients were classified as having either the p.R778L or the p.P992L mutation (N = 43) or other mutations (N = 14). Utilizing the amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and voxel-based morphology, the brain function and structure of Wilson's disease were explored. Compared to healthy controls, Wilson's disease patients with the p.R778L or p.P992L mutation showed greater atrophy in the bilateral putamen, caudate, globus pallidus, thalamus, amygdala, insula, and hippocampus. And these patients showed altered spontaneous neural activity in many more brain regions than healthy controls in three frequency bands. Significant correlation was found between altered brain volume and Unified Wilson's Disease Rating Scale neurological subscale scores. These findings reveal the functional and structural characteristics of Wilson's disease and emphasize the importance of exploring the neuroimaging correlation of genetic mutations in Wilson's disease.},
}
MeSH Terms:
show MeSH Terms
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Humans
*Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology
Male
Female
Adult
*Brain/pathology/diagnostic imaging/physiopathology
Young Adult
*Mutation/genetics
Magnetic Resonance Imaging
Neuroimaging
Copper-Transporting ATPases/genetics
Adolescent
Middle Aged
Atrophy
RevDate: 2025-07-24
Psychedelics, Spirituality, and Fundamentalism: A Brain Network Approach to Cognitive Flexibility and Rigidity.
ACS chemical neuroscience [Epub ahead of print].
This viewpoint reconceptualizes mysticism and fundamentalism as brain network disorders, with psychedelics like psilocybin, lysergic acid diethylamide, and N,N-dimethyltryptamine offering potential to modulate these states. By disrupting rigid neural patterns, psychedelics may foster cognitive flexibility, challenge inflexible belief systems, and offer therapeutic value for extremism and mental health disorders.
Additional Links: PMID-40702747
Publisher:
PubMed:
Citation:
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@article {pmid40702747,
year = {2025},
author = {Yang, A and Lv, X and Wang, H and Wang, X},
title = {Psychedelics, Spirituality, and Fundamentalism: A Brain Network Approach to Cognitive Flexibility and Rigidity.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00509},
pmid = {40702747},
issn = {1948-7193},
abstract = {This viewpoint reconceptualizes mysticism and fundamentalism as brain network disorders, with psychedelics like psilocybin, lysergic acid diethylamide, and N,N-dimethyltryptamine offering potential to modulate these states. By disrupting rigid neural patterns, psychedelics may foster cognitive flexibility, challenge inflexible belief systems, and offer therapeutic value for extremism and mental health disorders.},
}
RevDate: 2025-07-23
A generic non-invasive neuromotor interface for human-computer interaction.
Nature [Epub ahead of print].
Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive and universal. While diverse modalities have been developed, including keyboards, mice and touchscreens, they require interaction with a device that can be limiting, especially in on-the-go scenarios. Gesture-based systems use cameras or inertial sensors to avoid an intermediary device, but tend to perform well only for unobscured movements. By contrast, brain-computer or neuromotor interfaces that directly interface with the body's electrical signalling have been imagined to solve the interface problem[1], but high-bandwidth communication has been demonstrated only using invasive interfaces with bespoke decoders designed for single individuals[2-4]. Here, we describe the development of a generic non-invasive neuromotor interface that enables computer input decoded from surface electromyography (sEMG). We developed a highly sensitive, easily donned sEMG wristband and a scalable infrastructure for collecting training data from thousands of consenting participants. Together, these data enabled us to develop generic sEMG decoding models that generalize across people. Test users demonstrate a closed-loop median performance of gesture decoding of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task and handwriting at 20.9 words per minute. We demonstrate that the decoding performance of handwriting models can be further improved by 16% by personalizing sEMG decoding models. To our knowledge, this is the first high-bandwidth neuromotor interface with performant out-of-the-box generalization across people.
Additional Links: PMID-40702190
PubMed:
Citation:
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@article {pmid40702190,
year = {2025},
author = {Kaifosh, P and Reardon, TR and , },
title = {A generic non-invasive neuromotor interface for human-computer interaction.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40702190},
issn = {1476-4687},
abstract = {Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive and universal. While diverse modalities have been developed, including keyboards, mice and touchscreens, they require interaction with a device that can be limiting, especially in on-the-go scenarios. Gesture-based systems use cameras or inertial sensors to avoid an intermediary device, but tend to perform well only for unobscured movements. By contrast, brain-computer or neuromotor interfaces that directly interface with the body's electrical signalling have been imagined to solve the interface problem[1], but high-bandwidth communication has been demonstrated only using invasive interfaces with bespoke decoders designed for single individuals[2-4]. Here, we describe the development of a generic non-invasive neuromotor interface that enables computer input decoded from surface electromyography (sEMG). We developed a highly sensitive, easily donned sEMG wristband and a scalable infrastructure for collecting training data from thousands of consenting participants. Together, these data enabled us to develop generic sEMG decoding models that generalize across people. Test users demonstrate a closed-loop median performance of gesture decoding of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task and handwriting at 20.9 words per minute. We demonstrate that the decoding performance of handwriting models can be further improved by 16% by personalizing sEMG decoding models. To our knowledge, this is the first high-bandwidth neuromotor interface with performant out-of-the-box generalization across people.},
}
RevDate: 2025-07-23
Outcomes in Older Patients Undergoing Surgical Aortic Valve Replacement With Concomitant Procedures.
Journal of the American College of Cardiology, 86(4):280-283.
Additional Links: PMID-40701672
Publisher:
PubMed:
Citation:
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@article {pmid40701672,
year = {2025},
author = {Kundi, H and Popma, JJ and Granada, JF and Leon, MB and Kodesh, A and Ascione, G and George, I and Latib, A and Thompson, JB and Popma, A and Alu, MC and Cohen, DJ},
title = {Outcomes in Older Patients Undergoing Surgical Aortic Valve Replacement With Concomitant Procedures.},
journal = {Journal of the American College of Cardiology},
volume = {86},
number = {4},
pages = {280-283},
doi = {10.1016/j.jacc.2025.05.021},
pmid = {40701672},
issn = {1558-3597},
}
RevDate: 2025-07-23
Decoding natural visual scenes via learnable representations of neural spiking sequences.
Neural networks : the official journal of the International Neural Network Society, 192:107863 pii:S0893-6080(25)00743-9 [Epub ahead of print].
Visual input underpins cognitive function by providing the brain with essential environmental information. Neural decoding of visual scenes seeks to reconstruct pixel-level images from neural activity, a vital capability for vision restoration via brain-computer interfaces. However, extracting visual content from time-resolved spiking activity remains a significant challenge. Here, we introduce the Wavelet-Informed Spike Augmentation (WISA) model, which applies multilevel wavelet transforms to spike trains to learn compact representations that can be directly fed into deep reconstruction networks. When tested on recorded retinal spike data responding to natural video stimuli, WISA substantially improves reconstruction accuracy, especially in recovering fine-grained details. These results emphasize the value of temporal spike patterns for high-fidelity visual decoding and demonstrate WISA as a promising model for visual decoding.
Additional Links: PMID-40700800
Publisher:
PubMed:
Citation:
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@article {pmid40700800,
year = {2025},
author = {Peng, J and Jia, S and Zhang, J and Wang, Y and Yu, Z and Liu, JK},
title = {Decoding natural visual scenes via learnable representations of neural spiking sequences.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107863},
doi = {10.1016/j.neunet.2025.107863},
pmid = {40700800},
issn = {1879-2782},
abstract = {Visual input underpins cognitive function by providing the brain with essential environmental information. Neural decoding of visual scenes seeks to reconstruct pixel-level images from neural activity, a vital capability for vision restoration via brain-computer interfaces. However, extracting visual content from time-resolved spiking activity remains a significant challenge. Here, we introduce the Wavelet-Informed Spike Augmentation (WISA) model, which applies multilevel wavelet transforms to spike trains to learn compact representations that can be directly fed into deep reconstruction networks. When tested on recorded retinal spike data responding to natural video stimuli, WISA substantially improves reconstruction accuracy, especially in recovering fine-grained details. These results emphasize the value of temporal spike patterns for high-fidelity visual decoding and demonstrate WISA as a promising model for visual decoding.},
}
RevDate: 2025-07-23
A Framework for Corticomuscle Control Studies Using a Serious Gaming Approach.
Methods and protocols, 8(4): pii:mps8040074.
Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimental protocol for simultaneous EEG-EMG data acquisition, optimized for corticomuscular studies. This work introduces, for the first time, a method for assessing brain-muscle functional connectivity during the execution of a demanding motor task. By identifying neuronal sources linked to muscular activity, this methodology has the potential to advance our understanding of motor control mechanisms. These insights could contribute to improving clinical practices and fostering the development of novel brain-computer interface technologies.
Additional Links: PMID-40700312
Publisher:
PubMed:
Citation:
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@article {pmid40700312,
year = {2025},
author = {Correia, P and Quintão, C and Quaresma, C and Vigário, R},
title = {A Framework for Corticomuscle Control Studies Using a Serious Gaming Approach.},
journal = {Methods and protocols},
volume = {8},
number = {4},
pages = {},
doi = {10.3390/mps8040074},
pmid = {40700312},
issn = {2409-9279},
support = {UI/BD/151321/2021//Fundação para a Ciência e Tecnologia (FCT, Portugal)/ ; },
abstract = {Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimental protocol for simultaneous EEG-EMG data acquisition, optimized for corticomuscular studies. This work introduces, for the first time, a method for assessing brain-muscle functional connectivity during the execution of a demanding motor task. By identifying neuronal sources linked to muscular activity, this methodology has the potential to advance our understanding of motor control mechanisms. These insights could contribute to improving clinical practices and fostering the development of novel brain-computer interface technologies.},
}
RevDate: 2025-07-23
Dissecting Social Working Memory: Neural and Behavioral Evidence for Externally and Internally Oriented Components.
Neuroscience bulletin [Epub ahead of print].
Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.
Additional Links: PMID-40699544
PubMed:
Citation:
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@article {pmid40699544,
year = {2025},
author = {Pan, H and Chen, Z and Xu, N and Wang, B and Hu, Y and Zhou, H and Perry, A and Kong, XZ and Shen, M and Gao, Z},
title = {Dissecting Social Working Memory: Neural and Behavioral Evidence for Externally and Internally Oriented Components.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40699544},
issn = {1995-8218},
abstract = {Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.},
}
RevDate: 2025-07-23
Toward the Clinical Translation of Implantable Brain-Computer Interfaces for Motor Impairment: Research Trends and Outcome Measures.
Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Epub ahead of print].
Implantable brain-computer interfaces (iBCIs) decode neural signals to control external effectors, offering potential to restore function in individuals with severe motor impairments, such as loss of limb function or speech. This systematic review examines the evolution of iBCI research and key bottlenecks to clinical translation, particularly the absence of standardized, clinically meaningful outcome measures. A comprehensive search of MEDLINE, Embase, and CINAHL identifies 112 studies, nearly half (49.1%) published since 2020. Eighty unique iBCI participants were identified, providing the most up-to-date estimate of global users. Research remains concentrated in the United States (83%), with growing contributions from Europe, China, and Australia. Electrocorticography (ECoG)-based devices increasingly emerge alongside micro-electrode arrays. iBCI devices are now being used to control a broader range of effectors, including robotic prosthetics and digital technologies. Although most (69.6%) studies reported outcome measures prospectively, these primarily related to decoding (69.6%) and task performance (62.5%), with only 17.9% assessing clinical outcomes. When cassessed, clinical outcomes were highly heterogeneous due to varied approaches across target populations. iBCIs show potential to restore functional independence at scale. However, challenges remain around cross-subject generalization, scalable implantation, and outcome standardization. Novel measures should be developed collaboratively with engineers, clinicians, and individuals with lived experience of motor impairment.
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@article {pmid40697162,
year = {2025},
author = {Dohle, E and Swanson, E and Jovanovic, L and Yusuf, S and Thompson, L and Horsfall, HL and Muirhead, W and Bashford, L and Brannigan, J},
title = {Toward the Clinical Translation of Implantable Brain-Computer Interfaces for Motor Impairment: Research Trends and Outcome Measures.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e01912},
doi = {10.1002/advs.202501912},
pmid = {40697162},
issn = {2198-3844},
support = {FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; //Rosetrees Trust and Stoneygate Trust/ ; },
abstract = {Implantable brain-computer interfaces (iBCIs) decode neural signals to control external effectors, offering potential to restore function in individuals with severe motor impairments, such as loss of limb function or speech. This systematic review examines the evolution of iBCI research and key bottlenecks to clinical translation, particularly the absence of standardized, clinically meaningful outcome measures. A comprehensive search of MEDLINE, Embase, and CINAHL identifies 112 studies, nearly half (49.1%) published since 2020. Eighty unique iBCI participants were identified, providing the most up-to-date estimate of global users. Research remains concentrated in the United States (83%), with growing contributions from Europe, China, and Australia. Electrocorticography (ECoG)-based devices increasingly emerge alongside micro-electrode arrays. iBCI devices are now being used to control a broader range of effectors, including robotic prosthetics and digital technologies. Although most (69.6%) studies reported outcome measures prospectively, these primarily related to decoding (69.6%) and task performance (62.5%), with only 17.9% assessing clinical outcomes. When cassessed, clinical outcomes were highly heterogeneous due to varied approaches across target populations. iBCIs show potential to restore functional independence at scale. However, challenges remain around cross-subject generalization, scalable implantation, and outcome standardization. Novel measures should be developed collaboratively with engineers, clinicians, and individuals with lived experience of motor impairment.},
}
RevDate: 2025-07-22
Author Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.
Additional Links: PMID-40696184
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@article {pmid40696184,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Author Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41586-025-09404-1},
pmid = {40696184},
issn = {1476-4687},
}
RevDate: 2025-07-22
Speech mode classification from electrocorticography: transfer between electrodes and participants.
Journal of neural engineering [Epub ahead of print].
Objective Speech brain-computer interfaces aim to restore communication for individuals who have lost the ability to speak by interpreting their brain activity and decoding the intended speech. As an initial component of these decoders, speech detectors have been developed to distinguish between the intent to speak and silence. However, it is important that these detectors account for real-life scenarios in which users may engage language-related brain areas-such as during reading or listening-without any intention to speak. Approach In this study, we analyze the interplay between different speech modes: speaking, listening, imagining speaking, reading and mouthing. We gathered a large dataset of 29 participants implanted with electrocorticography electrodes and developed a speech mode classifier. We also assessed how well classifiers trained on data from a specific participant transfer to other participants, both in the case of a single- and multi-electrode classifier. Main Results High accuracy was achieved using linear classifiers, for both single-electrode and multi-electrode configurations. Single-electrode classification reached 88.89% accuracy and multi-electrode classification 96.49% accuracy in distinguishing among three classes (speaking, listening, and silence). The best performing electrodes were located on the superior temporal gyrus and sensorimotor cortex. We found that single-electrode classifiers could be transferred across recording sites. For multi-electrode classifiers, we observed that transfer performance was higher for binary classifiers compared to multiclass classifiers, with the optimal source subject of the binary classifiers depending on the speech modes being classified. Significance Accurately detecting speech from brain signals is essential to prevent spurious outputs from a speech brain-computer interface and to advance its use beyond lab settings. To achieve this objective, the transfer between participants is particularly valuable as it can reduce training time, especially in cases where subject training is challenging.
Additional Links: PMID-40695313
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@article {pmid40695313,
year = {2025},
author = {de Borman, A and Wittevrongel, B and Van Dyck, B and Van Rooy, K and Carrette, E and Meurs, A and Van Roost, D and Van Hulle, MM},
title = {Speech mode classification from electrocorticography: transfer between electrodes and participants.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf2de},
pmid = {40695313},
issn = {1741-2552},
abstract = {Objective Speech brain-computer interfaces aim to restore communication for individuals who have lost the ability to speak by interpreting their brain activity and decoding the intended speech. As an initial component of these decoders, speech detectors have been developed to distinguish between the intent to speak and silence. However, it is important that these detectors account for real-life scenarios in which users may engage language-related brain areas-such as during reading or listening-without any intention to speak. Approach In this study, we analyze the interplay between different speech modes: speaking, listening, imagining speaking, reading and mouthing. We gathered a large dataset of 29 participants implanted with electrocorticography electrodes and developed a speech mode classifier. We also assessed how well classifiers trained on data from a specific participant transfer to other participants, both in the case of a single- and multi-electrode classifier. Main Results High accuracy was achieved using linear classifiers, for both single-electrode and multi-electrode configurations. Single-electrode classification reached 88.89% accuracy and multi-electrode classification 96.49% accuracy in distinguishing among three classes (speaking, listening, and silence). The best performing electrodes were located on the superior temporal gyrus and sensorimotor cortex. We found that single-electrode classifiers could be transferred across recording sites. For multi-electrode classifiers, we observed that transfer performance was higher for binary classifiers compared to multiclass classifiers, with the optimal source subject of the binary classifiers depending on the speech modes being classified. Significance Accurately detecting speech from brain signals is essential to prevent spurious outputs from a speech brain-computer interface and to advance its use beyond lab settings. To achieve this objective, the transfer between participants is particularly valuable as it can reduce training time, especially in cases where subject training is challenging.},
}
RevDate: 2025-07-22
CmpDate: 2025-07-22
[Improve athletes' performance with neurofeedback].
Biologie aujourd'hui, 219(1-2):51-58.
In order to optimise their performance, athletes are looking for innovative, efficient and reliable training approaches. The development of electroencephalography and neurofeedback (NF) offers the opportunity to create innovative cognitive training procedures. Indeed, these technologies allow athletes to benefit from a feedback during mental training sessions and to objectively assess performance and progress. In addition, NF makes it possible to guide the athletes towards optimal cognitive strategies according to their objectives, and has a motivational dimension that pushes them to engage in the sessions. We first introduce the usefulness of NF to improve sports performance. Then, we review the current results concerning its efficiency. Finally, we provide an overview of the literature showing the heterogeneity of the studies published on the subject, focusing mainly on the aspects that could explain the variability of reported data.
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@article {pmid40694675,
year = {2025},
author = {Izac, M and N'Kaoua, B and Pillette, L and Jeunet-Kelway, C},
title = {[Improve athletes' performance with neurofeedback].},
journal = {Biologie aujourd'hui},
volume = {219},
number = {1-2},
pages = {51-58},
doi = {10.1051/jbio/2025001},
pmid = {40694675},
issn = {2105-0686},
mesh = {Humans ; *Neurofeedback/methods/physiology ; *Athletic Performance/physiology/psychology ; *Athletes/psychology ; Electroencephalography ; Cognition/physiology ; },
abstract = {In order to optimise their performance, athletes are looking for innovative, efficient and reliable training approaches. The development of electroencephalography and neurofeedback (NF) offers the opportunity to create innovative cognitive training procedures. Indeed, these technologies allow athletes to benefit from a feedback during mental training sessions and to objectively assess performance and progress. In addition, NF makes it possible to guide the athletes towards optimal cognitive strategies according to their objectives, and has a motivational dimension that pushes them to engage in the sessions. We first introduce the usefulness of NF to improve sports performance. Then, we review the current results concerning its efficiency. Finally, we provide an overview of the literature showing the heterogeneity of the studies published on the subject, focusing mainly on the aspects that could explain the variability of reported data.},
}
MeSH Terms:
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Humans
*Neurofeedback/methods/physiology
*Athletic Performance/physiology/psychology
*Athletes/psychology
Electroencephalography
Cognition/physiology
RevDate: 2025-07-22
Effective cerebellar neuroprosthetic control after stroke.
Cell reports, 44(8):116030 pii:S2211-1247(25)00801-0 [Epub ahead of print].
Brain-machine interfaces (BMIs) offer a viable option for restoring function in patients with motor disabilities post-stroke. Most BMI systems rely on signals from the motor cortex (M1), which is often compromised after stroke. The cerebellum, a subcortical structure involved in motor control, remains an underexplored source for neuroprosthetic control. Using chronic electrophysiological recordings in a rat stroke model, we show that cerebellar neural activity can effectively drive BMI control, performing comparably to M1-driven control. We observed this even in animals with motor impairments post-stroke. Simultaneous M1-cerebellum recordings during cerebellar BMI control revealed that cerebellar "direct" neurons driving the interface were influenced by both local cerebellar and distant M1 neurons. While cerebellar influence remained stable, M1's interaction with cerebellar direct neurons shifted from longer to shorter timescales after stroke. These findings highlight that cerebellar direct neural control is possible in the stroke brain and reveal changes in M1-cerebellar network dynamics post-stroke.
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@article {pmid40694476,
year = {2025},
author = {Rangwani, R and Abbasi, A and Gulati, T},
title = {Effective cerebellar neuroprosthetic control after stroke.},
journal = {Cell reports},
volume = {44},
number = {8},
pages = {116030},
doi = {10.1016/j.celrep.2025.116030},
pmid = {40694476},
issn = {2211-1247},
abstract = {Brain-machine interfaces (BMIs) offer a viable option for restoring function in patients with motor disabilities post-stroke. Most BMI systems rely on signals from the motor cortex (M1), which is often compromised after stroke. The cerebellum, a subcortical structure involved in motor control, remains an underexplored source for neuroprosthetic control. Using chronic electrophysiological recordings in a rat stroke model, we show that cerebellar neural activity can effectively drive BMI control, performing comparably to M1-driven control. We observed this even in animals with motor impairments post-stroke. Simultaneous M1-cerebellum recordings during cerebellar BMI control revealed that cerebellar "direct" neurons driving the interface were influenced by both local cerebellar and distant M1 neurons. While cerebellar influence remained stable, M1's interaction with cerebellar direct neurons shifted from longer to shorter timescales after stroke. These findings highlight that cerebellar direct neural control is possible in the stroke brain and reveal changes in M1-cerebellar network dynamics post-stroke.},
}
RevDate: 2025-07-22
A Novel Hybrid Brain-Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].
Brain-Computer Interface (BCI) that integrate Motor Imagery (MI) with Steady-State Visual Evoked Potentials (SSVEP) or Overt Spatial Attention (OSA) have demonstrated superior performance compared to MI only BCI. Nonetheless, the exploration of BCI that combine MI with visual tasks remains limited, and the synchronization between MI and visual tasks is often weak. To address this gap, our study introduces a novel BCI paradigm that combines MI with two visual tasks: SSVEP and OSA. In this paradigm, dynamic images depicting left and right arm movements flash at distinct frequencies, serving as visual stimuli positioned on both sides of the screen. Four classification methods are used for testing. The MI+SSVEP+OSA paradigm achieves higher average accuracy than the MI, MI+SSVEP, and MI+OSA paradigms. This validates the effectiveness of our novel paradigm and confirms the feasibility of simultaneously integrating MI with two visual stimuli. Moreover, we observe that the integration of SSVEP offers significant improvements, especially for participants who exhibit limited performance in the MI only paradigm. Additionally, our results indicate comparable performance between the MI+SSVEP and MI+OSA paradigms. Overall, this study offers valuable insights that can guide future research in hybrid BCI development, paving the way for more efficient and user-friendly BCI.
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@article {pmid40694466,
year = {2025},
author = {Zhang, C and Li, G and Wu, X and Gao, X},
title = {A Novel Hybrid Brain-Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3591616},
pmid = {40694466},
issn = {1558-0210},
abstract = {Brain-Computer Interface (BCI) that integrate Motor Imagery (MI) with Steady-State Visual Evoked Potentials (SSVEP) or Overt Spatial Attention (OSA) have demonstrated superior performance compared to MI only BCI. Nonetheless, the exploration of BCI that combine MI with visual tasks remains limited, and the synchronization between MI and visual tasks is often weak. To address this gap, our study introduces a novel BCI paradigm that combines MI with two visual tasks: SSVEP and OSA. In this paradigm, dynamic images depicting left and right arm movements flash at distinct frequencies, serving as visual stimuli positioned on both sides of the screen. Four classification methods are used for testing. The MI+SSVEP+OSA paradigm achieves higher average accuracy than the MI, MI+SSVEP, and MI+OSA paradigms. This validates the effectiveness of our novel paradigm and confirms the feasibility of simultaneously integrating MI with two visual stimuli. Moreover, we observe that the integration of SSVEP offers significant improvements, especially for participants who exhibit limited performance in the MI only paradigm. Additionally, our results indicate comparable performance between the MI+SSVEP and MI+OSA paradigms. Overall, this study offers valuable insights that can guide future research in hybrid BCI development, paving the way for more efficient and user-friendly BCI.},
}
RevDate: 2025-07-22
A distributed adaptive network framework for ERP-Based classification of multichannel EEG signals.
Physical and engineering sciences in medicine [Epub ahead of print].
Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes of this network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on different types of data. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) pattern identification, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.
Additional Links: PMID-40694230
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@article {pmid40694230,
year = {2025},
author = {Afkhaminia, F and Shamsollahi, MB and Bahraini, T},
title = {A distributed adaptive network framework for ERP-Based classification of multichannel EEG signals.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40694230},
issn = {2662-4737},
abstract = {Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes of this network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on different types of data. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) pattern identification, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.},
}
RevDate: 2025-07-22
Veteran and Brain-Computer Interfaces: The Duty to Care.
AJOB neuroscience [Epub ahead of print].
Anticipated by science fiction, the enhanced soldier crystallized in the United States at the dawn of the 21st century within the Pentagon's scientific agency, the Defense Advanced Research Projects Agency (DARPA). Fueled by the fear of being overtaken by the enemy, and then by its own technology, this agency's new vision produced a "bifurcation" within anthropotechnics: the modification of humans for war. The soldier is now at the heart of a process of radical innovation, with as yet unknown implications. Emblematic of this enhancement, the use of the brain-computer interfaces (BCIs) will not only expose the soldier to previously unknown psychocognitive and emotional effects, but also offer the enemy potential access to his/her inner self. By giving birth to a new kind of veteran, this hybridization will generate new responsibilities for military commanders and politicians, as well as a new type of care.
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@article {pmid40694026,
year = {2025},
author = {Guérin, V},
title = {Veteran and Brain-Computer Interfaces: The Duty to Care.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/21507740.2025.2530948},
pmid = {40694026},
issn = {2150-7759},
abstract = {Anticipated by science fiction, the enhanced soldier crystallized in the United States at the dawn of the 21st century within the Pentagon's scientific agency, the Defense Advanced Research Projects Agency (DARPA). Fueled by the fear of being overtaken by the enemy, and then by its own technology, this agency's new vision produced a "bifurcation" within anthropotechnics: the modification of humans for war. The soldier is now at the heart of a process of radical innovation, with as yet unknown implications. Emblematic of this enhancement, the use of the brain-computer interfaces (BCIs) will not only expose the soldier to previously unknown psychocognitive and emotional effects, but also offer the enemy potential access to his/her inner self. By giving birth to a new kind of veteran, this hybridization will generate new responsibilities for military commanders and politicians, as well as a new type of care.},
}
RevDate: 2025-07-22
A commentary on "Brain-computer interfaces: the innovative to unlocking neurological conditions".
International journal of surgery (London, England) pii:01279778-990000000-02845 [Epub ahead of print].
Additional Links: PMID-40694018
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@article {pmid40694018,
year = {2025},
author = {Fan, C and Ding, Y and Zhang, H},
title = {A commentary on "Brain-computer interfaces: the innovative to unlocking neurological conditions".},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000003094},
pmid = {40694018},
issn = {1743-9159},
}
RevDate: 2025-07-21
Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.
Schizophrenia (Heidelberg, Germany), 11(1):104.
Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.
Additional Links: PMID-40691442
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@article {pmid40691442,
year = {2025},
author = {Wang, X and Chen, S and Li, J and Gao, Y and Li, S and Li, M and Liu, X and Liu, S and Ming, D},
title = {Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.},
journal = {Schizophrenia (Heidelberg, Germany)},
volume = {11},
number = {1},
pages = {104},
pmid = {40691442},
issn = {2754-6993},
abstract = {Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.},
}
RevDate: 2025-07-21
Enhanced Online Continuous Brain-Control by Deep Learning-based EEG Decoding.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].
OBJECTIVE: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear.
METHODS: We conduct a randomized and cross-session online MI-BCI study with 2D center-out tasks in 15 BCI-naive subjects. A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding.
RESULTS: Through extensive online analysis, the deep learning decoder consistently outperforms the classical counterpart across various performance metrics. In particular, IFNet significantly improves the average online task accuracy by 20% and 27% in two sessions compared with FBCSP, respectively. Moreover, a significant cross-session training effect is observed by the IFNet model (P = 0.017) while not for the controlled method (P = 0.337). Further offline evaluations also demonstrate the superior performance of IFNet over state-of-the-art deep learning models. Moreover, we present unique behavioral and neurophysiological insights underlying online brain-machine interaction.
CONCLUSION: We present one of the first studies about online MI-BCIs using deep learning, achieving substantially enhanced online performance for continuous BCI control.
SIGNIFICANCE: This study suggests the good utility of deep learning in MI-BCIs and has implications for clinical applications such as stroke rehabilitation.
Additional Links: PMID-40690341
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@article {pmid40690341,
year = {2025},
author = {Wang, J and Yao, L and Wang, Y},
title = {Enhanced Online Continuous Brain-Control by Deep Learning-based EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3591254},
pmid = {40690341},
issn = {1558-0210},
abstract = {OBJECTIVE: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear.
METHODS: We conduct a randomized and cross-session online MI-BCI study with 2D center-out tasks in 15 BCI-naive subjects. A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding.
RESULTS: Through extensive online analysis, the deep learning decoder consistently outperforms the classical counterpart across various performance metrics. In particular, IFNet significantly improves the average online task accuracy by 20% and 27% in two sessions compared with FBCSP, respectively. Moreover, a significant cross-session training effect is observed by the IFNet model (P = 0.017) while not for the controlled method (P = 0.337). Further offline evaluations also demonstrate the superior performance of IFNet over state-of-the-art deep learning models. Moreover, we present unique behavioral and neurophysiological insights underlying online brain-machine interaction.
CONCLUSION: We present one of the first studies about online MI-BCIs using deep learning, achieving substantially enhanced online performance for continuous BCI control.
SIGNIFICANCE: This study suggests the good utility of deep learning in MI-BCIs and has implications for clinical applications such as stroke rehabilitation.},
}
RevDate: 2025-07-23
Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.
Frontiers in human neuroscience, 19:1617748.
INTRODUCTION: Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.
METHODS: In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. From two recorded resting states (eyes-open, eyes-closed), absolute and relative alpha and beta power of the frontal, fronto-central and central electrodes were used to predict the accuracy of the different classifiers.
RESULTS: The prediction of classifier accuracies by neurophysiological features revealed negative correlations between the relative alpha band and classifier accuracies and positive correlations between the absolute and relative beta band and classifiers accuracies. Most ipsilateral EEG channels yielded significant correlations with classifier accuracies, especially for the machine learning classifier.
DISCUSSION: This pattern contrasts with previous findings from bilateral MI paradigms, where contralateral alpha and beta activity were more influential. These inverted correlations suggest task-specific neurophysiological mechanisms in unilateral MI, emphasizing the role of ipsilateral inhibition and attentional processes.
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@article {pmid40688356,
year = {2025},
author = {Sonntag, J and Yu, L and Wang, X and Schack, T},
title = {Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1617748},
pmid = {40688356},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.
METHODS: In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. From two recorded resting states (eyes-open, eyes-closed), absolute and relative alpha and beta power of the frontal, fronto-central and central electrodes were used to predict the accuracy of the different classifiers.
RESULTS: The prediction of classifier accuracies by neurophysiological features revealed negative correlations between the relative alpha band and classifier accuracies and positive correlations between the absolute and relative beta band and classifiers accuracies. Most ipsilateral EEG channels yielded significant correlations with classifier accuracies, especially for the machine learning classifier.
DISCUSSION: This pattern contrasts with previous findings from bilateral MI paradigms, where contralateral alpha and beta activity were more influential. These inverted correlations suggest task-specific neurophysiological mechanisms in unilateral MI, emphasizing the role of ipsilateral inhibition and attentional processes.},
}
RevDate: 2025-07-21
Femtosecond Laser-Engineered Multifunctional Bio-Metasurface for the Inhibition of Thrombosis and Bacterial Infections.
ACS applied materials & interfaces [Epub ahead of print].
Surface engineering is an effective strategy for addressing thrombosis and bacterial infection associated with blood-contacting implants (BCIs). However, most functional surfaces rely on a single mechanism and surface engineering poses substantial processing challenges for chemically inert and difficult-to-process materials such as pyrolytic carbon. Herein, a multifunctional bio-metasurface (LDT surface) synergizing liquid-repellent (L), drag-reduction (D), and turbulence-attenuation (T) strategies is proposed. The LDT surface is achieved through the synergistic interplay of surface texture-mediated flow control and interfacial lubrication effects. The textured LDT surface with microgrooves exhibits a hemodynamic modulation capability, exhibiting an effective turbulence-attenuation effect. The slippery coating on the LDT surface exhibits liquid-repellent and drag-reduction effects, regulating bio (blood and bacteria)-material interfacial interactions. The complex, hierarchical micro-groove, micro-hole, and nano-ripples/gaps/protrusions structures on the surface are fabricated on pyrolytic carbon via temporally shaped femtosecond laser texturing, followed by functional coating. The LDT surface exhibits excellent stability under continuous turbulent flow, with no toxic byproducts generated during processing. The computational fluid dynamics simulation results confirm that the streamwise microgrooves on the wall significantly attenuate turbulence. Compared to the pristine sample surface, the experimental results reveal a 98.2% reduction in platelet adhesion on the LDT surface, with a platelet adhesion rate of only 0.22% and no detected activated platelets, while denatured fibrinogen adhesion decreases by 55.3%. Moreover, the antiadhesion capacities of the LDT surface against Staphylococcus aureus and Escherichia coli improve by 99.4% and 98.4%, respectively, relative to the pristine sample surface, without viable residual bacteria or biofilm formation. The study offers a promising strategy to mitigate BCI-associated thrombosis and bacterial infection on BCIs, particularly those made from difficult-to-machine materials.
Additional Links: PMID-40685778
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@article {pmid40685778,
year = {2025},
author = {Niu, X and Jiang, L and Hu, J and Jia, Y and Zhao, S and Ma, Y and Qiu, Z and Lian, Y and Zhu, E and Ni, J},
title = {Femtosecond Laser-Engineered Multifunctional Bio-Metasurface for the Inhibition of Thrombosis and Bacterial Infections.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c05001},
pmid = {40685778},
issn = {1944-8252},
abstract = {Surface engineering is an effective strategy for addressing thrombosis and bacterial infection associated with blood-contacting implants (BCIs). However, most functional surfaces rely on a single mechanism and surface engineering poses substantial processing challenges for chemically inert and difficult-to-process materials such as pyrolytic carbon. Herein, a multifunctional bio-metasurface (LDT surface) synergizing liquid-repellent (L), drag-reduction (D), and turbulence-attenuation (T) strategies is proposed. The LDT surface is achieved through the synergistic interplay of surface texture-mediated flow control and interfacial lubrication effects. The textured LDT surface with microgrooves exhibits a hemodynamic modulation capability, exhibiting an effective turbulence-attenuation effect. The slippery coating on the LDT surface exhibits liquid-repellent and drag-reduction effects, regulating bio (blood and bacteria)-material interfacial interactions. The complex, hierarchical micro-groove, micro-hole, and nano-ripples/gaps/protrusions structures on the surface are fabricated on pyrolytic carbon via temporally shaped femtosecond laser texturing, followed by functional coating. The LDT surface exhibits excellent stability under continuous turbulent flow, with no toxic byproducts generated during processing. The computational fluid dynamics simulation results confirm that the streamwise microgrooves on the wall significantly attenuate turbulence. Compared to the pristine sample surface, the experimental results reveal a 98.2% reduction in platelet adhesion on the LDT surface, with a platelet adhesion rate of only 0.22% and no detected activated platelets, while denatured fibrinogen adhesion decreases by 55.3%. Moreover, the antiadhesion capacities of the LDT surface against Staphylococcus aureus and Escherichia coli improve by 99.4% and 98.4%, respectively, relative to the pristine sample surface, without viable residual bacteria or biofilm formation. The study offers a promising strategy to mitigate BCI-associated thrombosis and bacterial infection on BCIs, particularly those made from difficult-to-machine materials.},
}
RevDate: 2025-07-23
CmpDate: 2025-07-19
Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.
Scientific reports, 15(1):26267.
Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems.
Additional Links: PMID-40683976
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@article {pmid40683976,
year = {2025},
author = {Joshi, A and Matharu, PS and Malviya, L and Kumar, M and Jadhav, A},
title = {Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26267},
pmid = {40683976},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Machine Learning ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; },
abstract = {Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems.},
}
MeSH Terms:
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*Electroencephalography/methods
Humans
*Neural Networks, Computer
Brain-Computer Interfaces
Machine Learning
Wavelet Analysis
Signal Processing, Computer-Assisted
Algorithms
Deep Learning
RevDate: 2025-07-19
Prediction model for detrusor underactivity via noninvasive clinical parameters in men with benign prostatic hyperplasia.
Urology pii:S0090-4295(25)00700-9 [Epub ahead of print].
OBJECTIVE: To develop a clinical prediction model for detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).
METHODS: A retrospective review was conducted on 546 individuals with BPH who had undergone urodynamic testing between January 2012 and May 2022. The bladder contractility index (BCI) was assessed using a pressure-flow study (PFS). Patients were categorized into DU (BCI <100, n=196) and non-DU (BCI ≥100, n=350) groups. Univariate logistic regression was initially performed to identify potential DU-related factors, followed by multivariate analysis to determine independent risk factors.
RESULTS: A predictive model for DU in patients with BPH was developed using the coefficient of these independent risk factors. Among the 546 cases, 196 (35.9%) were diagnosed with DU. Older age, smaller prostate volume, lower urgency symptom score, lower incomplete emptying symptom score, higher straining symptom score, and lower maximum flow rate (Qmax) were identified as independent predictors of DU in patients with BPH. The model demonstrated an area under the curve (AUC) of 0.78 (95% CI, 0.74-0.82), with internal validation yielding 0.75 (95% CI, 0.74-0.75).
CONCLUSIONS: We developed a predictive model that effectively estimates the DU probability in patients with BPH without requiring invasive PFS.
Additional Links: PMID-40683565
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PubMed:
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@article {pmid40683565,
year = {2025},
author = {Wu, Y and Lv, K and Zhao, Y and Yang, G and Hao, X and Zheng, B and Lv, C and An, Z and Zhou, H and Yuan, Q and Song, T},
title = {Prediction model for detrusor underactivity via noninvasive clinical parameters in men with benign prostatic hyperplasia.},
journal = {Urology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.urology.2025.07.021},
pmid = {40683565},
issn = {1527-9995},
abstract = {OBJECTIVE: To develop a clinical prediction model for detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).
METHODS: A retrospective review was conducted on 546 individuals with BPH who had undergone urodynamic testing between January 2012 and May 2022. The bladder contractility index (BCI) was assessed using a pressure-flow study (PFS). Patients were categorized into DU (BCI <100, n=196) and non-DU (BCI ≥100, n=350) groups. Univariate logistic regression was initially performed to identify potential DU-related factors, followed by multivariate analysis to determine independent risk factors.
RESULTS: A predictive model for DU in patients with BPH was developed using the coefficient of these independent risk factors. Among the 546 cases, 196 (35.9%) were diagnosed with DU. Older age, smaller prostate volume, lower urgency symptom score, lower incomplete emptying symptom score, higher straining symptom score, and lower maximum flow rate (Qmax) were identified as independent predictors of DU in patients with BPH. The model demonstrated an area under the curve (AUC) of 0.78 (95% CI, 0.74-0.82), with internal validation yielding 0.75 (95% CI, 0.74-0.75).
CONCLUSIONS: We developed a predictive model that effectively estimates the DU probability in patients with BPH without requiring invasive PFS.},
}
RevDate: 2025-07-19
MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.
Neural networks : the official journal of the International Neural Network Society, 192:107873 pii:S0893-6080(25)00753-1 [Epub ahead of print].
Functional near-infrared spectroscopy (fNIRS) is a crucial brain activity monitoring tool with remarkable potential applications in brain-computer interfaces (BCI), particularly in rehabilitation therapy for disabilities. However, the performance of fNIRS-based BCI systems remains suboptimal, such as motor execution (ME) and motor imagery (MI) decoding. Inspired by the successful application of the PoolFormer framework in visual tasks, we first proposed a novel long-range dilation multilayer perceptron (LongDilMLP) to utilize the hemodynamic characteristics of fNIRS. Furthermore, the LongDilMLP was integrated with the PoolFormer framework, called as MetaNIRS in this study. The proposed framework MetaNIRS was employed for both ME and MI classification tasks, achieving rigorous validation of its effectiveness and practical applicability. To evaluate the performance of MetaNIRS, two publicly available ME datasets (A and C) and one self-collected MI dataset (B) were employed. The experimental results demonstrated that the average accuracy were 76.00 %, 57.45 %, and 84.14 %, with cross-subject accuracy of 77.24 %, 58.55 %, and 85.52 %, respectively. Moreover, sensitivity experiments of model parameters showed the robustness. Ablation experiments highlighted the significance of each MetaNIRS component and the efficacy of LongDilMLP over traditional MLP. Additionally, visualization techniques enhanced the interpretability of MetaNIRS, indicating the main contribution of the first half signals for classification. Using the first half of signals, the average accuracy only reduced 4.30 %, 1.69 %, and 1.11 %, respectively. These findings suggest that the superior performance of MetaNIRS, which provide an efficient general decoding framework for ME and MI.
Additional Links: PMID-40683191
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PubMed:
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@article {pmid40683191,
year = {2025},
author = {Li, Y and Sun, Y and Wan, F and Yuan, Z and Jung, TP and Wang, H},
title = {MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107873},
doi = {10.1016/j.neunet.2025.107873},
pmid = {40683191},
issn = {1879-2782},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a crucial brain activity monitoring tool with remarkable potential applications in brain-computer interfaces (BCI), particularly in rehabilitation therapy for disabilities. However, the performance of fNIRS-based BCI systems remains suboptimal, such as motor execution (ME) and motor imagery (MI) decoding. Inspired by the successful application of the PoolFormer framework in visual tasks, we first proposed a novel long-range dilation multilayer perceptron (LongDilMLP) to utilize the hemodynamic characteristics of fNIRS. Furthermore, the LongDilMLP was integrated with the PoolFormer framework, called as MetaNIRS in this study. The proposed framework MetaNIRS was employed for both ME and MI classification tasks, achieving rigorous validation of its effectiveness and practical applicability. To evaluate the performance of MetaNIRS, two publicly available ME datasets (A and C) and one self-collected MI dataset (B) were employed. The experimental results demonstrated that the average accuracy were 76.00 %, 57.45 %, and 84.14 %, with cross-subject accuracy of 77.24 %, 58.55 %, and 85.52 %, respectively. Moreover, sensitivity experiments of model parameters showed the robustness. Ablation experiments highlighted the significance of each MetaNIRS component and the efficacy of LongDilMLP over traditional MLP. Additionally, visualization techniques enhanced the interpretability of MetaNIRS, indicating the main contribution of the first half signals for classification. Using the first half of signals, the average accuracy only reduced 4.30 %, 1.69 %, and 1.11 %, respectively. These findings suggest that the superior performance of MetaNIRS, which provide an efficient general decoding framework for ME and MI.},
}
RevDate: 2025-07-19
EEG Emotion Copilot: Optimizing lightweight LLMs for emotional EEG interpretation with assisted medical record generation.
Neural networks : the official journal of the International Neural Network Society, 192:107848 pii:S0893-6080(25)00728-2 [Epub ahead of print].
In the fields of affective computing (AC) and brain-computer interface (BCI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including rapid processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0.5B parameters operating in a local setting, which first recognizes emotional states directly from EEG signals, subsequently generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records. Specifically, we demonstrate the critical techniques in the novel data structure of prompt, model pruning and fine-tuning training, and deployment strategies aiming at improving performance and computational efficiency. Extensive experiments show that our optimized lightweight LLM-based copilot achieves an enhanced intuitive interface for participant interaction, superior accuracy of emotion recognition and assisted electronic medical records generation, in comparison to such models with similar scale parameters or large-scale parameters such as 1.5B, 1.8B, 3B and 7B. In summary, through these efforts, the proposed copilot is expected to advance the application of AC in the medical domain, offering innovative solution to mental health monitoring. The codes will be released at https://github.com/NZWANG/EEG_Emotion_Copilot.
Additional Links: PMID-40683189
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PubMed:
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@article {pmid40683189,
year = {2025},
author = {Chen, H and Zeng, W and Chen, C and Cai, L and Wang, F and Shi, Y and Wang, L and Zhang, W and Li, Y and Yan, H and Siok, WT and Wang, N},
title = {EEG Emotion Copilot: Optimizing lightweight LLMs for emotional EEG interpretation with assisted medical record generation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107848},
doi = {10.1016/j.neunet.2025.107848},
pmid = {40683189},
issn = {1879-2782},
abstract = {In the fields of affective computing (AC) and brain-computer interface (BCI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including rapid processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0.5B parameters operating in a local setting, which first recognizes emotional states directly from EEG signals, subsequently generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records. Specifically, we demonstrate the critical techniques in the novel data structure of prompt, model pruning and fine-tuning training, and deployment strategies aiming at improving performance and computational efficiency. Extensive experiments show that our optimized lightweight LLM-based copilot achieves an enhanced intuitive interface for participant interaction, superior accuracy of emotion recognition and assisted electronic medical records generation, in comparison to such models with similar scale parameters or large-scale parameters such as 1.5B, 1.8B, 3B and 7B. In summary, through these efforts, the proposed copilot is expected to advance the application of AC in the medical domain, offering innovative solution to mental health monitoring. The codes will be released at https://github.com/NZWANG/EEG_Emotion_Copilot.},
}
RevDate: 2025-07-21
CmpDate: 2025-07-18
An investigation into the comfort and neural response of textured visual stimuli in pediatric SSVEP-based BCI.
Scientific reports, 15(1):26168.
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their reliability and possible training-free setup. Common SSVEP stimuli are high contrast and solidly colored, potentially causing discomfort and visual fatigue, particularly when high stimulation frequencies are employed. To address this, textured stimuli, which may evoke visual responses in higher processing systems, have been proposed as an alternative to conventional flashing stimuli. We evaluate the effectiveness of textured stimuli for SSVEP-based BCIs by examining both user comfort and neural responses across different EEG channel subsets. Neurotypical participants aged 5-18 (n = 35, 57% female) were exposed to traditional and textured stimuli at three frequencies (9, 14, and 33 Hz) and asked to report perceived comfort. While textured stimuli were consistently rated as more comfortable, especially at lower frequencies, signal-to-noise ratio analysis indicated that they did not enhance neural responses compared to conventional stimuli. Classification accuracy was driven primarily by stimulation frequency rather than stimulus type and there was a sharp decline in accuracy at 33 Hz. These findings suggest that while textured stimuli improve user comfort, their utility in enhancing BCI performance remains unclear, warranting further investigation into stimulus design for SSVEP-based BCIs.
Additional Links: PMID-40681665
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@article {pmid40681665,
year = {2025},
author = {Schrag, E and Comaduran Marquez, D and Kirton, A and Kinney-Lang, E},
title = {An investigation into the comfort and neural response of textured visual stimuli in pediatric SSVEP-based BCI.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26168},
pmid = {40681665},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adolescent ; Child ; *Photic Stimulation/methods ; Electroencephalography/methods ; Child, Preschool ; Signal-To-Noise Ratio ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their reliability and possible training-free setup. Common SSVEP stimuli are high contrast and solidly colored, potentially causing discomfort and visual fatigue, particularly when high stimulation frequencies are employed. To address this, textured stimuli, which may evoke visual responses in higher processing systems, have been proposed as an alternative to conventional flashing stimuli. We evaluate the effectiveness of textured stimuli for SSVEP-based BCIs by examining both user comfort and neural responses across different EEG channel subsets. Neurotypical participants aged 5-18 (n = 35, 57% female) were exposed to traditional and textured stimuli at three frequencies (9, 14, and 33 Hz) and asked to report perceived comfort. While textured stimuli were consistently rated as more comfortable, especially at lower frequencies, signal-to-noise ratio analysis indicated that they did not enhance neural responses compared to conventional stimuli. Classification accuracy was driven primarily by stimulation frequency rather than stimulus type and there was a sharp decline in accuracy at 33 Hz. These findings suggest that while textured stimuli improve user comfort, their utility in enhancing BCI performance remains unclear, warranting further investigation into stimulus design for SSVEP-based BCIs.},
}
MeSH Terms:
show MeSH Terms
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Humans
*Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
Female
Male
Adolescent
Child
*Photic Stimulation/methods
Electroencephalography/methods
Child, Preschool
Signal-To-Noise Ratio
RevDate: 2025-07-18
Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network model.
Journal of neuroscience methods pii:S0165-0270(25)00180-3 [Epub ahead of print].
BACKGROUND: Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.
NEW METHOD: To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).
RESULTS: The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3% and 99.56%, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.
Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.
CONCLUSION: The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.
Additional Links: PMID-40681115
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@article {pmid40681115,
year = {2025},
author = {Deepika, D and Rekha, G},
title = {Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network model.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110536},
doi = {10.1016/j.jneumeth.2025.110536},
pmid = {40681115},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.
NEW METHOD: To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).
RESULTS: The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3% and 99.56%, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.
Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.
CONCLUSION: The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.},
}
RevDate: 2025-07-19
Hybrid BCI for upper limb rehabilitation: integrating MI with peripheral field SSVEP stimulation.
Journal of neuroscience methods, 423:110537 pii:S0165-0270(25)00181-5 [Epub ahead of print].
BACKGROUND: Rehabilitation systems based on brain-computer interfaces (BCIs) hold significant potential for stroke patients. Existing systems, predominantly relying on motor imagery (MI), have room for improvement in both performance and user comfort. This study aims to enhance these aspects by developing a hybrid BCI system integrating MI with steady-state visual evoked potentials (SSVEPs) elicited by peripheral visual field stimulation.
NEW METHODS: The system is coupled with a soft robotic hand for feedback, forming a closed-loop framework. The design incorporates concentric rings with 7° and 10° eccentricities as peripheral stimuli, flashing at frequencies of 34 Hz and 35 Hz for left and right sides, respectively, to evoke SSVEPs. A central video (304 ×304 pixels) of left-hand/right-hand grasping motions guides subjects in performing synchronized MI tasks simply by focusing on it, which could also complete the SSVEP task.
RESULTS: The offline results of 11 subjects showed that the classification result of MI was 70.65 ± 3.38 %, and the SSVEP result was 96.04 ± 3.33 %, and the fusion result reached 96.23 ± 3.21 %, which confirmed the validity of the fusion method. The online experiment of 11 subjects achieved a result of 97.12 ± 2.09 %, validating the feasibility of the system.
The proposed system improves the comfort level while ensuring the performance of the system as compared to the existing systems.
CONCLUSION: The feasibility of the proposed system was verified by offline and online experiments to advance the clinical applications.
Additional Links: PMID-40681114
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PubMed:
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@article {pmid40681114,
year = {2025},
author = {Zhang, R and Li, Z and Pan, X and Cui, H and Chen, X},
title = {Hybrid BCI for upper limb rehabilitation: integrating MI with peripheral field SSVEP stimulation.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110537},
doi = {10.1016/j.jneumeth.2025.110537},
pmid = {40681114},
issn = {1872-678X},
abstract = {BACKGROUND: Rehabilitation systems based on brain-computer interfaces (BCIs) hold significant potential for stroke patients. Existing systems, predominantly relying on motor imagery (MI), have room for improvement in both performance and user comfort. This study aims to enhance these aspects by developing a hybrid BCI system integrating MI with steady-state visual evoked potentials (SSVEPs) elicited by peripheral visual field stimulation.
NEW METHODS: The system is coupled with a soft robotic hand for feedback, forming a closed-loop framework. The design incorporates concentric rings with 7° and 10° eccentricities as peripheral stimuli, flashing at frequencies of 34 Hz and 35 Hz for left and right sides, respectively, to evoke SSVEPs. A central video (304 ×304 pixels) of left-hand/right-hand grasping motions guides subjects in performing synchronized MI tasks simply by focusing on it, which could also complete the SSVEP task.
RESULTS: The offline results of 11 subjects showed that the classification result of MI was 70.65 ± 3.38 %, and the SSVEP result was 96.04 ± 3.33 %, and the fusion result reached 96.23 ± 3.21 %, which confirmed the validity of the fusion method. The online experiment of 11 subjects achieved a result of 97.12 ± 2.09 %, validating the feasibility of the system.
The proposed system improves the comfort level while ensuring the performance of the system as compared to the existing systems.
CONCLUSION: The feasibility of the proposed system was verified by offline and online experiments to advance the clinical applications.},
}
RevDate: 2025-07-18
Post-training quantization for efficient ANN-SNN conversion.
Neural networks : the official journal of the International Neural Network Society, 191:107832 pii:S0893-6080(25)00712-9 [Epub ahead of print].
Spiking Neural Networks (SNNs), inspired by the behavior of biological neurons, offer a promising direction for next-generation neural computing. Two primary methodologies have emerged for training deep SNNs: Direct Training, which optimizes SNNs using surrogate gradients, and ANN-to-SNN Conversion, which derives SNNs from Artificial Neural Networks (ANNs). In this work, we focus on the latter and investigate the conversion error that arises during the transformation. We provide a theoretical analysis showing that channel-wise thresholds are more effective than traditional layer-wise thresholds in mitigating this error. To achieve this efficiently, we leverage post-training quantization (PTQ), which enables calibration using only a small dataset without requiring retraining. Compared to conventional direct training and ANN-to-SNN conversion methods, our approach significantly reduces training time while improving accuracy on both static image and neuromorphic datasets.
Additional Links: PMID-40680338
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@article {pmid40680338,
year = {2025},
author = {Sun, R and Ma, D and Pan, G},
title = {Post-training quantization for efficient ANN-SNN conversion.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107832},
doi = {10.1016/j.neunet.2025.107832},
pmid = {40680338},
issn = {1879-2782},
abstract = {Spiking Neural Networks (SNNs), inspired by the behavior of biological neurons, offer a promising direction for next-generation neural computing. Two primary methodologies have emerged for training deep SNNs: Direct Training, which optimizes SNNs using surrogate gradients, and ANN-to-SNN Conversion, which derives SNNs from Artificial Neural Networks (ANNs). In this work, we focus on the latter and investigate the conversion error that arises during the transformation. We provide a theoretical analysis showing that channel-wise thresholds are more effective than traditional layer-wise thresholds in mitigating this error. To achieve this efficiently, we leverage post-training quantization (PTQ), which enables calibration using only a small dataset without requiring retraining. Compared to conventional direct training and ANN-to-SNN conversion methods, our approach significantly reduces training time while improving accuracy on both static image and neuromorphic datasets.},
}
RevDate: 2025-07-18
Performance enhancement of steady-state visual evoked field-based brain-computer interfaces incorporating MEG source imaging.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].
Recent advancements in helmet-type magnetoencephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain-computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages. To overcome this limitation, this study employed MEG source imaging to utilize information from whole-head MEG recordings fully and developed a novel weighting method called the averaged source location-based weighting (ASLW). ASLW leverages the averaged source locations of SSVEF signals to enhance BCI performance. Experimental results with 20 participants demonstrated that integrating ASLW with the FBMSI algorithm (ASLW-FBMSI) significantly improved both the classification accuracy and ITR across all tested window sizes. Notably, the largest performance gains included a 13.89% accuracy improvement at a 3-s window size and a 13.12 bits/min increase in ITR at a 2.5-s window size. Additionally, the ASLW-FBMSI algorithm exhibited a short processing delay of 0.107 s at a 4-s data length and was successfully validated in online BCI experiments with 20 participants. Although tested in SQUID-MEG in this study, our findings demonstrate the effectiveness of ASLW in significantly enhancing the overall performance of SSVEF-based BCIs.
Additional Links: PMID-40679899
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@article {pmid40679899,
year = {2025},
author = {Kim, YS and Han, H and Kim, CU and Choi, SI and Kim, MY and Im, CH},
title = {Performance enhancement of steady-state visual evoked field-based brain-computer interfaces incorporating MEG source imaging.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3590576},
pmid = {40679899},
issn = {1558-0210},
abstract = {Recent advancements in helmet-type magnetoencephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain-computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages. To overcome this limitation, this study employed MEG source imaging to utilize information from whole-head MEG recordings fully and developed a novel weighting method called the averaged source location-based weighting (ASLW). ASLW leverages the averaged source locations of SSVEF signals to enhance BCI performance. Experimental results with 20 participants demonstrated that integrating ASLW with the FBMSI algorithm (ASLW-FBMSI) significantly improved both the classification accuracy and ITR across all tested window sizes. Notably, the largest performance gains included a 13.89% accuracy improvement at a 3-s window size and a 13.12 bits/min increase in ITR at a 2.5-s window size. Additionally, the ASLW-FBMSI algorithm exhibited a short processing delay of 0.107 s at a 4-s data length and was successfully validated in online BCI experiments with 20 participants. Although tested in SQUID-MEG in this study, our findings demonstrate the effectiveness of ASLW in significantly enhancing the overall performance of SSVEF-based BCIs.},
}
RevDate: 2025-07-18
CmpDate: 2025-07-18
Tirzepatide Induces Ferroptosis in Glioblastoma Cell Lines via the SOX2/SLC7A11 Axis: A Potential Therapeutic Strategy for Glioma Treatment.
Journal of biochemical and molecular toxicology, 39(8):e70392.
Tirzepatide, a dual agonist for glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors used in type 2 diabetes and obesity management, was investigated for its effects on glioma cells, focusing on its potential to induce ferroptosis. Tirzepatide treatment significantly inhibited glioma cell proliferation and migration, as demonstrated by the CCK-8 and Transwell migration assays. Tirzepatide also induced lipid peroxidation, evidenced by increased ROS levels, elevated MDA production, and reduced SOD activity, while the GSH/GSSG ratio was decreased, reflecting oxidative stress. Ferroptosis was further confirmed by increased Fe[2+] concentrations and alterations in iron metabolism-related genes (Ferritin and TFR1) and lipid metabolism-related genes (ACSL4 and GPX4). Tirzepatide also inhibited the SOX2/SLC7A11 axis, which plays a critical role in resisting ferroptosis. Fer-1, a ferroptosis inhibitor, or SOX2 overexpression, markedly reduced Tirzepatide's effects on proliferation, migration, lipid peroxidation, and ferroptosis, highlighting the critical role of the SOX2/SLC7A11 axis in mediating these effects. These findings indicate that Tirzepatide inhibits glioma cell growth by inducing ferroptosis, presenting a potential therapeutic approach for glioma.
Additional Links: PMID-40678831
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@article {pmid40678831,
year = {2025},
author = {Wang, J and Chen, H and Wang, X},
title = {Tirzepatide Induces Ferroptosis in Glioblastoma Cell Lines via the SOX2/SLC7A11 Axis: A Potential Therapeutic Strategy for Glioma Treatment.},
journal = {Journal of biochemical and molecular toxicology},
volume = {39},
number = {8},
pages = {e70392},
doi = {10.1002/jbt.70392},
pmid = {40678831},
issn = {1099-0461},
support = {//This study was supported by the Fifth Affiliated Hospital of Zhengzhou University./ ; },
mesh = {*Ferroptosis/drug effects ; Humans ; Cell Line, Tumor ; *Amino Acid Transport System y+/metabolism ; *SOXB1 Transcription Factors/metabolism ; *Glioblastoma/metabolism/drug therapy/pathology ; Cell Proliferation/drug effects ; Lipid Peroxidation/drug effects ; *Neoplasm Proteins/metabolism ; *Brain Neoplasms/metabolism/drug therapy/pathology ; Cell Movement/drug effects ; Tirzepatide ; },
abstract = {Tirzepatide, a dual agonist for glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors used in type 2 diabetes and obesity management, was investigated for its effects on glioma cells, focusing on its potential to induce ferroptosis. Tirzepatide treatment significantly inhibited glioma cell proliferation and migration, as demonstrated by the CCK-8 and Transwell migration assays. Tirzepatide also induced lipid peroxidation, evidenced by increased ROS levels, elevated MDA production, and reduced SOD activity, while the GSH/GSSG ratio was decreased, reflecting oxidative stress. Ferroptosis was further confirmed by increased Fe[2+] concentrations and alterations in iron metabolism-related genes (Ferritin and TFR1) and lipid metabolism-related genes (ACSL4 and GPX4). Tirzepatide also inhibited the SOX2/SLC7A11 axis, which plays a critical role in resisting ferroptosis. Fer-1, a ferroptosis inhibitor, or SOX2 overexpression, markedly reduced Tirzepatide's effects on proliferation, migration, lipid peroxidation, and ferroptosis, highlighting the critical role of the SOX2/SLC7A11 axis in mediating these effects. These findings indicate that Tirzepatide inhibits glioma cell growth by inducing ferroptosis, presenting a potential therapeutic approach for glioma.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
*Ferroptosis/drug effects
Humans
Cell Line, Tumor
*Amino Acid Transport System y+/metabolism
*SOXB1 Transcription Factors/metabolism
*Glioblastoma/metabolism/drug therapy/pathology
Cell Proliferation/drug effects
Lipid Peroxidation/drug effects
*Neoplasm Proteins/metabolism
*Brain Neoplasms/metabolism/drug therapy/pathology
Cell Movement/drug effects
Tirzepatide
RevDate: 2025-07-18
A Phase I Dose-Escalation Clinical Trial of Bronchoscopic Cryoimmunotherapy in Advanced-Stage NSCLC.
JTO clinical and research reports, 6(8):100849.
INTRODUCTION: Outcomes for NSCLC remain suboptimal. Recent data suggest that cryoablation can generate antitumor immune effects. In this first-in-human phase I clinical trial, we investigated the safety and feasibility of bronchoscopic cryoimmunotherapy (BCI) delivered during standard-of-care bronchoscopy and explored associated systemic immune responses.
METHODS: Subjects with known or suspected advanced-stage NSCLC were recruited. BCI was delivered in dose-escalated freeze-thaw cycles to determine maximum dose tolerance. Feasibility assessment was determined with a pre-set goal of achieving successful BCI in more than or equal to 80% of subjects. Safety was assessed by review of BCI-related complications, including grades 2 to 3 bleeding, pneumothorax requiring intervention, and National Cancer Institute Common Terminology Criteria for Adverse Events grade 3 to 5 adverse events. Pre- and post-BCI blood samples were collected to explore changes in the systemic immune profile.
RESULTS: Subjects with predominantly clinical TNM stage 3 or 4 adenocarcinoma or squamous cell carcinoma were enrolled. We reached the maximum dose of 30 seconds with 100% feasibility and no BCI-related adverse events. In peripheral blood analysis, we observed a significant decrease in derived neutrophil-to-lymphocyte ratio in the high-dose BCI group in comparison to the low-dose BCI cohort. We also observed increases in inflammatory cytokines-GM-CSF, IFN-γ, IL-1β, IL-17A, and IL-2-and effector memory T cells post-BCI.
CONCLUSION: BCI is safe and feasible. In addition, we provide preliminary evidence that at higher dose levels there is a systemic immune response consistent with a cytotoxic profile. Further immune analyses will determine the potential of BCI as an adjunctive therapy in combination with immune checkpoint inhibition in NSCLC treatment.
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@article {pmid40678346,
year = {2025},
author = {Tsay, JJ and Velez, A and Collazo, D and Laniado, I and Bessich, J and Murthy, V and DeMaio, A and Rafeq, S and Kwok, B and Darawshy, F and Pillai, R and Wong, K and Li, Y and Schluger, R and Lukovnikova, A and Roldan, S and Blaisdell, M and Paz, F and Krolikowski, K and Gershner, K and Liu, Y and Gong, J and Borghi, S and Zhou, F and Tsirigos, A and Pass, H and Segal, LN and Sterman, DH},
title = {A Phase I Dose-Escalation Clinical Trial of Bronchoscopic Cryoimmunotherapy in Advanced-Stage NSCLC.},
journal = {JTO clinical and research reports},
volume = {6},
number = {8},
pages = {100849},
pmid = {40678346},
issn = {2666-3643},
abstract = {INTRODUCTION: Outcomes for NSCLC remain suboptimal. Recent data suggest that cryoablation can generate antitumor immune effects. In this first-in-human phase I clinical trial, we investigated the safety and feasibility of bronchoscopic cryoimmunotherapy (BCI) delivered during standard-of-care bronchoscopy and explored associated systemic immune responses.
METHODS: Subjects with known or suspected advanced-stage NSCLC were recruited. BCI was delivered in dose-escalated freeze-thaw cycles to determine maximum dose tolerance. Feasibility assessment was determined with a pre-set goal of achieving successful BCI in more than or equal to 80% of subjects. Safety was assessed by review of BCI-related complications, including grades 2 to 3 bleeding, pneumothorax requiring intervention, and National Cancer Institute Common Terminology Criteria for Adverse Events grade 3 to 5 adverse events. Pre- and post-BCI blood samples were collected to explore changes in the systemic immune profile.
RESULTS: Subjects with predominantly clinical TNM stage 3 or 4 adenocarcinoma or squamous cell carcinoma were enrolled. We reached the maximum dose of 30 seconds with 100% feasibility and no BCI-related adverse events. In peripheral blood analysis, we observed a significant decrease in derived neutrophil-to-lymphocyte ratio in the high-dose BCI group in comparison to the low-dose BCI cohort. We also observed increases in inflammatory cytokines-GM-CSF, IFN-γ, IL-1β, IL-17A, and IL-2-and effector memory T cells post-BCI.
CONCLUSION: BCI is safe and feasible. In addition, we provide preliminary evidence that at higher dose levels there is a systemic immune response consistent with a cytotoxic profile. Further immune analyses will determine the potential of BCI as an adjunctive therapy in combination with immune checkpoint inhibition in NSCLC treatment.},
}
RevDate: 2025-07-18
Renal Impairment in Wilson's Disease.
Kidney international reports, 10(7):2453-2456.
Additional Links: PMID-40677333
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@article {pmid40677333,
year = {2025},
author = {Zheng, ZW and Xu, MH and Fan, LN and Wang, RM and Xu, WQ and Yang, GM and Guo, LY and Liu, C and Dong, Y and Wu, ZY},
title = {Renal Impairment in Wilson's Disease.},
journal = {Kidney international reports},
volume = {10},
number = {7},
pages = {2453-2456},
pmid = {40677333},
issn = {2468-0249},
}
RevDate: 2025-07-18
CmpDate: 2025-07-17
Unraveling the emergent chorus of the mind: Machine learning reveals how a hidden neural code orchestrates diverse emotion states.
Science (New York, N.Y.), 389(6757):245.
Machine learning reveals how a hidden neural code orchestrates diverse emotion states.
Additional Links: PMID-40674496
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@article {pmid40674496,
year = {2025},
author = {Nair, A},
title = {Unraveling the emergent chorus of the mind: Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
journal = {Science (New York, N.Y.)},
volume = {389},
number = {6757},
pages = {245},
doi = {10.1126/science.adx7811},
pmid = {40674496},
issn = {1095-9203},
mesh = {*Emotions/physiology ; *Machine Learning ; Humans ; *Brain/physiology ; *Neurons/physiology ; },
abstract = {Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
}
MeSH Terms:
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*Emotions/physiology
*Machine Learning
Humans
*Brain/physiology
*Neurons/physiology
RevDate: 2025-07-17
How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs.
Frontiers in neuroergonomics, 6:1578586.
Speech imagery brain-computer interfaces (SI-BCIs) aim to decode imagined speech from brain activity and have been successfully established using non-invasive brain measures such as electroencephalography (EEG). However, current EEG-based SI-BCIs predominantly rely on high-resolution systems with 64 or more electrodes, making them cumbersome to set up and impractical for real-world use. In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. Our results showed that, across all datasets, the original 64 channels could be reduced by 50% without a significant performance loss in classification accuracy. Furthermore, the relevant areas were not limited to the left hemisphere, widely known to be responsible for speech production and comprehension, but were distributed across the cortex. However, we could not identify a consistent set of optimal electrode positions across datasets, indicating that electrode configurations are highly subject-specific and should be individually tailored. Nonetheless, our findings support the move away from extensive and costly high-resolution systems toward more compact, user-specific setups, facilitating the transition of SI-BCIs from laboratory settings to real-world applications.
Additional Links: PMID-40672704
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Citation:
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@article {pmid40672704,
year = {2025},
author = {Rekrut, M and Ihl, J and Jungbluth, T and Krüger, A},
title = {How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1578586},
pmid = {40672704},
issn = {2673-6195},
abstract = {Speech imagery brain-computer interfaces (SI-BCIs) aim to decode imagined speech from brain activity and have been successfully established using non-invasive brain measures such as electroencephalography (EEG). However, current EEG-based SI-BCIs predominantly rely on high-resolution systems with 64 or more electrodes, making them cumbersome to set up and impractical for real-world use. In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. Our results showed that, across all datasets, the original 64 channels could be reduced by 50% without a significant performance loss in classification accuracy. Furthermore, the relevant areas were not limited to the left hemisphere, widely known to be responsible for speech production and comprehension, but were distributed across the cortex. However, we could not identify a consistent set of optimal electrode positions across datasets, indicating that electrode configurations are highly subject-specific and should be individually tailored. Nonetheless, our findings support the move away from extensive and costly high-resolution systems toward more compact, user-specific setups, facilitating the transition of SI-BCIs from laboratory settings to real-world applications.},
}
RevDate: 2025-07-17
Reimagining Neuropsychiatric and Neurological Disorders through the Lens of Brain Network Dynamics: Psychedelics as Catalysts for System-Level Plasticity.
ACS pharmacology & translational science, 8(7):2308-2311.
Neuropsychiatric disorders reflect disruptions in brain network dynamics along an "order-complexity-chaos" continuum. Psychedelics may therapeutically increase neural entropy, disrupt maladaptive patterns, and promote network reorganization. This system-level framework emphasizes dynamic connectome remodeling over static molecular correction, offering a novel strategy for treating psychiatric and neurological conditions through controlled neural destabilization and reconnection.
Additional Links: PMID-40672675
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@article {pmid40672675,
year = {2025},
author = {Zhang, C and Wang, Y and Wang, X},
title = {Reimagining Neuropsychiatric and Neurological Disorders through the Lens of Brain Network Dynamics: Psychedelics as Catalysts for System-Level Plasticity.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {7},
pages = {2308-2311},
pmid = {40672675},
issn = {2575-9108},
abstract = {Neuropsychiatric disorders reflect disruptions in brain network dynamics along an "order-complexity-chaos" continuum. Psychedelics may therapeutically increase neural entropy, disrupt maladaptive patterns, and promote network reorganization. This system-level framework emphasizes dynamic connectome remodeling over static molecular correction, offering a novel strategy for treating psychiatric and neurological conditions through controlled neural destabilization and reconnection.},
}
RevDate: 2025-07-17
High-gamma electrocorticography activity represents perceived vibration intensity in human somatosensory cortex.
medRxiv : the preprint server for health sciences pii:2025.07.09.25331186.
Haptic feedback can play a useful role in rehabilitation and brain-computer interface applications by providing users with information about their system or performance. One challenge delivering tactile stimulation is not knowing how the haptic sensation is actually perceived, irrespective of the stimulation amplitude, during real-world use and beyond controlled psychophysical experiments. In a participant with chronically implanted electrocorticography arrays, we observed that perceived intensity of haptic vibration on the fingertips was represented in the high-gamma (HG) frequency band (70-170 Hz) in the somatosensory cortex. The five fingers of the participant's right hand were represented by distinct channels in the implanted array and modulated by the vibration amplitude at the fingertips. Although it reliably varied with the vibration amplitude, we found that HG activity had a stronger relationship with the actual perceived intensity of haptic stimulation (r s = 0.45 , p < 10 [-6]). These results demonstrate that neural signals, specifically HG activity, in the somatosensory cortex can represent qualities of perceived haptic intensity regardless of the stimulation amplitude, which could enable a new way to passively quantify or ensure effective haptic feedback to a user.
Additional Links: PMID-40672502
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@article {pmid40672502,
year = {2025},
author = {Chaichanasittikarn, O and Diaz, L and Thomas, N and Candrea, D and Luo, S and Nathan, K and Tenore, FV and Fifer, MS and Crone, NE and Christie, B and Osborn, LE},
title = {High-gamma electrocorticography activity represents perceived vibration intensity in human somatosensory cortex.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.09.25331186},
pmid = {40672502},
abstract = {Haptic feedback can play a useful role in rehabilitation and brain-computer interface applications by providing users with information about their system or performance. One challenge delivering tactile stimulation is not knowing how the haptic sensation is actually perceived, irrespective of the stimulation amplitude, during real-world use and beyond controlled psychophysical experiments. In a participant with chronically implanted electrocorticography arrays, we observed that perceived intensity of haptic vibration on the fingertips was represented in the high-gamma (HG) frequency band (70-170 Hz) in the somatosensory cortex. The five fingers of the participant's right hand were represented by distinct channels in the implanted array and modulated by the vibration amplitude at the fingertips. Although it reliably varied with the vibration amplitude, we found that HG activity had a stronger relationship with the actual perceived intensity of haptic stimulation (r s = 0.45 , p < 10 [-6]). These results demonstrate that neural signals, specifically HG activity, in the somatosensory cortex can represent qualities of perceived haptic intensity regardless of the stimulation amplitude, which could enable a new way to passively quantify or ensure effective haptic feedback to a user.},
}
RevDate: 2025-07-17
Active Dissociation of Intracortical Spiking and High Gamma Activity.
bioRxiv : the preprint server for biology pii:2025.07.10.663559.
Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.
Additional Links: PMID-40672280
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@article {pmid40672280,
year = {2025},
author = {Lei, T and Scheid, MR and Glaser, JI and Slutzky, MW},
title = {Active Dissociation of Intracortical Spiking and High Gamma Activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.10.663559},
pmid = {40672280},
issn = {2692-8205},
abstract = {Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.},
}
RevDate: 2025-07-17
CmpDate: 2025-07-16
Making Heads and Tails of the Coming Era of Neural Devices, Could Moore's Law Address the Declining Mental Health Trend.
IEEE pulse, 16(3):50-55.
Despite major advances in medicine and technology, mental health outcomes have declined globally over the past several decades. Fortunately we are in the early phases of exponential growth neurotech similar to Moore's Law. These emerging neural devices may provide a solution to the growing mental health crisis. Clinical data shows promising outcomes from technologies such as transcranial magnetic stimulation (TMS) leading to exponential improvement in performance improvements and cost reductions. As a result, neurotechnology could follow a similar path to personal computing going from a handful of niche markets to ubiquity over the next decade. Indeed, next generation therapeutic brain-computer interfaces (BCIs)-particularly minimally invasive implants-could become mass-market solutions for regulating mental states. The future may be one where neural devices help individuals thrive in an increasingly complex world, not by augmenting human intelligence but by enhancing emotional well-being and preserving the most precious aspects of our humanity.
Additional Links: PMID-40668700
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@article {pmid40668700,
year = {2025},
author = {Robinson, JT},
title = {Making Heads and Tails of the Coming Era of Neural Devices, Could Moore's Law Address the Declining Mental Health Trend.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {50-55},
doi = {10.1109/MPULS.2025.3572593},
pmid = {40668700},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Mental Health ; *Transcranial Magnetic Stimulation ; },
abstract = {Despite major advances in medicine and technology, mental health outcomes have declined globally over the past several decades. Fortunately we are in the early phases of exponential growth neurotech similar to Moore's Law. These emerging neural devices may provide a solution to the growing mental health crisis. Clinical data shows promising outcomes from technologies such as transcranial magnetic stimulation (TMS) leading to exponential improvement in performance improvements and cost reductions. As a result, neurotechnology could follow a similar path to personal computing going from a handful of niche markets to ubiquity over the next decade. Indeed, next generation therapeutic brain-computer interfaces (BCIs)-particularly minimally invasive implants-could become mass-market solutions for regulating mental states. The future may be one where neural devices help individuals thrive in an increasingly complex world, not by augmenting human intelligence but by enhancing emotional well-being and preserving the most precious aspects of our humanity.},
}
MeSH Terms:
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Humans
*Brain-Computer Interfaces/trends
*Mental Health
*Transcranial Magnetic Stimulation
RevDate: 2025-07-17
CmpDate: 2025-07-16
Silicon Synapses: The Bold Frontier of Brain-Computer Integration.
IEEE pulse, 16(3):5-9.
The allure of Neuralink is attracting investors to funnel money into the development of brain-computer interface (BCI) technology, primarily aimed at treating spinal cord injury (SCI) patients. But what is the payoff? Jim Banks examines the inspired innovation in BCI that is reestablishing connections for patients with the world.
Additional Links: PMID-40668693
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@article {pmid40668693,
year = {2025},
author = {Banks, J},
title = {Silicon Synapses: The Bold Frontier of Brain-Computer Integration.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {5-9},
doi = {10.1109/MPULS.2025.3572569},
pmid = {40668693},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces ; Humans ; *Silicon ; *Synapses/physiology ; Spinal Cord Injuries ; *Brain/physiology ; },
abstract = {The allure of Neuralink is attracting investors to funnel money into the development of brain-computer interface (BCI) technology, primarily aimed at treating spinal cord injury (SCI) patients. But what is the payoff? Jim Banks examines the inspired innovation in BCI that is reestablishing connections for patients with the world.},
}
MeSH Terms:
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hide MeSH Terms
*Brain-Computer Interfaces
Humans
*Silicon
*Synapses/physiology
Spinal Cord Injuries
*Brain/physiology
RevDate: 2025-07-17
CmpDate: 2025-07-16
EEG-Based Brain-Computer Interfaces: Pioneering Frontier Research in the 21st Century.
IEEE pulse, 16(3):36-39.
Electroencephalography (EEG)-based brain-computer interface (BCI) systems are inevitably needed to set up non-invasive therapies in neurorehabilitation. Along with the artificial intelligence (AI) techniques trending, constructing EEG-based brain computer interfaces is still in demand with high classification accuracy for advancing the state-of-the-art BCIs. From the perspective of pioneering frontier research, this article highlights the 21st-century's EEG-based BCI systems, their challenges, and its future direction for neuroscientists and clinical applications.
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@article {pmid40668691,
year = {2025},
author = {Goktas, P and Tun, NN},
title = {EEG-Based Brain-Computer Interfaces: Pioneering Frontier Research in the 21st Century.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {36-39},
doi = {10.1109/MPULS.2025.3572556},
pmid = {40668691},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Electroencephalography/methods/trends ; Artificial Intelligence ; *Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) systems are inevitably needed to set up non-invasive therapies in neurorehabilitation. Along with the artificial intelligence (AI) techniques trending, constructing EEG-based brain computer interfaces is still in demand with high classification accuracy for advancing the state-of-the-art BCIs. From the perspective of pioneering frontier research, this article highlights the 21st-century's EEG-based BCI systems, their challenges, and its future direction for neuroscientists and clinical applications.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
*Brain-Computer Interfaces/trends
Humans
*Electroencephalography/methods/trends
Artificial Intelligence
*Signal Processing, Computer-Assisted
Brain/physiology
RevDate: 2025-07-17
CmpDate: 2025-07-16
The Potential of Brain-Computer Interface Technologies in Low- and Middle-Income Countries Global Health Perspective.
IEEE pulse, 16(3):40-42.
Historically, brain-computer interface (BCI) technologies have almost exclusively been available in high-income countries. What would it take for them to become more available and accessible in low- and middle-income countries, and in complex settings?
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@article {pmid40668688,
year = {2025},
author = {Zaman, MH},
title = {The Potential of Brain-Computer Interface Technologies in Low- and Middle-Income Countries Global Health Perspective.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {40-42},
doi = {10.1109/MPULS.2025.3572574},
pmid = {40668688},
issn = {2154-2317},
mesh = {*Brain-Computer Interfaces/economics ; Humans ; *Developing Countries ; *Global Health ; },
abstract = {Historically, brain-computer interface (BCI) technologies have almost exclusively been available in high-income countries. What would it take for them to become more available and accessible in low- and middle-income countries, and in complex settings?},
}
MeSH Terms:
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*Brain-Computer Interfaces/economics
Humans
*Developing Countries
*Global Health
RevDate: 2025-07-17
CmpDate: 2025-07-16
From Headsets to Healing: The Rise of Wearable Brain Tech and Its Impact on Mental Illness and Cognitive Health.
IEEE pulse, 16(3):25-29.
The rapidly evolving field of noninvasive brain-machine interfaces (BMIs) is transforming wearable technology from science fiction into a powerful tool for health care, offering a surgery-free and drug-free alternative to traditional treatments. Such devices are currently being used to target conditions such as depression, anxiety, PTSD, insomnia and more through targeted neurostimulation techniques.
Additional Links: PMID-40668686
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@article {pmid40668686,
year = {2025},
author = {Grifantini, K},
title = {From Headsets to Healing: The Rise of Wearable Brain Tech and Its Impact on Mental Illness and Cognitive Health.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {25-29},
doi = {10.1109/MPULS.2025.3572580},
pmid = {40668686},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces ; *Cognition/physiology ; *Mental Disorders/therapy ; Mental Health ; *Wearable Electronic Devices ; },
abstract = {The rapidly evolving field of noninvasive brain-machine interfaces (BMIs) is transforming wearable technology from science fiction into a powerful tool for health care, offering a surgery-free and drug-free alternative to traditional treatments. Such devices are currently being used to target conditions such as depression, anxiety, PTSD, insomnia and more through targeted neurostimulation techniques.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Brain-Computer Interfaces
*Cognition/physiology
*Mental Disorders/therapy
Mental Health
*Wearable Electronic Devices
RevDate: 2025-07-17
1-Year Results From a Multicenter Trial of a Polymer Surgical Mitral Valve: Insights Into New Technology.
Journal of the American College of Cardiology pii:S0735-1097(25)06842-1 [Epub ahead of print].
BACKGROUND: Polymer leaflet material may extend the durability of surgical mitral valve replacement (SMVR) to provide stable long-term hemodynamics. The India Mitral Surgical Trial sought to evaluate the safety and performance of a novel polymer leaflet material as part of a surgical mitral valve (MV) prosthesis.
OBJECTIVES: In this study, the authors sought to report 1-year outcomes in patients undergoing SMVR for MV disease using the Tria Mitral Valve (Foldax).
METHODS: Adult patients requiring MV replacement were enrolled in a prospective single-arm multicenter trial at 8 clinical sites in India from April to November 2023. An independent physician screening committee reviewed each patient for study eligibility before enrollment. Safety events were adjudicated per standard Valve Academic Research Consortium 3 criteria guidelines, and valve performance was assessed by means of echocardiographic and computed tomographic imaging at 30 days and 1 year. Patients were maintained on a vitamin K antagonist (target international normalized ratio: 2.5).
RESULTS: Sixty-seven patients, of whom 64% were female (48% of childbearing age), with a mean age of 42 years (range: 19-67 years), mean body mass index of 22.7 kg/m[2], and body surface area of 1.6 cm[2] were treated with SMVR with 100% technical success. Most patients (54%) were NYHA functional class III or IV at baseline. The mean Society of Thoracic Surgeons score was 1.4%. The etiology of MV disease was stenosis in 27%, regurgitation in 30%, and mixed in 43% of patients, primarily secondary to rheumatic heart disease. The 1-year rates for all-cause mortality, thromboembolic events, stroke, structural valve deterioration, and valve reintervention were 9.1%, 7.5%, 4.9%, 0%, and 0%, respectively. No death was valve related. One-year effective orifice area and mean inflow gradient were 1.4 ± 0.4 cm[2] and 4.6 ± 1.7 mm Hg, respectively. There were 2 thrombotic events and 3 ischemic strokes, all in patients with subtherapeutic international normalized ratio.
CONCLUSIONS: The polymer surgical MV demonstrated an acceptable safety profile and maintained stable hemodynamic performance through 1 year in patients undergoing MV replacement. Further study of this promising polymer leaflet technology is ongoing. (Clinical Investigation for the Foldax Tria Mitral Valve-India; NCT06191718).
Additional Links: PMID-40589299
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PubMed:
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@article {pmid40589299,
year = {2025},
author = {George, I and Rao, DP and Jain, A and Ascione, G and Sharma, M and Meharwal, ZS and Sarkar, B and Kochar, N and Shastri, N and Runt, J and Whisenant, B and Wilson, B and Kiser, A and Leon, MB and Pandey, K},
title = {1-Year Results From a Multicenter Trial of a Polymer Surgical Mitral Valve: Insights Into New Technology.},
journal = {Journal of the American College of Cardiology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jacc.2025.06.017},
pmid = {40589299},
issn = {1558-3597},
abstract = {BACKGROUND: Polymer leaflet material may extend the durability of surgical mitral valve replacement (SMVR) to provide stable long-term hemodynamics. The India Mitral Surgical Trial sought to evaluate the safety and performance of a novel polymer leaflet material as part of a surgical mitral valve (MV) prosthesis.
OBJECTIVES: In this study, the authors sought to report 1-year outcomes in patients undergoing SMVR for MV disease using the Tria Mitral Valve (Foldax).
METHODS: Adult patients requiring MV replacement were enrolled in a prospective single-arm multicenter trial at 8 clinical sites in India from April to November 2023. An independent physician screening committee reviewed each patient for study eligibility before enrollment. Safety events were adjudicated per standard Valve Academic Research Consortium 3 criteria guidelines, and valve performance was assessed by means of echocardiographic and computed tomographic imaging at 30 days and 1 year. Patients were maintained on a vitamin K antagonist (target international normalized ratio: 2.5).
RESULTS: Sixty-seven patients, of whom 64% were female (48% of childbearing age), with a mean age of 42 years (range: 19-67 years), mean body mass index of 22.7 kg/m[2], and body surface area of 1.6 cm[2] were treated with SMVR with 100% technical success. Most patients (54%) were NYHA functional class III or IV at baseline. The mean Society of Thoracic Surgeons score was 1.4%. The etiology of MV disease was stenosis in 27%, regurgitation in 30%, and mixed in 43% of patients, primarily secondary to rheumatic heart disease. The 1-year rates for all-cause mortality, thromboembolic events, stroke, structural valve deterioration, and valve reintervention were 9.1%, 7.5%, 4.9%, 0%, and 0%, respectively. No death was valve related. One-year effective orifice area and mean inflow gradient were 1.4 ± 0.4 cm[2] and 4.6 ± 1.7 mm Hg, respectively. There were 2 thrombotic events and 3 ischemic strokes, all in patients with subtherapeutic international normalized ratio.
CONCLUSIONS: The polymer surgical MV demonstrated an acceptable safety profile and maintained stable hemodynamic performance through 1 year in patients undergoing MV replacement. Further study of this promising polymer leaflet technology is ongoing. (Clinical Investigation for the Foldax Tria Mitral Valve-India; NCT06191718).},
}
RevDate: 2025-07-16
CmpDate: 2025-07-16
Why Consumer Neurofeedback Devices Are More Than Hype for Brain Health.
IEEE pulse, 16(3):21-24.
Neurofeedback uses a brain-computer interface to measure a person's brain activity and show it to them in real time. A number of companies offer neurofeedback devices directly to consumers, with promises of improving meditation and enhancing concentration. However, whether neurofeedback is actually effective remains controversial among researchers.
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@article {pmid40668685,
year = {2025},
author = {Bates, M},
title = {Why Consumer Neurofeedback Devices Are More Than Hype for Brain Health.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {21-24},
doi = {10.1109/MPULS.2025.3572577},
pmid = {40668685},
issn = {2154-2317},
mesh = {Humans ; *Neurofeedback/instrumentation ; *Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {Neurofeedback uses a brain-computer interface to measure a person's brain activity and show it to them in real time. A number of companies offer neurofeedback devices directly to consumers, with promises of improving meditation and enhancing concentration. However, whether neurofeedback is actually effective remains controversial among researchers.},
}
MeSH Terms:
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Humans
*Neurofeedback/instrumentation
*Brain/physiology
*Brain-Computer Interfaces
Electroencephalography
RevDate: 2025-07-16
CmpDate: 2025-07-16
Industry Corner Live With Synchron CEO Tom Oxley.
IEEE pulse, 16(3):43-49.
Pulse's Industry Corner Live featured a dynamic live Q&A session between IEEE Pulse Editor-in-Chief Chad Andresen and Dr. Tom Oxley, CEO and co-founder of Synchron, a leader in minimally invasive brain-computer interface (BCI) technology. The discussion explored the intersection of neurotechnology, artificial intelligence, and the evolving landscape of entrepreneurship in the MedTech sector. Dr. Oxley shared insights into Synchron's pioneering work with endovascular BCIs, offering a less invasive alternative to traditional neurosurgical approaches, and how this technology is reshaping the possibilities for restoring communication in patients with paralysis. The conversation delved into the growing role of AI in decoding neural signals and driving clinical translation, while also addressing the regulatory, financial, and ethical challenges faced by entrepreneurs in the neurotechnology space. With candid reflections on his journey from clinician to startup founder, Oxley provided an inside look at what it takes to bring disruptive technologies from concept to clinic. This session offered a rare glimpse into the mindset of a neurotech innovator navigating the high-stakes interface of science, medicine, and industry.
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@article {pmid40668684,
year = {2025},
author = {Anderson, C},
title = {Industry Corner Live With Synchron CEO Tom Oxley.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {43-49},
doi = {10.1109/MPULS.2025.3572578},
pmid = {40668684},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces ; *Biomedical Engineering ; Artificial Intelligence ; },
abstract = {Pulse's Industry Corner Live featured a dynamic live Q&A session between IEEE Pulse Editor-in-Chief Chad Andresen and Dr. Tom Oxley, CEO and co-founder of Synchron, a leader in minimally invasive brain-computer interface (BCI) technology. The discussion explored the intersection of neurotechnology, artificial intelligence, and the evolving landscape of entrepreneurship in the MedTech sector. Dr. Oxley shared insights into Synchron's pioneering work with endovascular BCIs, offering a less invasive alternative to traditional neurosurgical approaches, and how this technology is reshaping the possibilities for restoring communication in patients with paralysis. The conversation delved into the growing role of AI in decoding neural signals and driving clinical translation, while also addressing the regulatory, financial, and ethical challenges faced by entrepreneurs in the neurotechnology space. With candid reflections on his journey from clinician to startup founder, Oxley provided an inside look at what it takes to bring disruptive technologies from concept to clinic. This session offered a rare glimpse into the mindset of a neurotech innovator navigating the high-stakes interface of science, medicine, and industry.},
}
MeSH Terms:
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Humans
*Brain-Computer Interfaces
*Biomedical Engineering
Artificial Intelligence
RevDate: 2025-07-16
An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.
Cell reports, 44(7):115862 pii:S2211-1247(25)00633-3 [Epub ahead of print].
Cortical circuits contain diverse sensory, motor, and cognitive signals, and they form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We develop a calcium-imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we find that mice can immediately navigate toward goal locations when control is switched to the BMI. No learning or adaptation is observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decouple from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.
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@article {pmid40668677,
year = {2025},
author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T},
title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.},
journal = {Cell reports},
volume = {44},
number = {7},
pages = {115862},
doi = {10.1016/j.celrep.2025.115862},
pmid = {40668677},
issn = {2211-1247},
abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and they form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We develop a calcium-imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we find that mice can immediately navigate toward goal locations when control is switched to the BMI. No learning or adaptation is observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decouple from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.},
}
RevDate: 2025-07-16
The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.
Frontiers in neuroergonomics, 6:1582724.
Neuroadaptive technologies are a type of passive Brain-computer interface (pBCI) that aim to incorporate implicit user-state information into human-machine interactions by monitoring neurophysiological signals. Evaluating machine learning and signal processing approaches represents a core aspect of research into neuroadaptive technologies. These evaluations are often conducted under controlled laboratory settings and offline, where exhaustive analyses are possible. However, the manner in which classifiers are evaluated offline has been shown to impact reported accuracy levels, possibly biasing conclusions. In the current study, we investigated one of these sources of bias, the choice of cross-validation scheme, which is often not reported in sufficient detail. Across three independent electroencephalography (EEG) n-back datasets and 74 participants, we show how metrics and conclusions based on the same data can diverge with different cross-validation choices. A comparison of cross-validation schemes in which train and test subset boundaries either respect the block-structure of the data collection or not, illustrated how the relative performance of classifiers varies significantly with the evaluation method used. By computing bootstrapped 95% confidence intervals of differences across datasets, we showed that classification accuracies of Riemannian minimum distance (RMDM) classifiers may differ by up to 12.7% while those of a Filter Bank Common Spatial Pattern (FBCSP) based linear discriminant analysis (LDA) may differ by up to 30.4%. These differences across cross-validation implementations may impact the conclusions presented in research papers, which can complicate efforts to foster reproducibility. Our results exemplify why detailed reporting on data splitting procedures should become common practice.
Additional Links: PMID-40667422
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Citation:
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@article {pmid40667422,
year = {2025},
author = {Schroeder, F and Fairclough, S and Dehais, F and Richins, M},
title = {The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1582724},
pmid = {40667422},
issn = {2673-6195},
abstract = {Neuroadaptive technologies are a type of passive Brain-computer interface (pBCI) that aim to incorporate implicit user-state information into human-machine interactions by monitoring neurophysiological signals. Evaluating machine learning and signal processing approaches represents a core aspect of research into neuroadaptive technologies. These evaluations are often conducted under controlled laboratory settings and offline, where exhaustive analyses are possible. However, the manner in which classifiers are evaluated offline has been shown to impact reported accuracy levels, possibly biasing conclusions. In the current study, we investigated one of these sources of bias, the choice of cross-validation scheme, which is often not reported in sufficient detail. Across three independent electroencephalography (EEG) n-back datasets and 74 participants, we show how metrics and conclusions based on the same data can diverge with different cross-validation choices. A comparison of cross-validation schemes in which train and test subset boundaries either respect the block-structure of the data collection or not, illustrated how the relative performance of classifiers varies significantly with the evaluation method used. By computing bootstrapped 95% confidence intervals of differences across datasets, we showed that classification accuracies of Riemannian minimum distance (RMDM) classifiers may differ by up to 12.7% while those of a Filter Bank Common Spatial Pattern (FBCSP) based linear discriminant analysis (LDA) may differ by up to 30.4%. These differences across cross-validation implementations may impact the conclusions presented in research papers, which can complicate efforts to foster reproducibility. Our results exemplify why detailed reporting on data splitting procedures should become common practice.},
}
RevDate: 2025-07-16
Study on the Effect of the Envelope of Terahertz Unipolar Stimulation on Cell Membrane Communication-Related Variables.
Research (Washington, D.C.), 8:0755.
The development of terahertz science and technology has shown new application prospects in artificial intelligence. Terahertz stimulation can lead to information communication of cells. Terahertz unipolar picosecond pulse train stimulation can activate cell membrane hydrophilic pores and protein ion channels. However, the effect of the envelope of the terahertz unipolar stimulation remains unknown. This paper studies the effect of the envelope on membrane communication-related variables and the accompanying energy consumption by a cell model with considerations of hydrophilic pores and Na[+], K[+]-ATPase. According to the results, terahertz unipolar picosecond pulse train stimulation can deliver the signal contained in its envelope into the variation rates of membrane potentials no matter whether the hydrophilic pores are activated or not and also into the variation rates of the ion flow via the pores after activation of the pores. In contrast, the ion flow via Na[+], K[+]-ATPase seems irrelevant to the signal in the envelope. Moreover, the ion flows show a modulation effect on the variation rates of membrane potentials. The accompanying power dissipations in the cases of different envelopes are similar, as low as around the level of 10[-11] W. The results lay the foundations for application in artificial intelligence, like brain-machine communications.
Additional Links: PMID-40666829
PubMed:
Citation:
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@article {pmid40666829,
year = {2025},
author = {Bo, W and Che, R and Jia, F and Sun, K and Liu, Q and Guo, L and Zhang, X and Gong, Y},
title = {Study on the Effect of the Envelope of Terahertz Unipolar Stimulation on Cell Membrane Communication-Related Variables.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0755},
pmid = {40666829},
issn = {2639-5274},
abstract = {The development of terahertz science and technology has shown new application prospects in artificial intelligence. Terahertz stimulation can lead to information communication of cells. Terahertz unipolar picosecond pulse train stimulation can activate cell membrane hydrophilic pores and protein ion channels. However, the effect of the envelope of the terahertz unipolar stimulation remains unknown. This paper studies the effect of the envelope on membrane communication-related variables and the accompanying energy consumption by a cell model with considerations of hydrophilic pores and Na[+], K[+]-ATPase. According to the results, terahertz unipolar picosecond pulse train stimulation can deliver the signal contained in its envelope into the variation rates of membrane potentials no matter whether the hydrophilic pores are activated or not and also into the variation rates of the ion flow via the pores after activation of the pores. In contrast, the ion flow via Na[+], K[+]-ATPase seems irrelevant to the signal in the envelope. Moreover, the ion flows show a modulation effect on the variation rates of membrane potentials. The accompanying power dissipations in the cases of different envelopes are similar, as low as around the level of 10[-11] W. The results lay the foundations for application in artificial intelligence, like brain-machine communications.},
}
RevDate: 2025-07-15
Characterizing the neural representations and decoding performance of foot rhythmic motor execution or imagery guided by action observation.
Journal of neural engineering [Epub ahead of print].
OBJECTIVE: The limited spatial resolution inherent in electroencephalography (EEG), a widely-adopted non-invasive neuroimaging technique, combined with the intrinsic complexity of performing unilateral lower-limb motor imagery (MI), restricts decoding accuracy. To address these challenges, we propose a paradigm based on action observation-guided rhythmic motor execution (AO-ME) and motor imagery (AO-MI), designed to simplify task demands and enhance decoding performance. Magnetoencephalography (MEG) serves as the data acquisition method, leveraging its superior spatiotemporal resolution.
APPROACH: Spatiotemporal and spectral features were characterized at the sensor level, and source imaging techniques were employed to examine cortical activation patterns. Ensemble task-related component analysis (eTRCA) facilitated decoding of unilateral tasks.
Main results.
Robust lateralized neural responses were observed, exhibiting low-frequency phase-locked components that distinctly reflected the task frequency and its second harmonic within sensorimotor, parietal, and occipital cortices. Moreover, significant contralateral suppression of the sensorimotor rhythm was observed. Decoding accuracies reached 95.22% ± 4.75% for AO-ME and 88.66% ± 8.52% for AO-MI across twenty participants based on the phase-locked features.
SIGNIFICANCE: Collectively, our findings demonstrate that the AO-ME/MI paradigm elicits stable, distinguishable neural activity, highlighting its potential as an effective strategy for decoding unilateral lower-limb movements within brain-computer interface (BCI) applications.
.
Additional Links: PMID-40664224
Publisher:
PubMed:
Citation:
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@article {pmid40664224,
year = {2025},
author = {Wang, X and Meng, J and Zheng, Y and Wei, Y and Wang, F and Ding, H and Zhuo, Y},
title = {Characterizing the neural representations and decoding performance of foot rhythmic motor execution or imagery guided by action observation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf011},
pmid = {40664224},
issn = {1741-2552},
abstract = {OBJECTIVE: The limited spatial resolution inherent in electroencephalography (EEG), a widely-adopted non-invasive neuroimaging technique, combined with the intrinsic complexity of performing unilateral lower-limb motor imagery (MI), restricts decoding accuracy. To address these challenges, we propose a paradigm based on action observation-guided rhythmic motor execution (AO-ME) and motor imagery (AO-MI), designed to simplify task demands and enhance decoding performance. Magnetoencephalography (MEG) serves as the data acquisition method, leveraging its superior spatiotemporal resolution.
APPROACH: Spatiotemporal and spectral features were characterized at the sensor level, and source imaging techniques were employed to examine cortical activation patterns. Ensemble task-related component analysis (eTRCA) facilitated decoding of unilateral tasks.
Main results.
Robust lateralized neural responses were observed, exhibiting low-frequency phase-locked components that distinctly reflected the task frequency and its second harmonic within sensorimotor, parietal, and occipital cortices. Moreover, significant contralateral suppression of the sensorimotor rhythm was observed. Decoding accuracies reached 95.22% ± 4.75% for AO-ME and 88.66% ± 8.52% for AO-MI across twenty participants based on the phase-locked features.
SIGNIFICANCE: Collectively, our findings demonstrate that the AO-ME/MI paradigm elicits stable, distinguishable neural activity, highlighting its potential as an effective strategy for decoding unilateral lower-limb movements within brain-computer interface (BCI) applications.
.},
}
RevDate: 2025-07-15
Detection of movement-related cortical potentials associated with upper and low limb movements in patients with multiple sclerosis for brain-computer interfacing.
Journal of neural engineering [Epub ahead of print].
Brain-computer interface (BCI) training has been shown to be effective for inducing neural plasticity and for improving motor function in stroke patients. BCI training could potentially have a positive effect on people with multiple sclerosis (MS) as well by pairing movement-related brain activity with congruent afferent feedback from e.g. functional electrical stimulation. In the current study, the aim was to detect movement-related cortical potentials (MRCPs) from single-trial EEG in people with MS across two separate days using different classifier calibration schemes to estimate the performance of a BCI that can be used for neurorehabilitation. Approach: Fifteen individuals with MS performed 100 wrist movements and 100 ankle movements while continuous EEG was recorded. Also, idle brain activity was recorded. This was repeated on a separate day. The data were filtered and divided into epochs containing data prior to the movement onset. Temporal, spectral and template-matching features were extracted and classified with a random forest classifier using different calibration schemes to estimate the performance when training the classifier on data from the same day and same participant, different day but same participant, and across different participants. Main results: Clear MRCPs were elicited across both recording days, and it was possible to discriminate between idle activity and movement-related brain activity with accuracies between ~80-90% when training and testing the classifier on data from the same day and participant. The performance decreased when using data from a separate day but same participant (~70-80%) or data from separate participants (~70%) for training the classifier. Significance: The results showed that it is feasible for people with MS to use a BCI for inducing neural plasticity. .
Additional Links: PMID-40664221
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PubMed:
Citation:
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@article {pmid40664221,
year = {2025},
author = {Jochumsen, M and Petersen, BS and Mikkelsen Vestergaard, L and Falborg, NF and Wisler, L and Olesen, MV and Andersen, MS and Sørensen, NB and Jørgensen, ST},
title = {Detection of movement-related cortical potentials associated with upper and low limb movements in patients with multiple sclerosis for brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf010},
pmid = {40664221},
issn = {1741-2552},
abstract = {Brain-computer interface (BCI) training has been shown to be effective for inducing neural plasticity and for improving motor function in stroke patients. BCI training could potentially have a positive effect on people with multiple sclerosis (MS) as well by pairing movement-related brain activity with congruent afferent feedback from e.g. functional electrical stimulation. In the current study, the aim was to detect movement-related cortical potentials (MRCPs) from single-trial EEG in people with MS across two separate days using different classifier calibration schemes to estimate the performance of a BCI that can be used for neurorehabilitation. Approach: Fifteen individuals with MS performed 100 wrist movements and 100 ankle movements while continuous EEG was recorded. Also, idle brain activity was recorded. This was repeated on a separate day. The data were filtered and divided into epochs containing data prior to the movement onset. Temporal, spectral and template-matching features were extracted and classified with a random forest classifier using different calibration schemes to estimate the performance when training the classifier on data from the same day and same participant, different day but same participant, and across different participants. Main results: Clear MRCPs were elicited across both recording days, and it was possible to discriminate between idle activity and movement-related brain activity with accuracies between ~80-90% when training and testing the classifier on data from the same day and participant. The performance decreased when using data from a separate day but same participant (~70-80%) or data from separate participants (~70%) for training the classifier. Significance: The results showed that it is feasible for people with MS to use a BCI for inducing neural plasticity. .},
}
RevDate: 2025-07-15
CmpDate: 2025-07-15
More generosity, less inequity aversion? Neural correlates of fairness perception under social distance and of its relation to generosity.
Cerebral cortex (New York, N.Y. : 1991), 35(7):.
Humans instinctively react negatively to inequity, while generosity counters this tendency. Previous studies show that both fairness perception and generosity involve balancing behaviors and motivations in social interactions. However, their relationship remains underexplored, limiting our understanding of the complex psychological processes underlying social behavior. Using a social discounting task, we assessed individual generosity, while an Ultimatum Game task with concurrent electroencephalogram recording allowed us to quantify inequity aversion and fairness perception by manipulating social distance and inequity levels. We found that both generosity and fairness perception decrease with increasing social distance, whereas inequity aversion increases. Modeling the decay of generosity across social distances, we found that decayed generosity was positively associated with inequity aversion in the friend condition and negatively correlated with the attenuation of fairness perception. These results suggest that the decay of generosity with social distance is linked to reduced sensitivity to inequity toward friends and heightened neural differences in fairness perception across social relationships. Our study provides electrophysiological evidence of individual variability in generosity and inequity aversion influenced by social distance, expanding inequity aversion theory.
Additional Links: PMID-40663645
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@article {pmid40663645,
year = {2025},
author = {Wang, A and Lin, C and Mao, W and Jin, J},
title = {More generosity, less inequity aversion? Neural correlates of fairness perception under social distance and of its relation to generosity.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf152},
pmid = {40663645},
issn = {1460-2199},
support = {//Shanghai Philosophy and Social Sciences Planning Project/ ; //National Nature Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; Electroencephalography ; Adult ; *Psychological Distance ; *Social Behavior ; *Social Perception ; *Brain/physiology ; Interpersonal Relations ; Games, Experimental ; *Altruism ; Adolescent ; },
abstract = {Humans instinctively react negatively to inequity, while generosity counters this tendency. Previous studies show that both fairness perception and generosity involve balancing behaviors and motivations in social interactions. However, their relationship remains underexplored, limiting our understanding of the complex psychological processes underlying social behavior. Using a social discounting task, we assessed individual generosity, while an Ultimatum Game task with concurrent electroencephalogram recording allowed us to quantify inequity aversion and fairness perception by manipulating social distance and inequity levels. We found that both generosity and fairness perception decrease with increasing social distance, whereas inequity aversion increases. Modeling the decay of generosity across social distances, we found that decayed generosity was positively associated with inequity aversion in the friend condition and negatively correlated with the attenuation of fairness perception. These results suggest that the decay of generosity with social distance is linked to reduced sensitivity to inequity toward friends and heightened neural differences in fairness perception across social relationships. Our study provides electrophysiological evidence of individual variability in generosity and inequity aversion influenced by social distance, expanding inequity aversion theory.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
Male
Female
Young Adult
Electroencephalography
Adult
*Psychological Distance
*Social Behavior
*Social Perception
*Brain/physiology
Interpersonal Relations
Games, Experimental
*Altruism
Adolescent
RevDate: 2025-07-15
A Mini-Review on Unlocking Cognitive Enhancement: An Innovative Strategy for Optimal Brain Functions.
Central nervous system agents in medicinal chemistry pii:CNSAMC-EPUB-149385 [Epub ahead of print].
Cognitive enhancement, aimed at improving or preserving memory, attention, and executive functions, has gained significant interest from both the scientific community and the public. This review explores various strategies for enhancing cognitive function, including natural compounds, synthetic enhancers, and behavioural approaches. Natural compounds like curcumin, Ginkgo biloba, Panax ginseng, and Rhodiola rosea are examined for their cognitive benefits, with ongoing research on their mechanisms and potential nanoformulation-based drug delivery. Synthetic enhancers such as Modafinil, Piracetam, Methylphenidate, and Noopept show promise in improving cognitive functions. Additionally, substances influencing brain metabolism, like Creatine and Coenzyme Q10, are discussed. Behavioural interventions, including sleep optimization, meditation, and physical exercise, are evaluated for their cognitive-enhancing effects. Noninvasive brain stimulation techniques, such as TMS and tDCS, along with innovative methods like whole-body vibration and brain-machine interfaces, are also explored. The review emphasizes the complex interplay of these strategies and the need for continued research to fully exploit their potential. By highlighting natural compounds, synthetic drugs, and behavioural approaches, the review advocates for a multifaceted approach to cognitive enhancement and calls for more detailed and longitudinal studies to understand their long-term benefits and mechanisms.
Additional Links: PMID-40662561
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@article {pmid40662561,
year = {2025},
author = {Vikal, A and Maurya, R and Patel, BB and Patel, P and Kumar, M and Kurmi, BD},
title = {A Mini-Review on Unlocking Cognitive Enhancement: An Innovative Strategy for Optimal Brain Functions.},
journal = {Central nervous system agents in medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.2174/0118715249357704250702140026},
pmid = {40662561},
issn = {1875-6166},
abstract = {Cognitive enhancement, aimed at improving or preserving memory, attention, and executive functions, has gained significant interest from both the scientific community and the public. This review explores various strategies for enhancing cognitive function, including natural compounds, synthetic enhancers, and behavioural approaches. Natural compounds like curcumin, Ginkgo biloba, Panax ginseng, and Rhodiola rosea are examined for their cognitive benefits, with ongoing research on their mechanisms and potential nanoformulation-based drug delivery. Synthetic enhancers such as Modafinil, Piracetam, Methylphenidate, and Noopept show promise in improving cognitive functions. Additionally, substances influencing brain metabolism, like Creatine and Coenzyme Q10, are discussed. Behavioural interventions, including sleep optimization, meditation, and physical exercise, are evaluated for their cognitive-enhancing effects. Noninvasive brain stimulation techniques, such as TMS and tDCS, along with innovative methods like whole-body vibration and brain-machine interfaces, are also explored. The review emphasizes the complex interplay of these strategies and the need for continued research to fully exploit their potential. By highlighting natural compounds, synthetic drugs, and behavioural approaches, the review advocates for a multifaceted approach to cognitive enhancement and calls for more detailed and longitudinal studies to understand their long-term benefits and mechanisms.},
}
RevDate: 2025-07-14
Reduced Cochlear Implant Performance in Listeners with Single-Sided Deafness: Comparison with Bilateral Listeners.
Journal of the Association for Research in Otolaryngology : JARO [Epub ahead of print].
PURPOSE: The efficacy of the Cochlear Implant (CI) in listeners with single-sided deafness (SSD) was evaluated by comparing single-ear speech perception in SSD listeners and bilateral cochlear implant listeners (BCI).
METHODS: Consonant-nucleus-consonant (CNC) speech perception scores for the CI-only ear in SSD listeners (N = 55; 36 female, 19 male) were compared to single-ear performance in age and device experience-matched BCI listeners (N = 55; 29 female, 26 male). Separate analyses examined: (1) a matched ear from the BCI listeners (for sequentially implanted BCI listeners, the first-implanted ear in sequential BCI listeners, or, for simultaneously implanted BCI listeners, the ear on the same side as the CI in the matching SSD listener), and (2) the lower-performing ear across BCI listeners. Additional models included moderators such as age, time since activation, CI usage, and etiology. A final analysis compared first and second implants for sequential BCI listeners.
RESULTS: SSD listeners showed significantly lower CNC performance after controlling for age, time since activation, CI usage, and etiology. Sequential BCI listeners exhibited significantly lower CNC performance on their second ear, compared to their first ear.
CONCLUSION: Speech perception with CIs is reduced in SSD listeners compared to BCI users, likely due to blocking, where the normal-hearing ear diminishes reliance on the CI. Lower performance in the second implanted ear of sequential BCI listeners also suggests greater reliance on the more experienced ear. These findings highlight the need for additional training, resources, and support to optimize CI performance in SSD listeners, despite prior evidence of positive CNC outcomes.
Additional Links: PMID-40660069
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@article {pmid40660069,
year = {2025},
author = {Jeppsen, C and McMurray, B},
title = {Reduced Cochlear Implant Performance in Listeners with Single-Sided Deafness: Comparison with Bilateral Listeners.},
journal = {Journal of the Association for Research in Otolaryngology : JARO},
volume = {},
number = {},
pages = {},
pmid = {40660069},
issn = {1438-7573},
support = {P50 DC00242//Foundation for the National Institutes of Health/ ; R01 DC008089/DC/NIDCD NIH HHS/United States ; },
abstract = {PURPOSE: The efficacy of the Cochlear Implant (CI) in listeners with single-sided deafness (SSD) was evaluated by comparing single-ear speech perception in SSD listeners and bilateral cochlear implant listeners (BCI).
METHODS: Consonant-nucleus-consonant (CNC) speech perception scores for the CI-only ear in SSD listeners (N = 55; 36 female, 19 male) were compared to single-ear performance in age and device experience-matched BCI listeners (N = 55; 29 female, 26 male). Separate analyses examined: (1) a matched ear from the BCI listeners (for sequentially implanted BCI listeners, the first-implanted ear in sequential BCI listeners, or, for simultaneously implanted BCI listeners, the ear on the same side as the CI in the matching SSD listener), and (2) the lower-performing ear across BCI listeners. Additional models included moderators such as age, time since activation, CI usage, and etiology. A final analysis compared first and second implants for sequential BCI listeners.
RESULTS: SSD listeners showed significantly lower CNC performance after controlling for age, time since activation, CI usage, and etiology. Sequential BCI listeners exhibited significantly lower CNC performance on their second ear, compared to their first ear.
CONCLUSION: Speech perception with CIs is reduced in SSD listeners compared to BCI users, likely due to blocking, where the normal-hearing ear diminishes reliance on the CI. Lower performance in the second implanted ear of sequential BCI listeners also suggests greater reliance on the more experienced ear. These findings highlight the need for additional training, resources, and support to optimize CI performance in SSD listeners, despite prior evidence of positive CNC outcomes.},
}
RevDate: 2025-07-14
Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.
The Journal of neuroscience : the official journal of the Society for Neuroscience pii:JNEUROSCI.0073-25.2025 [Epub ahead of print].
The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1: n = 175, 106 females; Study 2: n = 47, 33 females), we used a longitudinal behavioral study and a naturalistic stimuli neuroimaging study to investigate the endogenous consumer behavior similarities and their neural basis in real-world social networks. The findings reveal that friends, compared to nonfriends, exhibit higher similarity in product evaluation, which undergoes dynamic changes as the structure of social networks changes. Both neuroimaging and meta-analytic decoding results indicate that friends exhibit heightened neural synchrony, which is linked to cognitive functions such as object perception, attention, memory, social judgment, and reward processing. Stacking machine learning-based predictive models demonstrate that the functional connectivity maps of brain activity can predict the purchase intention of their friends or their own rather than strangers. Based on the significant neural similarity which exists among individuals in close relationships within authentic social networks, the current study reveals the predictive capacity of neural activity in predicting the behavior of friends.Significance Statement Understanding the endogenous aspects of social impact is critical for comprehending human social behavior. The current study provides novel evidence that close social relationships within real-world networks exhibit heightened behavioral and neural synchrony, and dynamically evolve with changes in social network structures. Using naturalistic stimuli and longitudinal studies, it is demonstrated that neural activity not only reflects shared cognitive functions, but also predicts purchase intentions of individuals and their close friends with greater accuracy than strangers. These findings uncover the neural basis of endogenous consumer behavior similarities and highlight the predictive capacity of brain activity in understanding and forecasting the behavior of individuals within close social connections. This research offers valuable insights into the intersection of neuroscience, social behavior, and consumer decision-making.
Additional Links: PMID-40659530
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@article {pmid40659530,
year = {2025},
author = {Hu, Y and Ma, B and Jin, J},
title = {Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0073-25.2025},
pmid = {40659530},
issn = {1529-2401},
abstract = {The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1: n = 175, 106 females; Study 2: n = 47, 33 females), we used a longitudinal behavioral study and a naturalistic stimuli neuroimaging study to investigate the endogenous consumer behavior similarities and their neural basis in real-world social networks. The findings reveal that friends, compared to nonfriends, exhibit higher similarity in product evaluation, which undergoes dynamic changes as the structure of social networks changes. Both neuroimaging and meta-analytic decoding results indicate that friends exhibit heightened neural synchrony, which is linked to cognitive functions such as object perception, attention, memory, social judgment, and reward processing. Stacking machine learning-based predictive models demonstrate that the functional connectivity maps of brain activity can predict the purchase intention of their friends or their own rather than strangers. Based on the significant neural similarity which exists among individuals in close relationships within authentic social networks, the current study reveals the predictive capacity of neural activity in predicting the behavior of friends.Significance Statement Understanding the endogenous aspects of social impact is critical for comprehending human social behavior. The current study provides novel evidence that close social relationships within real-world networks exhibit heightened behavioral and neural synchrony, and dynamically evolve with changes in social network structures. Using naturalistic stimuli and longitudinal studies, it is demonstrated that neural activity not only reflects shared cognitive functions, but also predicts purchase intentions of individuals and their close friends with greater accuracy than strangers. These findings uncover the neural basis of endogenous consumer behavior similarities and highlight the predictive capacity of brain activity in understanding and forecasting the behavior of individuals within close social connections. This research offers valuable insights into the intersection of neuroscience, social behavior, and consumer decision-making.},
}
RevDate: 2025-07-14
CmpDate: 2025-07-14
Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.
PloS one, 20(7):e0325850.
Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. Consequently, the efficacy of fNIRS signals in predicting the valence of standardized stimuli from the International Affective Picture System (IAPS) was assessed. fNIRS data were collected from twenty healthy participants while images from the IAPS database were presented. The images varied in both valence (i.e., positive, neutral, negative) and arousal (i.e., high, low) level. Hemodynamic responses of prefrontal cortical (PFC) regions were recorded with a twenty-two channel system. Twenty fNIRS derived time domain features were extracted from HbO time traces of each channel corresponding to each stimulus period. Classification performances of three machine learning algorithms, namely the k-Nearest Neighbors (kNN), Ensemble (Subspace kNN) and Support Vector Machines (SVM), in two class and three class classification of positive, neutral and negative states were evaluated with ten runs of a tenfold cross-validation procedure through splitting the data into test, train and validation groups at each run. Three class classification performances of all algorithms were above 90% in terms of accuracy, sensitivity, specificity, F-1 score and precision metrics while two class accuracy performances of all algorithms were above 93% in terms of each performance metric. The high-performance classification results highlight the potential of fNIRS-based BCI systems for real-time, objective detection of basic emotional states for daily life and clinical applications. fNIRSbased BCIs may show promise for future developments in personalized user experiences and clinical applications due to their practicality and low computational complexity.
Additional Links: PMID-40658672
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@article {pmid40658672,
year = {2025},
author = {Eser, A and Erdoğan, SB},
title = {Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.},
journal = {PloS one},
volume = {20},
number = {7},
pages = {e0325850},
pmid = {40658672},
issn = {1932-6203},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; *Supervised Machine Learning ; Algorithms ; Prefrontal Cortex/physiology ; Support Vector Machine ; },
abstract = {Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. Consequently, the efficacy of fNIRS signals in predicting the valence of standardized stimuli from the International Affective Picture System (IAPS) was assessed. fNIRS data were collected from twenty healthy participants while images from the IAPS database were presented. The images varied in both valence (i.e., positive, neutral, negative) and arousal (i.e., high, low) level. Hemodynamic responses of prefrontal cortical (PFC) regions were recorded with a twenty-two channel system. Twenty fNIRS derived time domain features were extracted from HbO time traces of each channel corresponding to each stimulus period. Classification performances of three machine learning algorithms, namely the k-Nearest Neighbors (kNN), Ensemble (Subspace kNN) and Support Vector Machines (SVM), in two class and three class classification of positive, neutral and negative states were evaluated with ten runs of a tenfold cross-validation procedure through splitting the data into test, train and validation groups at each run. Three class classification performances of all algorithms were above 90% in terms of accuracy, sensitivity, specificity, F-1 score and precision metrics while two class accuracy performances of all algorithms were above 93% in terms of each performance metric. The high-performance classification results highlight the potential of fNIRS-based BCI systems for real-time, objective detection of basic emotional states for daily life and clinical applications. fNIRSbased BCIs may show promise for future developments in personalized user experiences and clinical applications due to their practicality and low computational complexity.},
}
MeSH Terms:
show MeSH Terms
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Humans
Spectroscopy, Near-Infrared/methods
*Brain-Computer Interfaces
*Emotions/physiology
Male
Female
Adult
Young Adult
*Supervised Machine Learning
Algorithms
Prefrontal Cortex/physiology
Support Vector Machine
RevDate: 2025-07-14
Electroencephalogram-Based Satisfaction Assessment Brain-Computer Interface in Emerging Video Service by Using Graph Representation Learning.
Brain connectivity [Epub ahead of print].
Background: Emerging video services (EVS) offer users various multimedia presentations, and satisfaction assessment is crucial for enhancing their user experience and competitiveness. However, existing research methods are unable to provide a quantitative satisfaction assessment. Electroencephalogram (EEG), as a popular signal source in brain-computer interface (BCI), with the advantage of being difficult to disguise and containing rich brain activity information, has gained increasing attention from researchers. This article aims to investigate the advantages of employing EEG for modeling satisfaction in EVS. Unlike the subjective metrics assessment in traditional video services, generating satisfaction in EVS involves a range of cognitive functions, including cognitive load, emotion, and audiovisual perception, which are difficult to characterize using a single feature. The representation of brain states for complex cognitive functions has been a major challenge for EEG modeling approaches. Methods: To address this challenge, we propose an EEG-based EVS satisfaction assessment BCI by raising a Point-to-Global graph representation learning strategy (P2G) that efficiently identifies satisfaction level through a parallel coding module and a graph-based brain region perception module. P2G captures satisfaction-sensitive graph representations in EEG samples based on coding and integrating point features and the global topography. Results: We validate the effectiveness of introducing a P2G learning strategy in EVS satisfaction modeling using a self-constructed dataset and a relevant public dataset, and our method outperforms existing methods. Additionally, we provide a detailed visual analysis to unveil neural markers associated with EVS satisfaction, thereby laying a scientific foundation for the optimization and development of video services.
Additional Links: PMID-40658035
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@article {pmid40658035,
year = {2025},
author = {Niu, Y and Li, Z and Zeng, G and Zhang, Y and Yao, L and Wu, X},
title = {Electroencephalogram-Based Satisfaction Assessment Brain-Computer Interface in Emerging Video Service by Using Graph Representation Learning.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1177/21580014251359107},
pmid = {40658035},
issn = {2158-0022},
abstract = {Background: Emerging video services (EVS) offer users various multimedia presentations, and satisfaction assessment is crucial for enhancing their user experience and competitiveness. However, existing research methods are unable to provide a quantitative satisfaction assessment. Electroencephalogram (EEG), as a popular signal source in brain-computer interface (BCI), with the advantage of being difficult to disguise and containing rich brain activity information, has gained increasing attention from researchers. This article aims to investigate the advantages of employing EEG for modeling satisfaction in EVS. Unlike the subjective metrics assessment in traditional video services, generating satisfaction in EVS involves a range of cognitive functions, including cognitive load, emotion, and audiovisual perception, which are difficult to characterize using a single feature. The representation of brain states for complex cognitive functions has been a major challenge for EEG modeling approaches. Methods: To address this challenge, we propose an EEG-based EVS satisfaction assessment BCI by raising a Point-to-Global graph representation learning strategy (P2G) that efficiently identifies satisfaction level through a parallel coding module and a graph-based brain region perception module. P2G captures satisfaction-sensitive graph representations in EEG samples based on coding and integrating point features and the global topography. Results: We validate the effectiveness of introducing a P2G learning strategy in EVS satisfaction modeling using a self-constructed dataset and a relevant public dataset, and our method outperforms existing methods. Additionally, we provide a detailed visual analysis to unveil neural markers associated with EVS satisfaction, thereby laying a scientific foundation for the optimization and development of video services.},
}
RevDate: 2025-07-14
Mechanically-adaptive, resveratrol-eluting neural probes for improved intracortical recording performance and stability.
Npj flexible electronics, 9(1):64.
Intracortical microelectrodes are used for recording activity from individual neurons, providing both a valuable neuroscience tool and an enabling medical technology for individuals with motor disabilities. Standard neural probes carrying the microelectrodes are rigid silicon-based structures that can penetrate the brain parenchyma to interface with the targeted neurons. Unfortunately, within weeks after implantation, neural recording quality from microelectrodes degrades, owing largely to a neuroinflammatory response. Key contributors to the neuroinflammatory response include mechanical mismatch at the device-tissue interface and oxidative stress. We developed a mechanically-adaptive, resveratrol-eluting (MARE) neural probe to mitigate both mechanical mismatch and oxidative stress and thereby promote improved neural recording quality and longevity. In this work, we demonstrate that compared to rigid silicon controls, highly-flexible MARE probes exhibit improved recording performance, more stable impedance, and a healing tissue response. With further optimization, MARE probes can serve as long-term, robust neural probes for brain-machine interface applications.
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@article {pmid40656548,
year = {2025},
author = {Mueller, NN and Ocoko, MYM and Kim, Y and Li, K and Gisser, K and Glusauskas, G and Lugo, I and Dernelle, P and Hermoso, AC and Wang, J and Duncan, J and Druschel, LN and Graham, F and Capadona, JR and Hess-Dunning, A},
title = {Mechanically-adaptive, resveratrol-eluting neural probes for improved intracortical recording performance and stability.},
journal = {Npj flexible electronics},
volume = {9},
number = {1},
pages = {64},
pmid = {40656548},
issn = {2397-4621},
abstract = {Intracortical microelectrodes are used for recording activity from individual neurons, providing both a valuable neuroscience tool and an enabling medical technology for individuals with motor disabilities. Standard neural probes carrying the microelectrodes are rigid silicon-based structures that can penetrate the brain parenchyma to interface with the targeted neurons. Unfortunately, within weeks after implantation, neural recording quality from microelectrodes degrades, owing largely to a neuroinflammatory response. Key contributors to the neuroinflammatory response include mechanical mismatch at the device-tissue interface and oxidative stress. We developed a mechanically-adaptive, resveratrol-eluting (MARE) neural probe to mitigate both mechanical mismatch and oxidative stress and thereby promote improved neural recording quality and longevity. In this work, we demonstrate that compared to rigid silicon controls, highly-flexible MARE probes exhibit improved recording performance, more stable impedance, and a healing tissue response. With further optimization, MARE probes can serve as long-term, robust neural probes for brain-machine interface applications.},
}
RevDate: 2025-07-15
CmpDate: 2025-07-15
Disruption of the KLHL37-N-Myc complex restores N-Myc degradation and arrests neuroblastoma growth in mouse models.
The Journal of clinical investigation, 135(14):.
The N-Myc gene MYCN amplification accounts for the most common genetic aberration in neuroblastoma and strongly predicts the aggressive progression and poor clinical prognosis. However, clinically effective therapies that directly target N-Myc activity are limited. N-Myc is a transcription factor, and its stability is tightly controlled by ubiquitination-dependent proteasomal degradation. Here, we discovered that Kelch-like protein 37 (KLHL37) played a crucial role in enhancing the protein stability of N-Myc in neuroblastoma. KLHL37 directly interacted with N-Myc to disrupt N-Myc-FBXW7 interaction, thereby stabilizing N-Myc and enabling tumor progression. Suppressing KLHL37 effectively induced the degradation of N-Myc and had a profound inhibitory effect on the growth of MYCN-amplified neuroblastoma. Notably, we identified RTA-408 as an inhibitor of KLHL37 to disrupt the KLHL37-N-Myc complex, promoting the degradation of N-Myc and suppressing neuroblastoma in vivo and in vitro. Moreover, we elucidated the therapeutic potential of RTA-408 for neuroblastoma using patient-derived neuroblastoma cell and patient-derived xenograft tumor models. RTA408's antitumor effects may not occur exclusively via KLHL37, and specific KLHL37 inhibitors are expected to be developed in the future. These findings not only uncover the biological function of KLHL37 in regulating N-Myc stability, but also indicate that KLHL37 inhibition is a promising therapeutic regimen for neuroblastoma, especially in patients with MYCN-amplified tumors.
Additional Links: PMID-40493396
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@article {pmid40493396,
year = {2025},
author = {Xiang, S and Chen, P and Shi, X and Cai, H and Shen, Z and Liu, L and Xu, A and Zhang, J and Zhang, X and Bing, S and Wang, J and Shao, X and Cao, J and Yang, B and He, Q and Ying, M},
title = {Disruption of the KLHL37-N-Myc complex restores N-Myc degradation and arrests neuroblastoma growth in mouse models.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {14},
pages = {},
pmid = {40493396},
issn = {1558-8238},
mesh = {*Neuroblastoma/metabolism/pathology/genetics/drug therapy ; Animals ; *N-Myc Proto-Oncogene Protein/metabolism/genetics ; Humans ; Mice ; *Proteolysis ; Cell Line, Tumor ; Xenograft Model Antitumor Assays ; Proto-Oncogene Proteins c-myc ; },
abstract = {The N-Myc gene MYCN amplification accounts for the most common genetic aberration in neuroblastoma and strongly predicts the aggressive progression and poor clinical prognosis. However, clinically effective therapies that directly target N-Myc activity are limited. N-Myc is a transcription factor, and its stability is tightly controlled by ubiquitination-dependent proteasomal degradation. Here, we discovered that Kelch-like protein 37 (KLHL37) played a crucial role in enhancing the protein stability of N-Myc in neuroblastoma. KLHL37 directly interacted with N-Myc to disrupt N-Myc-FBXW7 interaction, thereby stabilizing N-Myc and enabling tumor progression. Suppressing KLHL37 effectively induced the degradation of N-Myc and had a profound inhibitory effect on the growth of MYCN-amplified neuroblastoma. Notably, we identified RTA-408 as an inhibitor of KLHL37 to disrupt the KLHL37-N-Myc complex, promoting the degradation of N-Myc and suppressing neuroblastoma in vivo and in vitro. Moreover, we elucidated the therapeutic potential of RTA-408 for neuroblastoma using patient-derived neuroblastoma cell and patient-derived xenograft tumor models. RTA408's antitumor effects may not occur exclusively via KLHL37, and specific KLHL37 inhibitors are expected to be developed in the future. These findings not only uncover the biological function of KLHL37 in regulating N-Myc stability, but also indicate that KLHL37 inhibition is a promising therapeutic regimen for neuroblastoma, especially in patients with MYCN-amplified tumors.},
}
MeSH Terms:
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*Neuroblastoma/metabolism/pathology/genetics/drug therapy
Animals
*N-Myc Proto-Oncogene Protein/metabolism/genetics
Humans
Mice
*Proteolysis
Cell Line, Tumor
Xenograft Model Antitumor Assays
Proto-Oncogene Proteins c-myc
RevDate: 2025-07-14
Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG.
Frontiers in neuroscience, 19:1576954.
INTRODUCTION: Dry electroencephalography (EEG) allows for recording cortical activity in ecological scenarios with a high channel count, but it is often more prone to artifacts as compared to gel-based EEG. Spatial harmonic analysis (SPHARA) and ICA-based methods (Fingerprint and ARCI) have been separately used in previous studies for dry EEG de-noising and physiological artifact reduction. Here, we investigate if the combination of these techniques further improves EEG signal quality. For this purpose, we also introduced an improved version of SPHARA.
METHODS: Dry 64-channel EEG was recorded from 11 healthy volunteers during a motor performance paradigm (left and right hand, feet, and tongue movements). EEG signals were denoised separately using Fingerprint + ARCI, SPHARA, a combination of these two methods, and a combination of these two methods including an improved SPHARA version. The improved version of SPHARA includes an additional zeroing of artifactual jumps in single channels before application of SPHARA. The EEG signal quality after application of each denoising method was calculated by means of standard deviation (SD), signal to noise ratio (SNR), and root mean square deviation (RMSD), and a generalized linear mixed effects (GLME) model was used to identify significant changes of these parameters and quantify the changes in the EEG signal quality.
RESULTS: The grand average values of SD improved from 9.76 (reference preprocessed EEG) to 8.28, 7.91, 6.72, and 6.15 μV for Fingerprint + ARCI, SPHARA, Fingerprint + ARCI + SPHARA, and Fingerprint + ARCI + improved SPHARA, respectively. Similarly, the RMSD values improved from 4.65 to 4.82, 6.32, and 6.90 μV, and the SNR values changed from 2.31 to 1.55, 4.08, and 5.56 dB.
DISCUSSION: Our results demonstrate the different performance aspects of Fingerprint + ARCI and SPHARA, artifact reduction and de-noising techniques that complement each other. We also demonstrated that a combination of these techniques yields superior performance in the reduction of artifacts and noise in dry EEG recordings, which can be extended to infant EEG and adult MEG applications.
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@article {pmid40656455,
year = {2025},
author = {Komosar, M and Tamburro, G and Graichen, U and Comani, S and Haueisen, J},
title = {Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1576954},
pmid = {40656455},
issn = {1662-4548},
abstract = {INTRODUCTION: Dry electroencephalography (EEG) allows for recording cortical activity in ecological scenarios with a high channel count, but it is often more prone to artifacts as compared to gel-based EEG. Spatial harmonic analysis (SPHARA) and ICA-based methods (Fingerprint and ARCI) have been separately used in previous studies for dry EEG de-noising and physiological artifact reduction. Here, we investigate if the combination of these techniques further improves EEG signal quality. For this purpose, we also introduced an improved version of SPHARA.
METHODS: Dry 64-channel EEG was recorded from 11 healthy volunteers during a motor performance paradigm (left and right hand, feet, and tongue movements). EEG signals were denoised separately using Fingerprint + ARCI, SPHARA, a combination of these two methods, and a combination of these two methods including an improved SPHARA version. The improved version of SPHARA includes an additional zeroing of artifactual jumps in single channels before application of SPHARA. The EEG signal quality after application of each denoising method was calculated by means of standard deviation (SD), signal to noise ratio (SNR), and root mean square deviation (RMSD), and a generalized linear mixed effects (GLME) model was used to identify significant changes of these parameters and quantify the changes in the EEG signal quality.
RESULTS: The grand average values of SD improved from 9.76 (reference preprocessed EEG) to 8.28, 7.91, 6.72, and 6.15 μV for Fingerprint + ARCI, SPHARA, Fingerprint + ARCI + SPHARA, and Fingerprint + ARCI + improved SPHARA, respectively. Similarly, the RMSD values improved from 4.65 to 4.82, 6.32, and 6.90 μV, and the SNR values changed from 2.31 to 1.55, 4.08, and 5.56 dB.
DISCUSSION: Our results demonstrate the different performance aspects of Fingerprint + ARCI and SPHARA, artifact reduction and de-noising techniques that complement each other. We also demonstrated that a combination of these techniques yields superior performance in the reduction of artifacts and noise in dry EEG recordings, which can be extended to infant EEG and adult MEG applications.},
}
RevDate: 2025-07-14
From pronounced to imagined: improving speech decoding with multi-condition EEG data.
Frontiers in neuroinformatics, 19:1583428.
INTRODUCTION: Imagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. Here, we investigated whether incorporating EEG data from overt (pronounced) speech could enhance imagined speech classification.
METHODS: Our approach systematically compares four classification scenarios by modifying the training dataset: intra-subject (using only imagined speech, combining overt and imagined speech, and using only overt speech) and multi-subject (combining overt speech data from different participants with the imagined speech of the target participant). We implemented all scenarios using the convolutional neural network EEGNet. To this end, twenty-four healthy participants pronounced and imagined five Spanish words.
RESULTS: In binary word-pair classifications, combining overt and imagined speech data in the intra-subject scenario led to accuracy improvements of 3%-5.17% in four out of 10 word pairs, compared to training with imagined speech only. Although the highest individual accuracy (95%) was achieved with imagined speech alone, the inclusion of overt speech data allowed more participants to surpass 70% accuracy, increasing from 10 (imagined only) to 15 participants. In the intra-subject multi-class scenario, combining overt and imagined speech did not yield statistically significant improvements over using imagined speech exclusively.
DISCUSSION: Finally, we observed that features such as word length, phonological complexity, and frequency of use contributed to higher discriminability between certain imagined word pairs. These findings suggest that incorporating overt speech data can improve imagined speech decoding in individualized models, offering a feasible strategy to support the early adoption of brain-computer interfaces before speech deterioration occurs in individuals with motor neuron diseases.
Additional Links: PMID-40655558
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@article {pmid40655558,
year = {2025},
author = {Alonso-Vázquez, D and Mendoza-Montoya, O and Caraza, R and Martinez, HR and Antelis, JM},
title = {From pronounced to imagined: improving speech decoding with multi-condition EEG data.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1583428},
pmid = {40655558},
issn = {1662-5196},
abstract = {INTRODUCTION: Imagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. Here, we investigated whether incorporating EEG data from overt (pronounced) speech could enhance imagined speech classification.
METHODS: Our approach systematically compares four classification scenarios by modifying the training dataset: intra-subject (using only imagined speech, combining overt and imagined speech, and using only overt speech) and multi-subject (combining overt speech data from different participants with the imagined speech of the target participant). We implemented all scenarios using the convolutional neural network EEGNet. To this end, twenty-four healthy participants pronounced and imagined five Spanish words.
RESULTS: In binary word-pair classifications, combining overt and imagined speech data in the intra-subject scenario led to accuracy improvements of 3%-5.17% in four out of 10 word pairs, compared to training with imagined speech only. Although the highest individual accuracy (95%) was achieved with imagined speech alone, the inclusion of overt speech data allowed more participants to surpass 70% accuracy, increasing from 10 (imagined only) to 15 participants. In the intra-subject multi-class scenario, combining overt and imagined speech did not yield statistically significant improvements over using imagined speech exclusively.
DISCUSSION: Finally, we observed that features such as word length, phonological complexity, and frequency of use contributed to higher discriminability between certain imagined word pairs. These findings suggest that incorporating overt speech data can improve imagined speech decoding in individualized models, offering a feasible strategy to support the early adoption of brain-computer interfaces before speech deterioration occurs in individuals with motor neuron diseases.},
}
RevDate: 2025-07-14
Plastic-elastomer heterostructure for robust flexible brain-computer interfaces.
bioRxiv : the preprint server for biology pii:2025.04.29.651325.
Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSoft's unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.
Additional Links: PMID-40654838
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@article {pmid40654838,
year = {2025},
author = {Lin, X and Zhang, X and Wang, Z and Chen, J and Lee, J and Lee, AJ and Yang, H and Remy, A and Shen, H and He, Y and Zhao, H and Zhang, X and Wang, W and Aljović, A and Vlassak, JJ and Lu, N and Liu, J},
title = {Plastic-elastomer heterostructure for robust flexible brain-computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.29.651325},
pmid = {40654838},
issn = {2692-8205},
abstract = {Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSoft's unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.},
}
RevDate: 2025-07-13
CmpDate: 2025-07-13
Microshear bond strength of conventional and bioactive restorative materials to irradiated and non-irradiated dentin: an in vitro study.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 33(8):688.
PURPOSE: To evaluate the effect of conventional and bioactive restorative materials on bond strength to control (non-irradiated) and irradiated dentin.
METHODS: Human dentin fragments (240) were polished and divided into non-irradiated dentin (NI; n = 120) and irradiated dentin (ID; n = 120). ID specimens received 70 Gy irradiation (2 Gy/fraction, 5 days/week for 7 weeks). All dentin surfaces were bonded to restorative materials with Scotchbond universal adhesive in self-etching mode. Microshear bond strength cylinders were built on the bonded surface according to the restorative material (4 subgroups, n = 30): conventional resin composite (CC-Filtek Z250) and three bioactive restorative composites (BCI-Activa BioActive-Restorative; BCII-Beautiful II; BCIII-Predicta Bulk). Specimens were stored in water at 37 °C for 24 h or 30 days and subjected to microshear bond strength test. The data was analyzed using two-way ANOVA and Tukey's post-hoc test (⍺ < 0.05). The morphological surface of both NI and ID dentin (n = 3) was analyzed using Scanning Electron Microscopy (SEM).
RESULTS: Two-way ANOVA revealed a significant effect of the Time/Radiation (p < 0.001). Restorative material (p = 0.191) and the interaction Time/Radiation*Restorative material (p = 0.169) were not significant. Irradiation decreased the bond strength of CC specimens at both 24 h (p < 0.001) and 30 days (p < 0.001). None of the bioactive materials were significantly affected by irradiation and storage time. The SEM analysis revealed morphological changes in the ID specimens.
CONCLUSION: Ionizing radiation-induced morphological changes in the dentin surface. These changes negatively affected the conventional resin composite bond strengths to dentin. However, these morphological alterations did not affect the bond strength of the bioactive restorative materials.
Additional Links: PMID-40653584
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@article {pmid40653584,
year = {2025},
author = {Monteiro, RV and Amarante, JEV and Bona, VS and Lins, RBE and Lopes, GC and Blackburn, M and Swanson, C and Latorre, JA and De Souza, GM},
title = {Microshear bond strength of conventional and bioactive restorative materials to irradiated and non-irradiated dentin: an in vitro study.},
journal = {Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer},
volume = {33},
number = {8},
pages = {688},
pmid = {40653584},
issn = {1433-7339},
mesh = {Humans ; *Dentin/radiation effects/ultrastructure ; *Composite Resins/chemistry ; *Dental Bonding/methods ; Materials Testing ; In Vitro Techniques ; Shear Strength ; Microscopy, Electron, Scanning ; Resin Cements/chemistry ; Dentin-Bonding Agents/chemistry ; Surface Properties ; Time Factors ; },
abstract = {PURPOSE: To evaluate the effect of conventional and bioactive restorative materials on bond strength to control (non-irradiated) and irradiated dentin.
METHODS: Human dentin fragments (240) were polished and divided into non-irradiated dentin (NI; n = 120) and irradiated dentin (ID; n = 120). ID specimens received 70 Gy irradiation (2 Gy/fraction, 5 days/week for 7 weeks). All dentin surfaces were bonded to restorative materials with Scotchbond universal adhesive in self-etching mode. Microshear bond strength cylinders were built on the bonded surface according to the restorative material (4 subgroups, n = 30): conventional resin composite (CC-Filtek Z250) and three bioactive restorative composites (BCI-Activa BioActive-Restorative; BCII-Beautiful II; BCIII-Predicta Bulk). Specimens were stored in water at 37 °C for 24 h or 30 days and subjected to microshear bond strength test. The data was analyzed using two-way ANOVA and Tukey's post-hoc test (⍺ < 0.05). The morphological surface of both NI and ID dentin (n = 3) was analyzed using Scanning Electron Microscopy (SEM).
RESULTS: Two-way ANOVA revealed a significant effect of the Time/Radiation (p < 0.001). Restorative material (p = 0.191) and the interaction Time/Radiation*Restorative material (p = 0.169) were not significant. Irradiation decreased the bond strength of CC specimens at both 24 h (p < 0.001) and 30 days (p < 0.001). None of the bioactive materials were significantly affected by irradiation and storage time. The SEM analysis revealed morphological changes in the ID specimens.
CONCLUSION: Ionizing radiation-induced morphological changes in the dentin surface. These changes negatively affected the conventional resin composite bond strengths to dentin. However, these morphological alterations did not affect the bond strength of the bioactive restorative materials.},
}
MeSH Terms:
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Humans
*Dentin/radiation effects/ultrastructure
*Composite Resins/chemistry
*Dental Bonding/methods
Materials Testing
In Vitro Techniques
Shear Strength
Microscopy, Electron, Scanning
Resin Cements/chemistry
Dentin-Bonding Agents/chemistry
Surface Properties
Time Factors
RevDate: 2025-07-12
CmpDate: 2025-07-12
Advances in Neuromodulation and Digital Brain-Spinal Cord Interfaces for Spinal Cord Injury.
International journal of molecular sciences, 26(13):.
Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain-spinal cord interfaces combining brain-computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain-spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally.
Additional Links: PMID-40649800
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@article {pmid40649800,
year = {2025},
author = {Jaszczuk, P and Bratelj, D and Capone, C and Rudnick, M and Pötzel, T and Verma, RK and Fiechter, M},
title = {Advances in Neuromodulation and Digital Brain-Spinal Cord Interfaces for Spinal Cord Injury.},
journal = {International journal of molecular sciences},
volume = {26},
number = {13},
pages = {},
pmid = {40649800},
issn = {1422-0067},
mesh = {Humans ; *Spinal Cord Injuries/therapy/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Spinal Cord/physiopathology ; Animals ; },
abstract = {Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain-spinal cord interfaces combining brain-computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain-spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally.},
}
MeSH Terms:
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Humans
*Spinal Cord Injuries/therapy/rehabilitation/physiopathology
*Brain-Computer Interfaces
*Spinal Cord Stimulation/methods
Spinal Cord/physiopathology
Animals
RevDate: 2025-07-14
Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.
Healthcare (Basel, Switzerland), 13(13):.
BACKGROUND/OBJECTIVES: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains.
METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.
RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.
CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.
Additional Links: PMID-40648603
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@article {pmid40648603,
year = {2025},
author = {Bonanno, M and Saracino, B and Ciancarelli, I and Panza, G and Manuli, A and Morone, G and Calabrò, RS},
title = {Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.},
journal = {Healthcare (Basel, Switzerland)},
volume = {13},
number = {13},
pages = {},
pmid = {40648603},
issn = {2227-9032},
abstract = {BACKGROUND/OBJECTIVES: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains.
METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.
RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.
CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.},
}
RevDate: 2025-07-14
CmpDate: 2025-07-12
Latency and Amplitude of Cortical Activation in Interactive vs. Passive Tasks: An fNIRS Study Using the NefroBall System.
Sensors (Basel, Switzerland), 25(13):.
Functional near-infrared spectroscopy (fNIRS) allows non-invasive assessment of cortical activity during naturalistic tasks. This study aimed to compare cortical activation dynamics-specifically the latency (tmax) and amplitude (ΔoxyHb) of oxygenated haemoglobin changes-in passive observation and an interactive task using the Nefroball system. A total of 117 healthy adults performed two tasks involving rhythmic hand movements: a passive protocol and an interactive game-controlled condition. fNIRS recorded signals from the visual, parietal, motor, and prefrontal cortices of the left hemisphere. The Mann-Whitney test revealed significantly shorter tmax in all areas during the interactive task, suggesting faster recruitment of cortical networks. ΔoxyHb amplitude was significantly higher only in the visual cortex during the interactive task, indicating increased visual processing demand. No significant ΔoxyHb differences were observed in the motor, prefrontal, or parietal cortices. Weak but significant positive correlations were found between tmax and ΔoxyHb in the motor and prefrontal regions, but only in the passive condition. These findings support the notion that interactive tasks elicit faster, though not necessarily stronger, cortical responses. The results have potential implications for designing rehabilitation protocols and brain-computer interfaces involving visual-motor integration.
Additional Links: PMID-40648390
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@article {pmid40648390,
year = {2025},
author = {Jezierska, K and Turoń-Skrzypińska, A and Rotter, I and Syroka, A and Łukowiak, M and Rawojć, K and Rawojć, P and Rył, A},
title = {Latency and Amplitude of Cortical Activation in Interactive vs. Passive Tasks: An fNIRS Study Using the NefroBall System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648390},
issn = {1424-8220},
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Brain Mapping/methods ; Brain-Computer Interfaces ; Movement/physiology ; *Prefrontal Cortex/physiology ; *Spectroscopy, Near-Infrared/methods ; *Visual Cortex/physiology ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) allows non-invasive assessment of cortical activity during naturalistic tasks. This study aimed to compare cortical activation dynamics-specifically the latency (tmax) and amplitude (ΔoxyHb) of oxygenated haemoglobin changes-in passive observation and an interactive task using the Nefroball system. A total of 117 healthy adults performed two tasks involving rhythmic hand movements: a passive protocol and an interactive game-controlled condition. fNIRS recorded signals from the visual, parietal, motor, and prefrontal cortices of the left hemisphere. The Mann-Whitney test revealed significantly shorter tmax in all areas during the interactive task, suggesting faster recruitment of cortical networks. ΔoxyHb amplitude was significantly higher only in the visual cortex during the interactive task, indicating increased visual processing demand. No significant ΔoxyHb differences were observed in the motor, prefrontal, or parietal cortices. Weak but significant positive correlations were found between tmax and ΔoxyHb in the motor and prefrontal regions, but only in the passive condition. These findings support the notion that interactive tasks elicit faster, though not necessarily stronger, cortical responses. The results have potential implications for designing rehabilitation protocols and brain-computer interfaces involving visual-motor integration.},
}
MeSH Terms:
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Adult
Female
Humans
Male
Young Adult
Brain Mapping/methods
Brain-Computer Interfaces
Movement/physiology
*Prefrontal Cortex/physiology
*Spectroscopy, Near-Infrared/methods
*Visual Cortex/physiology
RevDate: 2025-07-12
CmpDate: 2025-07-12
Four-Dimensional Adjustable Electroencephalography Cap for Solid-Gel Electrode.
Sensors (Basel, Switzerland), 25(13):.
Currently, the electroencephalogram (EEG) cap is limited to a finite number of sizes based on head circumference, lacking the mechanical flexibility to accommodate the full range of skull dimensions. This reliance on head circumference data alone often results in a poor fit between the EEG cap and the user's head shape. To address these limitations, we have developed a four-dimensional (4D) adjustable EEG cap. This cap features an adjustable mechanism that covers the entire cranial area in four dimensions, allowing it to fit the head shapes of nearly all adults. The system is compatible with 64 channels or lower electrode counts. We conducted a study with numerous volunteers to compare the performance characteristics of the 4D caps with the commercial (COML) caps in terms of contact pressure, preparation time, wearing impedance, and performance in brain-computer interface (BCI) applications. The 4D cap demonstrated the ability to adapt to various head shapes more quickly, reduce impedance during testing, and enhance measurement accuracy, signal-to-noise ratio (SNR), and comfort. These improvements suggest its potential for broader application in both laboratory settings and daily life.
Additional Links: PMID-40648293
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@article {pmid40648293,
year = {2025},
author = {Zhang, J and Zhao, D and Li, Y and Ming, G and Pei, W},
title = {Four-Dimensional Adjustable Electroencephalography Cap for Solid-Gel Electrode.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648293},
issn = {1424-8220},
support = {62401325//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography/instrumentation/methods ; Humans ; Electrodes ; Adult ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Male ; Head ; Equipment Design ; Female ; },
abstract = {Currently, the electroencephalogram (EEG) cap is limited to a finite number of sizes based on head circumference, lacking the mechanical flexibility to accommodate the full range of skull dimensions. This reliance on head circumference data alone often results in a poor fit between the EEG cap and the user's head shape. To address these limitations, we have developed a four-dimensional (4D) adjustable EEG cap. This cap features an adjustable mechanism that covers the entire cranial area in four dimensions, allowing it to fit the head shapes of nearly all adults. The system is compatible with 64 channels or lower electrode counts. We conducted a study with numerous volunteers to compare the performance characteristics of the 4D caps with the commercial (COML) caps in terms of contact pressure, preparation time, wearing impedance, and performance in brain-computer interface (BCI) applications. The 4D cap demonstrated the ability to adapt to various head shapes more quickly, reduce impedance during testing, and enhance measurement accuracy, signal-to-noise ratio (SNR), and comfort. These improvements suggest its potential for broader application in both laboratory settings and daily life.},
}
MeSH Terms:
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*Electroencephalography/instrumentation/methods
Humans
Electrodes
Adult
Signal-To-Noise Ratio
Brain-Computer Interfaces
Male
Head
Equipment Design
Female
RevDate: 2025-07-12
CmpDate: 2025-07-12
Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.
Sensors (Basel, Switzerland), 25(13): pii:s25133987.
Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain-computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models-from early rule-based systems to contemporary deep learning architectures-and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users.
Additional Links: PMID-40648241
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@article {pmid40648241,
year = {2025},
author = {Carìa, A},
title = {Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
doi = {10.3390/s25133987},
pmid = {40648241},
issn = {1424-8220},
support = {no number available//5xMille Unitn/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Language ; Artificial Intelligence ; *Communication ; Brain/physiology ; Deep Learning ; Electroencephalography ; Large Language Models ; },
abstract = {Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain-computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models-from early rule-based systems to contemporary deep learning architectures-and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users.},
}
MeSH Terms:
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*Brain-Computer Interfaces
Humans
*Language
Artificial Intelligence
*Communication
Brain/physiology
Deep Learning
Electroencephalography
Large Language Models
RevDate: 2025-07-12
CmpDate: 2025-07-12
Narrowband Theta Investigations for Detecting Cognitive Mental Load.
Sensors (Basel, Switzerland), 25(13): pii:s25133902.
The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function-specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified.
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@article {pmid40648159,
year = {2025},
author = {Ionita, S and Coman, DA},
title = {Narrowband Theta Investigations for Detecting Cognitive Mental Load.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
doi = {10.3390/s25133902},
pmid = {40648159},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Theta Rhythm/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; },
abstract = {The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function-specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified.},
}
MeSH Terms:
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Humans
*Electroencephalography/methods
*Cognition/physiology
*Theta Rhythm/physiology
Algorithms
Signal Processing, Computer-Assisted
Male
Adult
Female
RevDate: 2025-07-12
Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region.
Animals : an open access journal from MDPI, 15(13): pii:ani15131851.
Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.
Additional Links: PMID-40646750
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@article {pmid40646750,
year = {2025},
author = {Li, S and Tang, Z and Li, M and Yang, L and Shang, Z},
title = {Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region.},
journal = {Animals : an open access journal from MDPI},
volume = {15},
number = {13},
pages = {},
doi = {10.3390/ani15131851},
pmid = {40646750},
issn = {2076-2615},
support = {62301496//the National Natural Science Foundation of China/ ; GZC20232447//the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; 252102311095//the Key Scientific and Technological Projects of Henan Province/ ; 252102210008//the Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.},
}
RevDate: 2025-07-11
Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.
Journal of neural engineering [Epub ahead of print].
OBJECTIVE: The flexibility of cognitive resource allocation is deteriorated due to aging and neurological degenerative diseases, such as Parkinson's disease (PD). Dual task performance reflects a subject's ability to allocate cognitive resources, and the investigation of cortical activation changes during dual tasking can provide a deep insight into the reallocation of neural resources. However, the cortical changes induced by increased cognitive task difficulty during dual tasking with changes in behavioral outcomes have not been explored in PD and older adults.
APPROACH: We designed a novel dual task paradigm comprising of balance maintenance and visual working memory (VWM) task to assess cognitive-motor interaction. Nineteen early-stage PD, 13 age-matched older adults (OA) and 15 young adults (YA) completed 4 blocks of 25 trials each for two VWM difficulty levels (2 squares and 4 squares). Behavioral performance, postural stability, and 32-channel EEG were recorded. One-way ANOVA was used to examine group and task effects while Spearman's correlation analysis assessed associations between EEG changes and behavioral performance.
MAIN RESULTS: Both PD and OA groups exhibited significantly longer reaction time, reduced postural stability, prolonged P300 latency and less alpha event related desynchronization (ERD) enhancement in response to the increased VWM task difficulty. Moreover, PD patients demonstrated significantly alpha ERD reduction at FC3, C3 and P4 in the 500-700ms compared to the OAs. The ERD changes at the central and parietal regions were found to be significantly related to postural stability and clinical scores, respectively.
SIGNIFICANCE: The results provide novel evidence that cortical EEG responses during dual tasking may reflect deficits in attention resource reallocation and reduced cognitive flexibility in PD and OA groups. These observed cortical changes with increasing cognitive task difficulty are correlated with postural instability, highlighting their potential as neurophysiological biomarkers for dual-task dysfunction.
Additional Links: PMID-40645213
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@article {pmid40645213,
year = {2025},
author = {Meng, L and Zhao, H and Dong, M and Wang, Q and Shi, Y and Wang, D and Zhu, X and Xu, R and Ming, D},
title = {Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeeca},
pmid = {40645213},
issn = {1741-2552},
abstract = {OBJECTIVE: The flexibility of cognitive resource allocation is deteriorated due to aging and neurological degenerative diseases, such as Parkinson's disease (PD). Dual task performance reflects a subject's ability to allocate cognitive resources, and the investigation of cortical activation changes during dual tasking can provide a deep insight into the reallocation of neural resources. However, the cortical changes induced by increased cognitive task difficulty during dual tasking with changes in behavioral outcomes have not been explored in PD and older adults.
APPROACH: We designed a novel dual task paradigm comprising of balance maintenance and visual working memory (VWM) task to assess cognitive-motor interaction. Nineteen early-stage PD, 13 age-matched older adults (OA) and 15 young adults (YA) completed 4 blocks of 25 trials each for two VWM difficulty levels (2 squares and 4 squares). Behavioral performance, postural stability, and 32-channel EEG were recorded. One-way ANOVA was used to examine group and task effects while Spearman's correlation analysis assessed associations between EEG changes and behavioral performance.
MAIN RESULTS: Both PD and OA groups exhibited significantly longer reaction time, reduced postural stability, prolonged P300 latency and less alpha event related desynchronization (ERD) enhancement in response to the increased VWM task difficulty. Moreover, PD patients demonstrated significantly alpha ERD reduction at FC3, C3 and P4 in the 500-700ms compared to the OAs. The ERD changes at the central and parietal regions were found to be significantly related to postural stability and clinical scores, respectively.
SIGNIFICANCE: The results provide novel evidence that cortical EEG responses during dual tasking may reflect deficits in attention resource reallocation and reduced cognitive flexibility in PD and OA groups. These observed cortical changes with increasing cognitive task difficulty are correlated with postural instability, highlighting their potential as neurophysiological biomarkers for dual-task dysfunction.},
}
RevDate: 2025-07-11
EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.
Journal of neural engineering [Epub ahead of print].
OBJECT: Speech imagery is a nascent paradigm that is receiving widespread attention in current Brain-Computer Interface (BCI) research. By collecting the electroencephalogram (EEG) data generated when imagining the pronunciation of a sentence or word in human mind, machine learning methods are used to decode the intention that the subject wants to express. Among existing decoding methods, graph is often used as an effective tool to model the data structure; however, in the field of BCI research, the correlations between EEG samples may not be fully characterized by simple pairwise relationships. Therefore, this paper attempts to employ a more effective data structure to model EEG data.
APPROACH: In this paper, we introduce hypergraph to describe the high-order correlations between samples by viewing feature vectors extracted from each sample as vertices and then connecting them through hyperedges. We also dynamically update the weights of hyperedges, the weights of vertices and the structure of the hypergraph in two transformed subspaces, i.e., projected and feature-weighted subspaces. Accordingly, two dynamic hypergraph learning models, i.e., dynamic hypergraph semi-supervised learning within projected subspace (DHSLP) and dynamic hypergraph semi-supervised learning within selected feature subspace (DHSLF), are proposed for speech imagery decoding.
MAIN RESULTS: To validate the proposed models, we performed a series of experiments on two EEG datasets. The obtained results demonstrated that both DHSLP and DHSLF have statistically significant improvements in decoding imagined speech intentions to existing studies. Specifically, DHSLP achieved accuracies of 78.40% and 66.64% on the two datasets, while DHSLF achieved accuracies of 71.07% and 63.94%.
SIGNIFICANCE: Our study indicates the effectiveness of the learned hypergraphs in characterizing the underlying semantic information of imagined contents; besides, interpretable results on quantitatively exploring the discriminative EEG channels in speech imagery decoding are obtained, which lay the foundation for further exploration of the physiological mechanisms during speech imagery.
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@article {pmid40645212,
year = {2025},
author = {Li, Y and Zhao, Z and Liu, J and Peng, Y and Camilleri, KP and Kong, W and Cichocki, A},
title = {EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeec8},
pmid = {40645212},
issn = {1741-2552},
abstract = {OBJECT: Speech imagery is a nascent paradigm that is receiving widespread attention in current Brain-Computer Interface (BCI) research. By collecting the electroencephalogram (EEG) data generated when imagining the pronunciation of a sentence or word in human mind, machine learning methods are used to decode the intention that the subject wants to express. Among existing decoding methods, graph is often used as an effective tool to model the data structure; however, in the field of BCI research, the correlations between EEG samples may not be fully characterized by simple pairwise relationships. Therefore, this paper attempts to employ a more effective data structure to model EEG data.
APPROACH: In this paper, we introduce hypergraph to describe the high-order correlations between samples by viewing feature vectors extracted from each sample as vertices and then connecting them through hyperedges. We also dynamically update the weights of hyperedges, the weights of vertices and the structure of the hypergraph in two transformed subspaces, i.e., projected and feature-weighted subspaces. Accordingly, two dynamic hypergraph learning models, i.e., dynamic hypergraph semi-supervised learning within projected subspace (DHSLP) and dynamic hypergraph semi-supervised learning within selected feature subspace (DHSLF), are proposed for speech imagery decoding.
MAIN RESULTS: To validate the proposed models, we performed a series of experiments on two EEG datasets. The obtained results demonstrated that both DHSLP and DHSLF have statistically significant improvements in decoding imagined speech intentions to existing studies. Specifically, DHSLP achieved accuracies of 78.40% and 66.64% on the two datasets, while DHSLF achieved accuracies of 71.07% and 63.94%.
SIGNIFICANCE: Our study indicates the effectiveness of the learned hypergraphs in characterizing the underlying semantic information of imagined contents; besides, interpretable results on quantitatively exploring the discriminative EEG channels in speech imagery decoding are obtained, which lay the foundation for further exploration of the physiological mechanisms during speech imagery.},
}
RevDate: 2025-07-11
CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.
Neural networks : the official journal of the International Neural Network Society, 191:107795 pii:S0893-6080(25)00675-6 [Epub ahead of print].
The synergy between amplitude and phase in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provides comprehensive and essential insights into neural oscillatory processes. However, constrained by real-valued computation paradigms, most deep learning methods have to process amplitude and phase independently, neglecting their crucial interaction mechanisms. To address this issue, we construct a Complex-valued Graph Network (CGNet) to capture comprehensive information from EEG signals, where both amplitude and phase information are encoded into the complex-valued representation. Specifically, we design a two-scale complex-valued convolutional network to learn local spatio-temporal information, develop a spatial attention module to enhance spatial information learning, and formulate a dynamic graph convolution to capture global temporal dependencies. Furthermore, we extend CGNet to Filter-Band CGNet (FBCGNet), enhancing the model's adaptability to broadband EEG data. Applied to motor imagery and execution BCI tasks, CGNet achieves state-of-the-art classification performance while maintaining computational efficiency comparable to other advanced algorithms. Notably, FBCGNet further improves CGNet's performance. Visualization results show that CGNet can identify the key spatio-temporal information consistent with paradigm principles. In addition, compared with using amplitude or phase alone, CGNet can capture more comprehensive task-related neural activities, thereby showing higher classification performance. CGNet is a promising tool for mining amplitude-phase information and offering more comprehensive neural decoding in EEG-based BCIs.
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@article {pmid40644990,
year = {2025},
author = {Cai, G and Chen, Y and Yang, B and Yang, Y and Ma, T and Wang, Y},
title = {CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107795},
doi = {10.1016/j.neunet.2025.107795},
pmid = {40644990},
issn = {1879-2782},
abstract = {The synergy between amplitude and phase in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provides comprehensive and essential insights into neural oscillatory processes. However, constrained by real-valued computation paradigms, most deep learning methods have to process amplitude and phase independently, neglecting their crucial interaction mechanisms. To address this issue, we construct a Complex-valued Graph Network (CGNet) to capture comprehensive information from EEG signals, where both amplitude and phase information are encoded into the complex-valued representation. Specifically, we design a two-scale complex-valued convolutional network to learn local spatio-temporal information, develop a spatial attention module to enhance spatial information learning, and formulate a dynamic graph convolution to capture global temporal dependencies. Furthermore, we extend CGNet to Filter-Band CGNet (FBCGNet), enhancing the model's adaptability to broadband EEG data. Applied to motor imagery and execution BCI tasks, CGNet achieves state-of-the-art classification performance while maintaining computational efficiency comparable to other advanced algorithms. Notably, FBCGNet further improves CGNet's performance. Visualization results show that CGNet can identify the key spatio-temporal information consistent with paradigm principles. In addition, compared with using amplitude or phase alone, CGNet can capture more comprehensive task-related neural activities, thereby showing higher classification performance. CGNet is a promising tool for mining amplitude-phase information and offering more comprehensive neural decoding in EEG-based BCIs.},
}
RevDate: 2025-07-11
Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.
Computers in biology and medicine, 196(Pt A):110713 pii:S0010-4825(25)01064-9 [Epub ahead of print].
A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly important for developing advanced applications that enhance brain machine interaction and improve brain health assessment systems. However, EEG signal analysis faces significant challenges due to their subject-specific nature, high noise levels, and the scarcity of high-quality labeled data, which collectively limit model generalizability and complicate signal analysis. Traditional approaches have employed handcrafted features with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF) for EEG feature extraction and classification. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable automatic feature learning from raw data to extract temporal, spatial, and spectral properties. The study employs a literature review approach and the analysis of the popular datasets (e.g., DEAP, SEED, AMIGOS). Despite technological advances, the fundamental challenges of noisy subject variability, and limited labeled data persist, requiring future research to focus on improving model robustness, scalability, and interpretability while addressing current limitations.
Additional Links: PMID-40644885
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@article {pmid40644885,
year = {2025},
author = {Al-Hadithy, SS and Abdalkafor, AS and Al-Khateeb, B},
title = {Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110713},
doi = {10.1016/j.compbiomed.2025.110713},
pmid = {40644885},
issn = {1879-0534},
abstract = {A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly important for developing advanced applications that enhance brain machine interaction and improve brain health assessment systems. However, EEG signal analysis faces significant challenges due to their subject-specific nature, high noise levels, and the scarcity of high-quality labeled data, which collectively limit model generalizability and complicate signal analysis. Traditional approaches have employed handcrafted features with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF) for EEG feature extraction and classification. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable automatic feature learning from raw data to extract temporal, spatial, and spectral properties. The study employs a literature review approach and the analysis of the popular datasets (e.g., DEAP, SEED, AMIGOS). Despite technological advances, the fundamental challenges of noisy subject variability, and limited labeled data persist, requiring future research to focus on improving model robustness, scalability, and interpretability while addressing current limitations.},
}
RevDate: 2025-07-11
CmpDate: 2025-07-11
Neural Network Sparsity in Brain-Body-Machine Interfaces.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:1-8.
Brain-body-machine interfaces acquire, process, and translate brain signals for individuals with severe motor impairments to communicate and control the assistive technology that supports their daily life activities. Electroencephalography (EEG) is a standard approach for acquiring such brain signals due to its low cost and high temporal resolution. EEG signals can be thought of as a proxy for the user's intent. One established method for translating this intent into inferences and actions are neural networks. However, densely connected neural networks can be computationally expensive-a problem for real-time, deployed brain-body-machine interface systems. In this paper we investigate the use of sparsity in neural networks for EEG-based motor classification, with the goal of reducing the number of neuronal connections without sacrificing a system's performance. We compare two sparsity-inducing algorithms, weight pruning and sparse evolutionary training, with a dense neural network under three experimental conditions. Overall, our results show that sparse neural networks can achieve higher performance accuracy and generalization than their densely-connected counterparts for an EEG-based classification task. We found that sparse evolutionary training achieves the highest and most stable performance across all experiments. Introducing sparsity into the network is a viable option for efficient EEG-based control, with promising applications in a range of related rehabilitation and assistive technologies. This brings us closer to helping individuals with severe motor impairments reclaim independence through more computationally realizable methods of interacting with their technology and the world around them.
Additional Links: PMID-40644284
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@article {pmid40644284,
year = {2025},
author = {Petrich, LC and Neumann, S and Pilarski, PM and Fyshe, A},
title = {Neural Network Sparsity in Brain-Body-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1-8},
doi = {10.1109/ICORR66766.2025.11062950},
pmid = {40644284},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-body-machine interfaces acquire, process, and translate brain signals for individuals with severe motor impairments to communicate and control the assistive technology that supports their daily life activities. Electroencephalography (EEG) is a standard approach for acquiring such brain signals due to its low cost and high temporal resolution. EEG signals can be thought of as a proxy for the user's intent. One established method for translating this intent into inferences and actions are neural networks. However, densely connected neural networks can be computationally expensive-a problem for real-time, deployed brain-body-machine interface systems. In this paper we investigate the use of sparsity in neural networks for EEG-based motor classification, with the goal of reducing the number of neuronal connections without sacrificing a system's performance. We compare two sparsity-inducing algorithms, weight pruning and sparse evolutionary training, with a dense neural network under three experimental conditions. Overall, our results show that sparse neural networks can achieve higher performance accuracy and generalization than their densely-connected counterparts for an EEG-based classification task. We found that sparse evolutionary training achieves the highest and most stable performance across all experiments. Introducing sparsity into the network is a viable option for efficient EEG-based control, with promising applications in a range of related rehabilitation and assistive technologies. This brings us closer to helping individuals with severe motor impairments reclaim independence through more computationally realizable methods of interacting with their technology and the world around them.},
}
MeSH Terms:
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*Brain-Computer Interfaces
Humans
Electroencephalography/methods
*Neural Networks, Computer
Algorithms
*Signal Processing, Computer-Assisted
RevDate: 2025-07-11
CmpDate: 2025-07-11
Handling Kinematic Features in an Action Observation Task to Optimize a Brain Computer Interface-Based Rehabilitation Training.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:1078-1082.
Brain-Computer Interface (BCI) technology promotes neuroplasticity mechanisms which favor the functional motor recovery in stroke survivors. Patients' residual motor abilities determine the intention, which, once detected by the BCI is fed back via an effector. The majority of studies aimed at optimizing the feedback branch, but not enough attention has been posed to supporting patient in the movement intention that should be detected by the BCI system. The inclusion of a visual motor priming (observed action before a task) in a BCI could promote the retrieval of movements from the patient's own impaired motor repertoire. None of the motor priming proposed until so far have been tailored to the patients' residual motor ability, although it is well known that the human brain recognizes movements closer from a kinematic perspective to its own repertoire more easily. The aim of this study was to investigate how individual motor style in an action observation task would affect the observer's cortical excitability. EEG signals were recorded from 10 individuals during an action observation task where different levels of motor distance between the observer and the agent were modulated. EEG-based group spectral activations shown an involvement of bilateral parietal areas in beta band in case of more unpredictable kinematics. The results would open the way for the design of a kinematic-based visual motor priming to be embedded in a BCI system for post-stroke rehabilitation.
Additional Links: PMID-40644274
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@article {pmid40644274,
year = {2025},
author = {Patarini, F and Maronati, C and Manuello, J and Cuturi, LF and Monti, M and Savina, G and Ferrari, E and Iarrobino, I and Iani, C and Rubichi, S and Ciaramidaro, A and Astolfi, L and Cavallo, A and Toppi, J},
title = {Handling Kinematic Features in an Action Observation Task to Optimize a Brain Computer Interface-Based Rehabilitation Training.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1078-1082},
doi = {10.1109/ICORR66766.2025.11062958},
pmid = {40644274},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Biomechanical Phenomena/physiology ; Electroencephalography ; *Stroke Rehabilitation ; Male ; Female ; Adult ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) technology promotes neuroplasticity mechanisms which favor the functional motor recovery in stroke survivors. Patients' residual motor abilities determine the intention, which, once detected by the BCI is fed back via an effector. The majority of studies aimed at optimizing the feedback branch, but not enough attention has been posed to supporting patient in the movement intention that should be detected by the BCI system. The inclusion of a visual motor priming (observed action before a task) in a BCI could promote the retrieval of movements from the patient's own impaired motor repertoire. None of the motor priming proposed until so far have been tailored to the patients' residual motor ability, although it is well known that the human brain recognizes movements closer from a kinematic perspective to its own repertoire more easily. The aim of this study was to investigate how individual motor style in an action observation task would affect the observer's cortical excitability. EEG signals were recorded from 10 individuals during an action observation task where different levels of motor distance between the observer and the agent were modulated. EEG-based group spectral activations shown an involvement of bilateral parietal areas in beta band in case of more unpredictable kinematics. The results would open the way for the design of a kinematic-based visual motor priming to be embedded in a BCI system for post-stroke rehabilitation.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Brain-Computer Interfaces
Biomechanical Phenomena/physiology
Electroencephalography
*Stroke Rehabilitation
Male
Female
Adult
Middle Aged
RevDate: 2025-07-11
CmpDate: 2025-07-11
Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:76-81.
Lower-limb rehabilitation traditionally relies on physical therapy, but motor imagery(MI)-based brain- computer interfaces (BCIs) promise to facilitate neuroplasticity and adaptation by closing the perception-action cycle. Here, we present a BCI system based on kinesthetic MI that enables treadmill velocity control, establishing a closed-loop feedback mechanism. The system was tested in a healthy participant translating mu (8-12 Hz) and high-beta (18-24 Hz) rhythm modulation into treadmill velocity control commands. Feature extraction techniques, including power spectral density (PSD) and Riemannian geometry (RG), were used for MI- and resting state classification. Additionally, Logistic Regression (LR), k-nearest neighbors, support vector machine, and Linear Discriminant Analysis (LDA) were employed and optimized for accuracy. The results showed increased mu and highbeta activation modulation at the vertex. The online RG+LDA classifier achieving an average accuracy of 72%, while the pseudo-online RG+LR reached 95%. The study's novelty lies in combining kinesthetic MI with treadmill control and employing RG for feature extraction, demonstrating its potential to enhance cortical modulation during rehabilitation. Future work will have to validate the system in poststroke patients for clinical applicability.
Additional Links: PMID-40644240
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@article {pmid40644240,
year = {2025},
author = {Gonzalez-Cely, AX and Soekadar, SR and Delisle-Rodriguez, D and Bastos-Filho, T},
title = {Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {76-81},
doi = {10.1109/ICORR66766.2025.11063181},
pmid = {40644240},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Male ; Adult ; Electroencephalography ; *Exercise Test ; *Imagination/physiology ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Lower-limb rehabilitation traditionally relies on physical therapy, but motor imagery(MI)-based brain- computer interfaces (BCIs) promise to facilitate neuroplasticity and adaptation by closing the perception-action cycle. Here, we present a BCI system based on kinesthetic MI that enables treadmill velocity control, establishing a closed-loop feedback mechanism. The system was tested in a healthy participant translating mu (8-12 Hz) and high-beta (18-24 Hz) rhythm modulation into treadmill velocity control commands. Feature extraction techniques, including power spectral density (PSD) and Riemannian geometry (RG), were used for MI- and resting state classification. Additionally, Logistic Regression (LR), k-nearest neighbors, support vector machine, and Linear Discriminant Analysis (LDA) were employed and optimized for accuracy. The results showed increased mu and highbeta activation modulation at the vertex. The online RG+LDA classifier achieving an average accuracy of 72%, while the pseudo-online RG+LR reached 95%. The study's novelty lies in combining kinesthetic MI with treadmill control and employing RG for feature extraction, demonstrating its potential to enhance cortical modulation during rehabilitation. Future work will have to validate the system in poststroke patients for clinical applicability.},
}
MeSH Terms:
show MeSH Terms
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Humans
*Brain-Computer Interfaces
*Lower Extremity/physiology
Male
Adult
Electroencephalography
*Exercise Test
*Imagination/physiology
Female
Signal Processing, Computer-Assisted
RevDate: 2025-07-11
CmpDate: 2025-07-11
Optimising Continuous Control of Real-Time Brain-Computer Interfaces Through Trial Length and Feedback Update Interval Selection.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:284-288.
Brain-computer interfaces (BCIs) offer promising potential to aid neurorehabilitation by transforming motor imagery (MI) signals into control commands, bypassing damaged neural pathways to support motor recovery. However, a key challenge in BCI research is achieving an effective balance between classification accuracy and real-time responsiveness, as both are critical for enhancing user embodiment and control for neurorehabilitation outcomes. This study investigates the impact of trial length and feedback update interval (FUI) on classification accuracy in an MI-based BCI system. Using EEG data from five subjects across 50 sessions, we evaluated classification performance across various trial length (1-5 seconds) and FUI (0.2-1 second) configurations. Results revealed that both trial length and FUI significantly influenced classification accuracy, with longer trial length (4-5 seconds) and FUI (0.4-1 seconds) yielding the highest accuracy. However, post-hoc analyses indicated a saturation effect, with no significant differences in the accuracy for these parameters. These findings underscore the importance of balancing signal stability with responsiveness for optimal BCI performance, providing insights into parameter settings that can enhance BCI usability in neurorehabilitation. Future work may explore adaptive approaches to dynamically adjust these parameters based on real-time requirements, potentially offering a more responsive and efficient BCI for clinical rehabilitation.
Additional Links: PMID-40644193
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PubMed:
Citation:
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@article {pmid40644193,
year = {2025},
author = {Mannan, MMN and Lloyd, DG and Pizzolato, C},
title = {Optimising Continuous Control of Real-Time Brain-Computer Interfaces Through Trial Length and Feedback Update Interval Selection.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {284-288},
doi = {10.1109/ICORR66766.2025.11063010},
pmid = {40644193},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) offer promising potential to aid neurorehabilitation by transforming motor imagery (MI) signals into control commands, bypassing damaged neural pathways to support motor recovery. However, a key challenge in BCI research is achieving an effective balance between classification accuracy and real-time responsiveness, as both are critical for enhancing user embodiment and control for neurorehabilitation outcomes. This study investigates the impact of trial length and feedback update interval (FUI) on classification accuracy in an MI-based BCI system. Using EEG data from five subjects across 50 sessions, we evaluated classification performance across various trial length (1-5 seconds) and FUI (0.2-1 second) configurations. Results revealed that both trial length and FUI significantly influenced classification accuracy, with longer trial length (4-5 seconds) and FUI (0.4-1 seconds) yielding the highest accuracy. However, post-hoc analyses indicated a saturation effect, with no significant differences in the accuracy for these parameters. These findings underscore the importance of balancing signal stability with responsiveness for optimal BCI performance, providing insights into parameter settings that can enhance BCI usability in neurorehabilitation. Future work may explore adaptive approaches to dynamically adjust these parameters based on real-time requirements, potentially offering a more responsive and efficient BCI for clinical rehabilitation.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Brain-Computer Interfaces
Electroencephalography
Male
Adult
Female
Signal Processing, Computer-Assisted
Young Adult
RevDate: 2025-07-11
CmpDate: 2025-07-11
Exploring Cortical Responses to Blood Flow Restriction through Deep Learning.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:546-552.
Blood flow restriction (BFR) training, which combines low-intensity resistance exercises with restricted blood flow, is effective in promoting muscle hypertrophy and strength. However, its impact on cortical activity remains largely unexplored, presenting an opportunity to investigate neural mechanisms using brain-computer interfaces (BCIs). Deep learning (DL)-based BCIs, with their large capacity for decoding complex brain signals, offer a promising avenue for such exploration. This study utilized magnetoencephalography (MEG) to analyze cortical responses in six subjects across three conditions-before, during, and after BFR. After preprocessing steps, such as data standardization and Euclidean-space alignment to optimize performance, the BaseNet architecture was utilized to classify the data. The models were tested using within-subject, cross-subject, and cross-time data splits. The results revealed classification accuracy well above 90% for individual subjects, indicating that cortical responses to BFR are detectable on a personal level. However, cross-subject models achieved only chance-level accuracy (33%), highlighting significant variability between individuals. Cross-time models showed better performance, with accuracy exceeding 50%. These findings suggest that while BFR elicits distinct cortical activity patterns, these responses are highly individualized, presenting challenges for generalization.
Additional Links: PMID-40644184
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PubMed:
Citation:
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@article {pmid40644184,
year = {2025},
author = {Koellner, J and Wimpff, M and Gizzi, L and Vujaklija, I and Yang, B},
title = {Exploring Cortical Responses to Blood Flow Restriction through Deep Learning.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {546-552},
doi = {10.1109/ICORR66766.2025.11063023},
pmid = {40644184},
issn = {1945-7901},
mesh = {Humans ; *Deep Learning ; Magnetoencephalography ; Male ; Adult ; Female ; Brain-Computer Interfaces ; *Cerebral Cortex/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Resistance Training ; },
abstract = {Blood flow restriction (BFR) training, which combines low-intensity resistance exercises with restricted blood flow, is effective in promoting muscle hypertrophy and strength. However, its impact on cortical activity remains largely unexplored, presenting an opportunity to investigate neural mechanisms using brain-computer interfaces (BCIs). Deep learning (DL)-based BCIs, with their large capacity for decoding complex brain signals, offer a promising avenue for such exploration. This study utilized magnetoencephalography (MEG) to analyze cortical responses in six subjects across three conditions-before, during, and after BFR. After preprocessing steps, such as data standardization and Euclidean-space alignment to optimize performance, the BaseNet architecture was utilized to classify the data. The models were tested using within-subject, cross-subject, and cross-time data splits. The results revealed classification accuracy well above 90% for individual subjects, indicating that cortical responses to BFR are detectable on a personal level. However, cross-subject models achieved only chance-level accuracy (33%), highlighting significant variability between individuals. Cross-time models showed better performance, with accuracy exceeding 50%. These findings suggest that while BFR elicits distinct cortical activity patterns, these responses are highly individualized, presenting challenges for generalization.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Deep Learning
Magnetoencephalography
Male
Adult
Female
Brain-Computer Interfaces
*Cerebral Cortex/physiology
Young Adult
Signal Processing, Computer-Assisted
Resistance Training
RevDate: 2025-07-11
CmpDate: 2025-07-11
Hybrid Brain Computer Interface-Based Rehabilitation Addressing Post-Stroke Maladaptive Movement Patterns.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:431-436.
Hybrid Brain-Computer Interfaces (hBCI) integrate brain and muscle signals to enhance motor rehabilitation of stroke survivors, by closing the loop between the lesioned brain and the paretic limb. To date, little attention has been devoted to their potential efficacy in managing the maladaptive movement patterns that afflict post-stroke motor outcome (unwanted abnormal co-contrations, spasticity). This study proposes a comparison of Cortico-Muscular Coherence (CMC) patterns assessed in stroke patients before and after a 1-month rehabilitation intervention based on a hBCI-controlled Functional Electrical Stimulation (FES) treatment, which included a module to monitor non-physiological movement patterns. Results demonstrated the efficacy of this type of assistive technology for post-stroke rehabilitation, addressing patient-tailored interventions able to reduce the maladaptive mechanisms.
Additional Links: PMID-40644160
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PubMed:
Citation:
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@article {pmid40644160,
year = {2025},
author = {Toppi, J and Savina, G and Colamarino, E and De Seta, V and Patarini, F and Cincotti, F and Pichiorri, F and Mattia, D},
title = {Hybrid Brain Computer Interface-Based Rehabilitation Addressing Post-Stroke Maladaptive Movement Patterns.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {431-436},
doi = {10.1109/ICORR66766.2025.11062988},
pmid = {40644160},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Middle Aged ; Female ; Aged ; Movement/physiology ; Adult ; Stroke/physiopathology ; Electromyography ; },
abstract = {Hybrid Brain-Computer Interfaces (hBCI) integrate brain and muscle signals to enhance motor rehabilitation of stroke survivors, by closing the loop between the lesioned brain and the paretic limb. To date, little attention has been devoted to their potential efficacy in managing the maladaptive movement patterns that afflict post-stroke motor outcome (unwanted abnormal co-contrations, spasticity). This study proposes a comparison of Cortico-Muscular Coherence (CMC) patterns assessed in stroke patients before and after a 1-month rehabilitation intervention based on a hBCI-controlled Functional Electrical Stimulation (FES) treatment, which included a module to monitor non-physiological movement patterns. Results demonstrated the efficacy of this type of assistive technology for post-stroke rehabilitation, addressing patient-tailored interventions able to reduce the maladaptive mechanisms.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
Male
Middle Aged
Female
Aged
Movement/physiology
Adult
Stroke/physiopathology
Electromyography
RevDate: 2025-07-11
CmpDate: 2025-07-11
Rehabilitation of Chronic Stroke Using Neurofeedback, Functional Electrical Stimulation and Cerebrospinal Direct Current Stimulation.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:1203-1208.
This work presents the application of a rehabilitation protocol using a novel Non-Invasive Brain Stimulation (NIBS) technique, called cerebrospinal Direct Current Stimulation (csDCS), together with the use of a Brain-Computer Interface (BCI) based on Motor Imagery (MI) with Neurofeedback (NFB), and applying Functional Electrical Stimulation (FES) plus the use of a pedal exerciser. This protocol uses the concept of Alternating Treatment Design (ATD), in which a chronic post-stroke subject is submitted to these techniques to recover his left hand and leg movements. The rehabilitation progress was verified through metrics, such as Fugl Meyer Assessment (FMA), Functional Independence Measure (FIM), Ashworth Scale, Muscle Strength Grading (MSG), and surface Electromyography (sEMG). Results from these metrics include a 41% gain in hand function recovery, a 5% gain in performance in motor and cognitive/social domains, and a 50% improvement in both wrist extensor muscle strength and finger extensor muscle strength. In addition, there was a 17% gain of Maximum Voluntary Contraction (MVC) for the tibialis anterior muscle of the patient's left leg. On the other hand, there was a worsening in some values of EMG, probably due to the participant having received application of botulinum toxin in his hand.
Additional Links: PMID-40644144
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PubMed:
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@article {pmid40644144,
year = {2025},
author = {Bastos-Filho, T and Gonzalez-Cely, AX and Mehrpour, S and Souza, F and Villa-Parra, AC and Cabral, F},
title = {Rehabilitation of Chronic Stroke Using Neurofeedback, Functional Electrical Stimulation and Cerebrospinal Direct Current Stimulation.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1203-1208},
doi = {10.1109/ICORR66766.2025.11063073},
pmid = {40644144},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Neurofeedback/methods ; Male ; Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; Chronic Disease ; Electromyography ; Middle Aged ; Stroke/physiopathology ; },
abstract = {This work presents the application of a rehabilitation protocol using a novel Non-Invasive Brain Stimulation (NIBS) technique, called cerebrospinal Direct Current Stimulation (csDCS), together with the use of a Brain-Computer Interface (BCI) based on Motor Imagery (MI) with Neurofeedback (NFB), and applying Functional Electrical Stimulation (FES) plus the use of a pedal exerciser. This protocol uses the concept of Alternating Treatment Design (ATD), in which a chronic post-stroke subject is submitted to these techniques to recover his left hand and leg movements. The rehabilitation progress was verified through metrics, such as Fugl Meyer Assessment (FMA), Functional Independence Measure (FIM), Ashworth Scale, Muscle Strength Grading (MSG), and surface Electromyography (sEMG). Results from these metrics include a 41% gain in hand function recovery, a 5% gain in performance in motor and cognitive/social domains, and a 50% improvement in both wrist extensor muscle strength and finger extensor muscle strength. In addition, there was a 17% gain of Maximum Voluntary Contraction (MVC) for the tibialis anterior muscle of the patient's left leg. On the other hand, there was a worsening in some values of EMG, probably due to the participant having received application of botulinum toxin in his hand.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Stroke Rehabilitation/methods
*Neurofeedback/methods
Male
Brain-Computer Interfaces
*Electric Stimulation Therapy/methods
Chronic Disease
Electromyography
Middle Aged
Stroke/physiopathology
RevDate: 2025-07-11
CmpDate: 2025-07-11
On the Impact of Proprioception in EEG Representations and Decoding During Human-Hand Exoskeleton Interaction.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:186-192.
Controlling a hand exoskeleton based on electroencephalogram (EEG)-based brain-computer interfacing (BCI) holds promise for human motor augmentation and neurore-habilitation. To achieve natural control, a critical step is to understand the impact of proprioception provided by the exoskeleton during interaction. In this study, we aim to approach the goal by quantifying EEG representations and BCI performance. We monitored 25 healthy subjects' full-scalp EEG while performing different finger movement tasks with a cable-driven hand exoskeleton. Each task involves three movement modalities, i.e., imagined (IM), passive (PM), and congruent imagined and passive (IPM) finger flexion. We found that alpha (8 - 13 Hz) and beta (13 - 30 Hz) band desynchronization in the sensorimotor area was significantly stronger for PM and IPM tasks compared to IM, with no significant difference between PM and IPM. Using machine learning models, we achieved a high accuracy in classifying exoskeleton-assisted movements from the rest condition (IPM vs. REST: 0.80 ± 0.07, PM vs. REST: 0.72 ± 0.10), with the IPM modality returning the highest accuracy. However, distinguishing between IPM and PM yielded only 0.61 ± 0.09, significantly lower than the condition of intention detection without the exoskeleton (IM vs. REST: 0.73 ± 0.08). Our findings suggest that sensorimotor EEG activity can track proprioceptive feedback induced by the hand exoskeleton. While this feedback is pronounced and distinguishable, detecting motor intention during exoskeleton movement remains highly challenging. This highlights the need for advanced decoders and control strategies for the future development of continuous BCI-actuated hand exoskeletons.
Additional Links: PMID-40644135
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PubMed:
Citation:
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@article {pmid40644135,
year = {2025},
author = {Sun, Q and Merino, EC and Yang, L and Faes, A and Van Hulle, MM},
title = {On the Impact of Proprioception in EEG Representations and Decoding During Human-Hand Exoskeleton Interaction.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {186-192},
doi = {10.1109/ICORR66766.2025.11063039},
pmid = {40644135},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Proprioception/physiology ; Male ; *Exoskeleton Device ; Female ; Adult ; Brain-Computer Interfaces ; Young Adult ; *Hand/physiology ; Movement/physiology ; Fingers/physiology ; },
abstract = {Controlling a hand exoskeleton based on electroencephalogram (EEG)-based brain-computer interfacing (BCI) holds promise for human motor augmentation and neurore-habilitation. To achieve natural control, a critical step is to understand the impact of proprioception provided by the exoskeleton during interaction. In this study, we aim to approach the goal by quantifying EEG representations and BCI performance. We monitored 25 healthy subjects' full-scalp EEG while performing different finger movement tasks with a cable-driven hand exoskeleton. Each task involves three movement modalities, i.e., imagined (IM), passive (PM), and congruent imagined and passive (IPM) finger flexion. We found that alpha (8 - 13 Hz) and beta (13 - 30 Hz) band desynchronization in the sensorimotor area was significantly stronger for PM and IPM tasks compared to IM, with no significant difference between PM and IPM. Using machine learning models, we achieved a high accuracy in classifying exoskeleton-assisted movements from the rest condition (IPM vs. REST: 0.80 ± 0.07, PM vs. REST: 0.72 ± 0.10), with the IPM modality returning the highest accuracy. However, distinguishing between IPM and PM yielded only 0.61 ± 0.09, significantly lower than the condition of intention detection without the exoskeleton (IM vs. REST: 0.73 ± 0.08). Our findings suggest that sensorimotor EEG activity can track proprioceptive feedback induced by the hand exoskeleton. While this feedback is pronounced and distinguishable, detecting motor intention during exoskeleton movement remains highly challenging. This highlights the need for advanced decoders and control strategies for the future development of continuous BCI-actuated hand exoskeletons.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Electroencephalography/methods
*Proprioception/physiology
Male
*Exoskeleton Device
Female
Adult
Brain-Computer Interfaces
Young Adult
*Hand/physiology
Movement/physiology
Fingers/physiology
RevDate: 2025-07-11
CmpDate: 2025-07-11
Comparative Analysis of Neural Decoding Algorithms for Brain-Machine Interfaces.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:222-227.
Accurate neural decoding of brain dynamics remains an open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies to evaluate different combinations of state-of-the-art algorithms for motor neural decoding in order to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that can help inform the design of next-generation neural decoding algorithms for brain-machine interfaces.
Additional Links: PMID-40644105
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PubMed:
Citation:
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@article {pmid40644105,
year = {2025},
author = {Shevchenko, O and Yeremeieva, S and Laschowski, B},
title = {Comparative Analysis of Neural Decoding Algorithms for Brain-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {222-227},
doi = {10.1109/ICORR66766.2025.11063033},
pmid = {40644105},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; *Algorithms ; Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Accurate neural decoding of brain dynamics remains an open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies to evaluate different combinations of state-of-the-art algorithms for motor neural decoding in order to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that can help inform the design of next-generation neural decoding algorithms for brain-machine interfaces.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
*Brain-Computer Interfaces
Humans
*Algorithms
Electroencephalography/methods
*Signal Processing, Computer-Assisted
Brain/physiology
Machine Learning
Neural Networks, Computer
RevDate: 2025-07-11
Explaining E/MEG Source Imaging and Beyond: An Updated Review.
IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].
E/MEG source imaging (ESI) provides noninvasive measurements of brain activity with high spatial and temporal resolution. In particular, the wearability and portability of EEG make it an attractive area of research not only in the biomedical communities especially when considering the wide applications prospects including brain-computer interface (BCI), neuromarketing, neuroergonomics, etc. Although there are already some valuable and impressive reviews on ESI, these reviews introduce the ESI models in a relatively isolated way and lack the recent advances in ESI. In this work, we aim to: 1) provide a timely in-depth review of the widely-explored/state-of-the art ESI models including their underlying neurophysiological assumptions and mathematical derivations; 2) list the primary applications of ESI and highlight the crucial steps regarding its implementations; 3) discuss the challenges in ESI and suggest several future research prospects; 4) demonstrate practical usage and implementation details of various representative ESI models along with open-source dataset/codes (link). As a rapidly expanding field, the development of ESI is continuously growing and evolving to embrace new technologies. We believe the widespread applications of ESI is happening, and it will dramatically expand our understanding of brain dynamics.
Additional Links: PMID-40644100
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PubMed:
Citation:
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@article {pmid40644100,
year = {2025},
author = {Feng, Z and Kakkos, I and Matsopoulos, GK and Guan, C and Sun, Y},
title = {Explaining E/MEG Source Imaging and Beyond: An Updated Review.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3588350},
pmid = {40644100},
issn = {2168-2208},
abstract = {E/MEG source imaging (ESI) provides noninvasive measurements of brain activity with high spatial and temporal resolution. In particular, the wearability and portability of EEG make it an attractive area of research not only in the biomedical communities especially when considering the wide applications prospects including brain-computer interface (BCI), neuromarketing, neuroergonomics, etc. Although there are already some valuable and impressive reviews on ESI, these reviews introduce the ESI models in a relatively isolated way and lack the recent advances in ESI. In this work, we aim to: 1) provide a timely in-depth review of the widely-explored/state-of-the art ESI models including their underlying neurophysiological assumptions and mathematical derivations; 2) list the primary applications of ESI and highlight the crucial steps regarding its implementations; 3) discuss the challenges in ESI and suggest several future research prospects; 4) demonstrate practical usage and implementation details of various representative ESI models along with open-source dataset/codes (link). As a rapidly expanding field, the development of ESI is continuously growing and evolving to embrace new technologies. We believe the widespread applications of ESI is happening, and it will dramatically expand our understanding of brain dynamics.},
}
RevDate: 2025-07-11
CmpDate: 2025-07-11
Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings], 2025:1564-1569.
Patients with neurological disorders, such as stroke, often undergo upper limb motor impairments, severely limiting their ability to perform activities of daily living (ADL). Wearable robots have been developed to provide intensive and precise repetitive training for upper limb rehabilitation. Effective rehabilitation requires aligning robotic assistance with patient movement intention to promote brain plasticity. Additionally, robotic assistance must accommodate the complex, coordinated upper limb motions required for ADL tasks, including not only isolated hand movements but also integrated hand and wrist actions. This paper presents a multimodal human-machine interface (HMI) for integrated hand-wrist rehabilitation using both EEG and EMG signals. A three-degrees-of-freedom (3-DOF) soft wearable robot, combining a robotic hand glove and forearm skin brace, was designed to assist coordinated hand and wrist movements during reaching and grasping. EEG signals classified rest and grasp states using a Riemannian geometry approach, while EMG signals from three forearm muscles detected reaching onset to trigger the wrist adjustment. Preliminary tests with four healthy participants demonstrated 85% accuracy in EEG-based classification and sufficient EMG amplitude for motion onset detection. Future studies will expand participant testing to improve system robustness and evaluate its effectiveness for stroke rehabilitation.
Additional Links: PMID-40644042
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PubMed:
Citation:
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@article {pmid40644042,
year = {2025},
author = {Kim, M and Jo, S and Cho, H and Ye, S and Kim, Y and Park, HS},
title = {Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1564-1569},
doi = {10.1109/ICORR66766.2025.11063079},
pmid = {40644042},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Electromyography/methods/instrumentation ; *Wrist/physiology ; Male ; Adult ; Wearable Electronic Devices ; Stroke Rehabilitation ; *Robotics/instrumentation ; Female ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; },
abstract = {Patients with neurological disorders, such as stroke, often undergo upper limb motor impairments, severely limiting their ability to perform activities of daily living (ADL). Wearable robots have been developed to provide intensive and precise repetitive training for upper limb rehabilitation. Effective rehabilitation requires aligning robotic assistance with patient movement intention to promote brain plasticity. Additionally, robotic assistance must accommodate the complex, coordinated upper limb motions required for ADL tasks, including not only isolated hand movements but also integrated hand and wrist actions. This paper presents a multimodal human-machine interface (HMI) for integrated hand-wrist rehabilitation using both EEG and EMG signals. A three-degrees-of-freedom (3-DOF) soft wearable robot, combining a robotic hand glove and forearm skin brace, was designed to assist coordinated hand and wrist movements during reaching and grasping. EEG signals classified rest and grasp states using a Riemannian geometry approach, while EMG signals from three forearm muscles detected reaching onset to trigger the wrist adjustment. Preliminary tests with four healthy participants demonstrated 85% accuracy in EEG-based classification and sufficient EMG amplitude for motion onset detection. Future studies will expand participant testing to improve system robustness and evaluate its effectiveness for stroke rehabilitation.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
*Electroencephalography/methods
*Hand/physiology
*Electromyography/methods/instrumentation
*Wrist/physiology
Male
Adult
Wearable Electronic Devices
Stroke Rehabilitation
*Robotics/instrumentation
Female
Signal Processing, Computer-Assisted
Brain-Computer Interfaces
RevDate: 2025-07-10
CmpDate: 2025-07-11
Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.
Journal of nanobiotechnology, 23(1):498.
Brain‒computer interfaces (BCIs) exhibit significant potential for various applications, including neurofeedback training, neurological injury management, and language, sensory and motor rehabilitation. Neural interfacing electrodes are positioned between external electronic devices and the nervous system to capture complex neuronal activity data and promote the repair of damaged neural tissues. Implantable neural electrodes can record and modulate neural activities with both high spatial and high temporal resolution, offering a wide window for neuroscience research. Despite significant advancements over the years, conventional neural electrode interfaces remain insufficient for fully achieving these objectives, particularly in the context of long-term implantation. The primary limitation stems from the poor biocompatibility and mechanical mismatch between the interfacing electrodes and neural tissues, which induce a local immune response and scar tissue formation, thus decreasing the performance and useful lifespan. Therefore, neural interfaces should ideally exhibit appropriate stiffness and minimal foreign body reactions to mitigate neuroinflammation and enhance recording quality. This review provides an exhaustive analysis of the current understanding of the critical failure modes that may impact the performance of implantable neural electrodes. Additionally, this study provides a comprehensive overview of the current research on coating materials and design strategies for implanted neural interfaces and discusses the primary challenges currently facing long-term implantation of neural electrodes. Finally, we present our perspective and propose possible future research directions to improve implantable neural interfaces for BCIs.
Additional Links: PMID-40640801
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@article {pmid40640801,
year = {2025},
author = {Gao, W and Yan, Z and Zhou, H and Xie, Y and Wang, H and Yang, J and Yu, J and Ni, C and Liu, P and Xie, M and Huang, L and Ye, Z},
title = {Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {498},
pmid = {40640801},
issn = {1477-3155},
support = {National Innovation Platform Development Program (No. 2020021105012440), the National Natural Science Foundation of China (No. 82172524, 81974355)//Zhewei Ye/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electrodes, Implanted ; Animals ; *Biocompatible Materials/chemistry ; Brain/physiology ; },
abstract = {Brain‒computer interfaces (BCIs) exhibit significant potential for various applications, including neurofeedback training, neurological injury management, and language, sensory and motor rehabilitation. Neural interfacing electrodes are positioned between external electronic devices and the nervous system to capture complex neuronal activity data and promote the repair of damaged neural tissues. Implantable neural electrodes can record and modulate neural activities with both high spatial and high temporal resolution, offering a wide window for neuroscience research. Despite significant advancements over the years, conventional neural electrode interfaces remain insufficient for fully achieving these objectives, particularly in the context of long-term implantation. The primary limitation stems from the poor biocompatibility and mechanical mismatch between the interfacing electrodes and neural tissues, which induce a local immune response and scar tissue formation, thus decreasing the performance and useful lifespan. Therefore, neural interfaces should ideally exhibit appropriate stiffness and minimal foreign body reactions to mitigate neuroinflammation and enhance recording quality. This review provides an exhaustive analysis of the current understanding of the critical failure modes that may impact the performance of implantable neural electrodes. Additionally, this study provides a comprehensive overview of the current research on coating materials and design strategies for implanted neural interfaces and discusses the primary challenges currently facing long-term implantation of neural electrodes. Finally, we present our perspective and propose possible future research directions to improve implantable neural interfaces for BCIs.},
}
MeSH Terms:
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*Brain-Computer Interfaces
Humans
*Electrodes, Implanted
Animals
*Biocompatible Materials/chemistry
Brain/physiology
RevDate: 2025-07-10
CmpDate: 2025-07-10
Changes in cortical beta power predict motor control flexibility, not vigor.
Communications biology, 8(1):1041.
The amplitude of beta-band activity (β power; 13-30 Hz) over motor cortical regions is used to assess and decode movement in clinical settings and brain-computer interfaces, as β power is often assumed to predict the strength of the brain's motor output, or "vigor". However, recent conflicting evidence challenges this assumption and underscores the need to clarify the relationship between β power and movement. In this study, sixty participants were trained to self-regulate β power using electroencephalography-based neurofeedback before performing different motor tasks. Results show that β power modulations can impact different motor variables, or the same variables in opposite directions, depending on task constraints. Importantly, downregulation of β power is associated with better task performance regardless of whether performance implied increasing or decreasing motor vigor. These findings demonstrate that β power should be interpreted as a measure of motor flexibility, which underlies adaptation to environmental constraints, rather than vigor.
Additional Links: PMID-40640486
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@article {pmid40640486,
year = {2025},
author = {Pierrieau, E and Dussard, C and Plantey-Veux, A and Guerrini, C and Lau, B and Pillette, L and George, N and Jeunet-Kelway, C},
title = {Changes in cortical beta power predict motor control flexibility, not vigor.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1041},
pmid = {40640486},
issn = {2399-3642},
support = {ANR-20-CE37-0012//Agence Nationale de la Recherche (French National Research Agency)/ ; },
mesh = {Humans ; Male ; Female ; *Beta Rhythm/physiology ; Adult ; Electroencephalography ; Young Adult ; *Motor Cortex/physiology ; Neurofeedback ; Brain-Computer Interfaces ; Movement/physiology ; Psychomotor Performance/physiology ; },
abstract = {The amplitude of beta-band activity (β power; 13-30 Hz) over motor cortical regions is used to assess and decode movement in clinical settings and brain-computer interfaces, as β power is often assumed to predict the strength of the brain's motor output, or "vigor". However, recent conflicting evidence challenges this assumption and underscores the need to clarify the relationship between β power and movement. In this study, sixty participants were trained to self-regulate β power using electroencephalography-based neurofeedback before performing different motor tasks. Results show that β power modulations can impact different motor variables, or the same variables in opposite directions, depending on task constraints. Importantly, downregulation of β power is associated with better task performance regardless of whether performance implied increasing or decreasing motor vigor. These findings demonstrate that β power should be interpreted as a measure of motor flexibility, which underlies adaptation to environmental constraints, rather than vigor.},
}
MeSH Terms:
show MeSH Terms
hide MeSH Terms
Humans
Male
Female
*Beta Rhythm/physiology
Adult
Electroencephalography
Young Adult
*Motor Cortex/physiology
Neurofeedback
Brain-Computer Interfaces
Movement/physiology
Psychomotor Performance/physiology
RevDate: 2025-07-10
Structures and Molecular Mechanisms of Insect Odorant and Gustatory Receptors.
Physiology (Bethesda, Md.) [Epub ahead of print].
Insects rely on chemoreceptors in sensory neurons to detect and discriminate various chemicals in constantly changing environments. Among the chemoreceptors, odorant receptors (ORs) and gustatory receptors (GRs) play essential roles in sensing different odorant and tastant molecules, thereby regulating insects' physiology and behaviors such as feeding, mating, and alarming. ORs and GRs are evolutionarily related seven-transmembrane helical proteins that constitute a large family of tetrameric ion channels. In recent years, great progress has been made in the structures and molecular mechanisms of insect ORs and GRs. In this review, we summarize the available structures of insect ORs and GRs, analyze their diverse ligand recognition modes, and examine their conserved ligand activation mechanisms. These structural analyses will not only enhance our understanding of molecular basis of insect ORs and GRs but also provide critical insights for the future discovery of repellents and attractants.
Additional Links: PMID-40638250
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@article {pmid40638250,
year = {2025},
author = {Zhang, X and Ma, D and Wang, J and Su, N and Guo, J},
title = {Structures and Molecular Mechanisms of Insect Odorant and Gustatory Receptors.},
journal = {Physiology (Bethesda, Md.)},
volume = {},
number = {},
pages = {},
doi = {10.1152/physiol.00011.2025},
pmid = {40638250},
issn = {1548-9221},
support = {2020YFA0908501//Ministry of Science and Technology of China/ ; 32371204//National Science Foundation of China/ ; LD25C050004//Zhejiang Provincial Natural Science Foundation/ ; //Foundamental Research Funds for the Central Universities/ ; //Ministry of Education Frontier Science Center for Brain Science & Brain-Machine Integration/ ; //K.C. Wong Education Foundation/ ; },
abstract = {Insects rely on chemoreceptors in sensory neurons to detect and discriminate various chemicals in constantly changing environments. Among the chemoreceptors, odorant receptors (ORs) and gustatory receptors (GRs) play essential roles in sensing different odorant and tastant molecules, thereby regulating insects' physiology and behaviors such as feeding, mating, and alarming. ORs and GRs are evolutionarily related seven-transmembrane helical proteins that constitute a large family of tetrameric ion channels. In recent years, great progress has been made in the structures and molecular mechanisms of insect ORs and GRs. In this review, we summarize the available structures of insect ORs and GRs, analyze their diverse ligand recognition modes, and examine their conserved ligand activation mechanisms. These structural analyses will not only enhance our understanding of molecular basis of insect ORs and GRs but also provide critical insights for the future discovery of repellents and attractants.},
}
RevDate: 2025-07-11
Vocal taking turns is premature at birth and improved by the postnatal phonetic environment in marmosets.
National science review, 12(7):nwaf162.
Precisely time-controlled vocal antiphony is crucial for the social communication of arboreal marmosets. However, it remains unclear when this antiphony is formed and how postnatal acoustic environments affect its development. In the present study, we systematically recorded the emitted calls of infant marmosets in an antiphonal calling scenario from postnatal day one (P1) to postnatal 10 weeks (W10). We found that infant marmosets emit most types of adult calls and engage in turn-taking as early as in P1. In addition, parent-reared infants emitted more antiphonal phee calls than hand-reared marmosets in W10. Call transitions in parent-reared W10 animals mainly occurred between phee calls or from phee calls to other call types. In contrast, P1 and hand-reared W10 marmosets displayed call transitions among various types of calls. These findings suggest that the antiphony in marmosets emerges on P1 but remains immature, and the antiphony skills can be improved by development environments, especially by the vocal feedback from parents.
Additional Links: PMID-40636103
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@article {pmid40636103,
year = {2025},
author = {Qi, R and Lin, Y and Liu, S and Cao, X and Xie, M and Yu, C and Sun, H and Gao, L and Li, X},
title = {Vocal taking turns is premature at birth and improved by the postnatal phonetic environment in marmosets.},
journal = {National science review},
volume = {12},
number = {7},
pages = {nwaf162},
pmid = {40636103},
issn = {2053-714X},
abstract = {Precisely time-controlled vocal antiphony is crucial for the social communication of arboreal marmosets. However, it remains unclear when this antiphony is formed and how postnatal acoustic environments affect its development. In the present study, we systematically recorded the emitted calls of infant marmosets in an antiphonal calling scenario from postnatal day one (P1) to postnatal 10 weeks (W10). We found that infant marmosets emit most types of adult calls and engage in turn-taking as early as in P1. In addition, parent-reared infants emitted more antiphonal phee calls than hand-reared marmosets in W10. Call transitions in parent-reared W10 animals mainly occurred between phee calls or from phee calls to other call types. In contrast, P1 and hand-reared W10 marmosets displayed call transitions among various types of calls. These findings suggest that the antiphony in marmosets emerges on P1 but remains immature, and the antiphony skills can be improved by development environments, especially by the vocal feedback from parents.},
}
RevDate: 2025-07-09
Projection of Cortical Beta Band Oscillations to a Motor Neuron Pool Across the Full Range of Recruitment.
The Journal of neuroscience : the official journal of the Society for Neuroscience pii:JNEUROSCI.0453-25.2025 [Epub ahead of print].
Cortical beta band oscillations (13-30 Hz) are associated with sensorimotor control, but their precise role remains unclear. Evidence suggests that for low-threshold motor neurons, these oscillations are conveyed to muscles via the fastest corticospinal fibers. However, their transmission to motor neurons of different sizes may vary due to differences in the relative strength of corticospinal and reticulospinal projections across the motor neuron pool. Consequently, it remains uncertain whether corticospinal beta transmission follows similar pathways and maintains consistent strength across the entire motor neuron pool. To investigate this, we examined beta activity in motor neurons innervating the tibialis anterior muscle across the full range of recruitment thresholds in a study involving 12 participants of both sexes. We characterized beta activity at both the cortical and motor unit levels while participants performed contractions from mild to submaximal levels. Corticomuscular coherence remained unchanged across contraction forces after normalizing for the net motor unit spike rate, suggesting that beta oscillations are transmitted with similar strength to motor neurons, regardless of size. To further explore beta transmission, we estimated corticospinal delays using the cumulant density function, identifying peak correlations between cortical and muscular activity. Once compensated for variable peripheral axonal propagation delay across motor neurons, the corticospinal delay remained stable, and its value (approximately 14 ms) indicated projections through the fastest corticospinal fibers for all motor neurons. These findings demonstrate that corticospinal beta band transmission is determined by the fastest pathway connecting in the corticospinal tract, projecting similarly across the entire motor neuron pool.Significance Statement Beta band oscillations (13-30 Hz) play a key role in sensorimotor control, yet their precise transmission to motor neurons remains unclear. This study demonstrates that beta oscillations are transmitted similarly across the entire motor neuron pool, regardless of recruitment threshold. By examining corticomuscular coherence and corticospinal delays during voluntary contractions, we show that beta activity is consistently relayed to motor neurons via the fastest corticospinal fibers. These findings provide evidence that beta band activity is not preferentially directed toward specific subsets of motor neurons but is instead a global signal influencing motor output. This insight advances our understanding of how the central nervous system regulates movement and may have implications for neurorehabilitation and brain-machine interfaces.
Additional Links: PMID-40634126
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@article {pmid40634126,
year = {2025},
author = {Abbagnano, E and Pascual-Valdunciel, A and Zicher, B and Ibáñez, J and Farina, D},
title = {Projection of Cortical Beta Band Oscillations to a Motor Neuron Pool Across the Full Range of Recruitment.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0453-25.2025},
pmid = {40634126},
issn = {1529-2401},
abstract = {Cortical beta band oscillations (13-30 Hz) are associated with sensorimotor control, but their precise role remains unclear. Evidence suggests that for low-threshold motor neurons, these oscillations are conveyed to muscles via the fastest corticospinal fibers. However, their transmission to motor neurons of different sizes may vary due to differences in the relative strength of corticospinal and reticulospinal projections across the motor neuron pool. Consequently, it remains uncertain whether corticospinal beta transmission follows similar pathways and maintains consistent strength across the entire motor neuron pool. To investigate this, we examined beta activity in motor neurons innervating the tibialis anterior muscle across the full range of recruitment thresholds in a study involving 12 participants of both sexes. We characterized beta activity at both the cortical and motor unit levels while participants performed contractions from mild to submaximal levels. Corticomuscular coherence remained unchanged across contraction forces after normalizing for the net motor unit spike rate, suggesting that beta oscillations are transmitted with similar strength to motor neurons, regardless of size. To further explore beta transmission, we estimated corticospinal delays using the cumulant density function, identifying peak correlations between cortical and muscular activity. Once compensated for variable peripheral axonal propagation delay across motor neurons, the corticospinal delay remained stable, and its value (approximately 14 ms) indicated projections through the fastest corticospinal fibers for all motor neurons. These findings demonstrate that corticospinal beta band transmission is determined by the fastest pathway connecting in the corticospinal tract, projecting similarly across the entire motor neuron pool.Significance Statement Beta band oscillations (13-30 Hz) play a key role in sensorimotor control, yet their precise transmission to motor neurons remains unclear. This study demonstrates that beta oscillations are transmitted similarly across the entire motor neuron pool, regardless of recruitment threshold. By examining corticomuscular coherence and corticospinal delays during voluntary contractions, we show that beta activity is consistently relayed to motor neurons via the fastest corticospinal fibers. These findings provide evidence that beta band activity is not preferentially directed toward specific subsets of motor neurons but is instead a global signal influencing motor output. This insight advances our understanding of how the central nervous system regulates movement and may have implications for neurorehabilitation and brain-machine interfaces.},
}
RevDate: 2025-07-09
CmpDate: 2025-07-09
Experiences and Well-Being of Early-Career Trauma Nurses in India: A Mixed Methods Study.
Journal of trauma nursing : the official journal of the Society of Trauma Nurses, 32(4):189-200.
BACKGROUND: Trauma nursing is fast-paced and high-pressure work that can affect nurses' physical and mental health. However, these effects remain unexplored among novice trauma nurses in a newly established trauma center in India.
OBJECTIVE: To examine relationships between professional quality of life, sleep disturbances, anxiety, and resilience and to explore the experiences of novice trauma nurses in a newly established trauma center in India.
METHODS: This sequential mixed-methods study was conducted between April and June 2024 in a newly established trauma center in India. A purposive sample of 80 nurses was surveyed using a demographic questionnaire, the Brief Resilience Scale, the Generalized Anxiety Disorder Scale, the Insomnia Severity Index, and the Professional Quality of Life Scale. Nine nurses were interviewed to explore their trauma experiences. The analysis included descriptive and inferential statistics, bootstrap-based mediation testing, and thematic content analysis.
RESULTS: A total of 80 nurses completed the survey (response rate: 67.8%) with a mean age of 27.7 years (standard deviation [SD] = 2.89) and average years of trauma experience of 2.08 years (SD = 1.93). Higher compassion satisfaction correlated with fewer sleep disturbances (r = -.23, p = .037). Burnout positively correlated with anxiety (r = .24, p = .033) and sleep disturbances (r = .34, p = .023), and negatively with nurses' resilience (r = -.12, p = .049). Professional quality of life significantly correlated with resilience (r = .18, p = .048). Resilience mediated the relationship between anxiety and both burnout (β = .24, bootstrap confidence interval [BCI] = 0.04, 0.46, p = .041) and secondary traumatic stress (β = .24, BCI = 0.03, 0.52, p = .029). Qualitative analysis revealed three major themes: personal and professional adaptation to trauma life, adverse physical and psychological issues, and challenges faced in trauma care.
CONCLUSION: Our findings highlight the adverse impact of trauma nursing on sleep, resilience, anxiety, and professional quality of life among novice nurses in a newly established Level I trauma center in India. Targeted interventions are required to enhance resilience and mitigate the impact of caring for trauma patients.
Additional Links: PMID-40632037
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Citation:
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@article {pmid40632037,
year = {2025},
author = {Kumar, R and Soni, A and Ahmed, T and Beniwal, K},
title = {Experiences and Well-Being of Early-Career Trauma Nurses in India: A Mixed Methods Study.},
journal = {Journal of trauma nursing : the official journal of the Society of Trauma Nurses},
volume = {32},
number = {4},
pages = {189-200},
pmid = {40632037},
issn = {1078-7496},
mesh = {Humans ; India ; Adult ; Female ; Male ; *Quality of Life/psychology ; Surveys and Questionnaires ; *Trauma Nursing ; Job Satisfaction ; Resilience, Psychological ; *Nursing Staff, Hospital/psychology ; Trauma Centers ; Burnout, Professional/psychology ; },
abstract = {BACKGROUND: Trauma nursing is fast-paced and high-pressure work that can affect nurses' physical and mental health. However, these effects remain unexplored among novice trauma nurses in a newly established trauma center in India.
OBJECTIVE: To examine relationships between professional quality of life, sleep disturbances, anxiety, and resilience and to explore the experiences of novice trauma nurses in a newly established trauma center in India.
METHODS: This sequential mixed-methods study was conducted between April and June 2024 in a newly established trauma center in India. A purposive sample of 80 nurses was surveyed using a demographic questionnaire, the Brief Resilience Scale, the Generalized Anxiety Disorder Scale, the Insomnia Severity Index, and the Professional Quality of Life Scale. Nine nurses were interviewed to explore their trauma experiences. The analysis included descriptive and inferential statistics, bootstrap-based mediation testing, and thematic content analysis.
RESULTS: A total of 80 nurses completed the survey (response rate: 67.8%) with a mean age of 27.7 years (standard deviation [SD] = 2.89) and average years of trauma experience of 2.08 years (SD = 1.93). Higher compassion satisfaction correlated with fewer sleep disturbances (r = -.23, p = .037). Burnout positively correlated with anxiety (r = .24, p = .033) and sleep disturbances (r = .34, p = .023), and negatively with nurses' resilience (r = -.12, p = .049). Professional quality of life significantly correlated with resilience (r = .18, p = .048). Resilience mediated the relationship between anxiety and both burnout (β = .24, bootstrap confidence interval [BCI] = 0.04, 0.46, p = .041) and secondary traumatic stress (β = .24, BCI = 0.03, 0.52, p = .029). Qualitative analysis revealed three major themes: personal and professional adaptation to trauma life, adverse physical and psychological issues, and challenges faced in trauma care.
CONCLUSION: Our findings highlight the adverse impact of trauma nursing on sleep, resilience, anxiety, and professional quality of life among novice nurses in a newly established Level I trauma center in India. Targeted interventions are required to enhance resilience and mitigate the impact of caring for trauma patients.},
}
MeSH Terms:
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Humans
India
Adult
Female
Male
*Quality of Life/psychology
Surveys and Questionnaires
*Trauma Nursing
Job Satisfaction
Resilience, Psychological
*Nursing Staff, Hospital/psychology
Trauma Centers
Burnout, Professional/psychology
RevDate: 2025-07-09
Psychedelics and the Gut Microbiome: Unraveling the Interplay and Therapeutic Implications.
ACS chemical neuroscience [Epub ahead of print].
Classic psychedelics and the gut microbiome interact bidirectionally through mechanisms involving 5-HT2A receptor signaling, neuroplasticity, and microbial metabolism. This viewpoint highlights how psychedelics may reshape microbiota and how microbes influence psychedelic efficacy, proposing microbiome-informed strategies─such as probiotics or dietary interventions─to personalize and enhance psychedelic-based mental health therapies.
Additional Links: PMID-40631920
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@article {pmid40631920,
year = {2025},
author = {Wang, X and Jun, F and Lin, C and Wang, X},
title = {Psychedelics and the Gut Microbiome: Unraveling the Interplay and Therapeutic Implications.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00418},
pmid = {40631920},
issn = {1948-7193},
abstract = {Classic psychedelics and the gut microbiome interact bidirectionally through mechanisms involving 5-HT2A receptor signaling, neuroplasticity, and microbial metabolism. This viewpoint highlights how psychedelics may reshape microbiota and how microbes influence psychedelic efficacy, proposing microbiome-informed strategies─such as probiotics or dietary interventions─to personalize and enhance psychedelic-based mental health therapies.},
}
RevDate: 2025-07-09
Reading specific memories from human neurons before and after sleep.
bioRxiv : the preprint server for biology pii:2025.07.01.662486.
The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory [1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model [2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode. Combining spikes throughout the brain via cross-channel attention [4], capable of discovering neural patterns spread across brain regions and timescales, individual participant models predict memory retrieval of specific concepts such as persons or places. Brain regions differentially contribute to memory decoding before and after sleep. Models trained using only medial temporal lobe (MTL) spikes significantly decode concepts before but not after sleep, while models trained using only frontal cortex (FC) spikes decode concepts after but not before sleep. These findings suggest a system-wide distribution of information across neural populations that transforms over wake/sleep cycles [5]. Such decoding of internally generated memories suggests a path towards brain-computer interfaces to treat episodic memory disorders through enhancement or muting of specific memories.
Additional Links: PMID-40631106
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@article {pmid40631106,
year = {2025},
author = {Ding, Y and Dunn, SLS and Sakon, JJ and Duan, C and Zhang, Y and Berger, JI and Rhone, AE and Nourski, KV and Kawasaki, H and Howard, MA and Roychowdhury, VP and Fried, I},
title = {Reading specific memories from human neurons before and after sleep.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.01.662486},
pmid = {40631106},
issn = {2692-8205},
abstract = {The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory [1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model [2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode. Combining spikes throughout the brain via cross-channel attention [4], capable of discovering neural patterns spread across brain regions and timescales, individual participant models predict memory retrieval of specific concepts such as persons or places. Brain regions differentially contribute to memory decoding before and after sleep. Models trained using only medial temporal lobe (MTL) spikes significantly decode concepts before but not after sleep, while models trained using only frontal cortex (FC) spikes decode concepts after but not before sleep. These findings suggest a system-wide distribution of information across neural populations that transforms over wake/sleep cycles [5]. Such decoding of internally generated memories suggests a path towards brain-computer interfaces to treat episodic memory disorders through enhancement or muting of specific memories.},
}
RevDate: 2025-07-09
Neural trajectories improve motor precision.
bioRxiv : the preprint server for biology pii:2025.07.01.662682.
Populations of neurons in motor cortex signal voluntary movement. Most classic neural encoding models and current brain-computer interface decoders assume individual neurons sum together along a neural dimension to represent movement features such as velocity or force. However, large population neural analyses continue to identify trajectories of neural activity evolving with time that traverse multiple dimensions. Explanations for these neural trajectories typically focus on how cortical circuits processes learn, organize, and implement movements. However, descriptions of how these neural trajectories might improve performance, and specifically motor precision, are lacking. In this study, we proposed and tested a computational model that highlights the role of neural trajectories, through the selective co-activation and selective timing of firing rates across the neural populations, for improving motor precision. Our model uses experimental results from a center-out reaching task as inspiration to create several physiologically realistic models for the neural encoding of movement. Using a recurrent neural network to simulate how a downstream population of neurons might receive such information, like the spinal cord and motor units, we show that movements are more accurate when neural information specific to the phase and/or amplitude of movement are incorporated across time instead of an instantaneous, velocity-only tuning model. Our finding suggests that precise motor control arises from spatiotemporal recruitment of neural populations that create distinct neural trajectories. We anticipate our results will significantly impact not only how neural encoding of movement in motor cortex is described but also future understating for how brain networks communicate information for planning and executing movements. Our model also provides potential inspiration for how to incorporate selective activation across a neural population to improve future brain-computer interfaces.
Additional Links: PMID-40631097
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@article {pmid40631097,
year = {2025},
author = {Lee, W and Scherschligt, X and Nishimoto, M and Rouse, AG},
title = {Neural trajectories improve motor precision.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.01.662682},
pmid = {40631097},
issn = {2692-8205},
abstract = {Populations of neurons in motor cortex signal voluntary movement. Most classic neural encoding models and current brain-computer interface decoders assume individual neurons sum together along a neural dimension to represent movement features such as velocity or force. However, large population neural analyses continue to identify trajectories of neural activity evolving with time that traverse multiple dimensions. Explanations for these neural trajectories typically focus on how cortical circuits processes learn, organize, and implement movements. However, descriptions of how these neural trajectories might improve performance, and specifically motor precision, are lacking. In this study, we proposed and tested a computational model that highlights the role of neural trajectories, through the selective co-activation and selective timing of firing rates across the neural populations, for improving motor precision. Our model uses experimental results from a center-out reaching task as inspiration to create several physiologically realistic models for the neural encoding of movement. Using a recurrent neural network to simulate how a downstream population of neurons might receive such information, like the spinal cord and motor units, we show that movements are more accurate when neural information specific to the phase and/or amplitude of movement are incorporated across time instead of an instantaneous, velocity-only tuning model. Our finding suggests that precise motor control arises from spatiotemporal recruitment of neural populations that create distinct neural trajectories. We anticipate our results will significantly impact not only how neural encoding of movement in motor cortex is described but also future understating for how brain networks communicate information for planning and executing movements. Our model also provides potential inspiration for how to incorporate selective activation across a neural population to improve future brain-computer interfaces.},
}
RevDate: 2025-07-10
CmpDate: 2025-07-10
Will our social brain inherently shape and be shaped by interactions with AI?.
Neuron, 113(13):2037-2041.
Social-specific brain circuits enable rapid understanding and affiliation in interpersonal interactions. These evolutionarily and experience-shaped mechanisms will influence-and be influenced by-interactions with conversational AI agents (chatbots, avatars). This NeuroView explores fundamental circuits, computations, and societal implications.
Additional Links: PMID-40505654
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@article {pmid40505654,
year = {2025},
author = {Becker, B},
title = {Will our social brain inherently shape and be shaped by interactions with AI?.},
journal = {Neuron},
volume = {113},
number = {13},
pages = {2037-2041},
doi = {10.1016/j.neuron.2025.04.034},
pmid = {40505654},
issn = {1097-4199},
mesh = {Humans ; *Brain/physiology ; *Artificial Intelligence ; *Social Behavior ; *Interpersonal Relations ; *Social Interaction ; Animals ; *Brain-Computer Interfaces ; },
abstract = {Social-specific brain circuits enable rapid understanding and affiliation in interpersonal interactions. These evolutionarily and experience-shaped mechanisms will influence-and be influenced by-interactions with conversational AI agents (chatbots, avatars). This NeuroView explores fundamental circuits, computations, and societal implications.},
}
MeSH Terms:
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Humans
*Brain/physiology
*Artificial Intelligence
*Social Behavior
*Interpersonal Relations
*Social Interaction
Animals
*Brain-Computer Interfaces
RevDate: 2025-07-09
CmpDate: 2025-07-09
Designing Multifunctional Microneedles in Biomedical Engineering: Materials, Methods, and Applications.
International journal of nanomedicine, 20:8693-8728.
This review focuses on the emerging technology of multifunctional microneedles (MNs) within the biomedical engineering (BME) field, highlighting their potential in drug delivery, diagnostics, and therapeutics. Previous studies have explored MNs in various applications; however, their diverse functionalities across different material types and advanced application domains have been rarely comprehensively explored. This review bridges this gap by providing insights into the application of MNs in materials science, drug delivery, diagnostic monitoring, and tissue engineering. The unique properties and skin effects of various inorganic (eg, silicon, metals) and organic materials (eg, polysaccharides, polymers, proteins) used in MNs are examined. The analysis emphasizes the advantages of different MN materials, ie, their biocompatibility, degradation rates, and application specificity. In addition, the preparation processes and application scenarios of each MN type, such as minimally invasive drug delivery in transdermal applications and their benefits in tissue engineering for promoting repair, regeneration, and precise delivery of cells and growth factors in tissues like skin, cartilage, muscle, bone, and nerves, are discussed. Furthermore, this review explores the innovative use of MNs in brain-computer interfaces-an area not yet thoroughly examined. This novel application offers significant opportunities in neuroscience and clinical practice. Overall, this review provides valuable insights into the current research landscape and unexplored areas of MNs, contributing to future advancements in BME.
Additional Links: PMID-40630938
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@article {pmid40630938,
year = {2025},
author = {Liu, L and Wang, F and Chen, X and Liu, L and Wang, Y and Bei, J and Lei, L and Zhao, Z and Tang, C},
title = {Designing Multifunctional Microneedles in Biomedical Engineering: Materials, Methods, and Applications.},
journal = {International journal of nanomedicine},
volume = {20},
number = {},
pages = {8693-8728},
pmid = {40630938},
issn = {1178-2013},
mesh = {*Needles ; Humans ; *Drug Delivery Systems/instrumentation/methods ; Tissue Engineering/methods ; *Biomedical Engineering/methods/instrumentation ; Animals ; Biocompatible Materials/chemistry ; Equipment Design ; *Microinjections/instrumentation ; Brain-Computer Interfaces ; },
abstract = {This review focuses on the emerging technology of multifunctional microneedles (MNs) within the biomedical engineering (BME) field, highlighting their potential in drug delivery, diagnostics, and therapeutics. Previous studies have explored MNs in various applications; however, their diverse functionalities across different material types and advanced application domains have been rarely comprehensively explored. This review bridges this gap by providing insights into the application of MNs in materials science, drug delivery, diagnostic monitoring, and tissue engineering. The unique properties and skin effects of various inorganic (eg, silicon, metals) and organic materials (eg, polysaccharides, polymers, proteins) used in MNs are examined. The analysis emphasizes the advantages of different MN materials, ie, their biocompatibility, degradation rates, and application specificity. In addition, the preparation processes and application scenarios of each MN type, such as minimally invasive drug delivery in transdermal applications and their benefits in tissue engineering for promoting repair, regeneration, and precise delivery of cells and growth factors in tissues like skin, cartilage, muscle, bone, and nerves, are discussed. Furthermore, this review explores the innovative use of MNs in brain-computer interfaces-an area not yet thoroughly examined. This novel application offers significant opportunities in neuroscience and clinical practice. Overall, this review provides valuable insights into the current research landscape and unexplored areas of MNs, contributing to future advancements in BME.},
}
MeSH Terms:
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*Needles
Humans
*Drug Delivery Systems/instrumentation/methods
Tissue Engineering/methods
*Biomedical Engineering/methods/instrumentation
Animals
Biocompatible Materials/chemistry
Equipment Design
*Microinjections/instrumentation
Brain-Computer Interfaces
RevDate: 2025-07-09
Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants.
medRxiv : the preprint server for health sciences pii:2025.07.02.25330310.
Brain-computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day-to-day stability properties of chronically implanted Utah arrays. Here, we present a comprehensive evaluation of 20 years of neural data from the BrainGate and BrainGate2 pilot clinical trials. This dataset spans 2,319 recording sessions and 20 arrays from the first 14 participants in these trials. On average, arrays successfully recorded neural spiking waveforms on 35.6% of electrodes, with only a 7% decline over the study enrollment period (up to 7.6 years, with a mean of 2.8 years). We assessed movement intention decoding performance using a "decoding signal-to-noise ratio" (dSNR) metric, and found that 11 of 14 arrays provided meaningful movement decoding throughout study enrollment (dSNR > 1). Three arrays reached a peak dSNR greater than 4.5, approaching that achieved during able-bodied computer mouse control (6.29). We also found that dSNR increases logarithmically with the number of electrodes, providing a pathway for scaling performance. Longevity and reliability of Utah array recordings in this study were better than in prior nonhuman primate studies. However, achieving peak performance consistently will require addressing unknown sources of variability.
Additional Links: PMID-40630584
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@article {pmid40630584,
year = {2025},
author = {Hahn, NV and Stein, E and , and Donoghue, JP and Simeral, JD and Hochberg, LR and Willett, FR},
title = {Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.02.25330310},
pmid = {40630584},
abstract = {Brain-computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day-to-day stability properties of chronically implanted Utah arrays. Here, we present a comprehensive evaluation of 20 years of neural data from the BrainGate and BrainGate2 pilot clinical trials. This dataset spans 2,319 recording sessions and 20 arrays from the first 14 participants in these trials. On average, arrays successfully recorded neural spiking waveforms on 35.6% of electrodes, with only a 7% decline over the study enrollment period (up to 7.6 years, with a mean of 2.8 years). We assessed movement intention decoding performance using a "decoding signal-to-noise ratio" (dSNR) metric, and found that 11 of 14 arrays provided meaningful movement decoding throughout study enrollment (dSNR > 1). Three arrays reached a peak dSNR greater than 4.5, approaching that achieved during able-bodied computer mouse control (6.29). We also found that dSNR increases logarithmically with the number of electrodes, providing a pathway for scaling performance. Longevity and reliability of Utah array recordings in this study were better than in prior nonhuman primate studies. However, achieving peak performance consistently will require addressing unknown sources of variability.},
}
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RJR Experience and Expertise
Researcher
Robbins holds BS, MS, and PhD degrees in the life sciences. He served as a tenured faculty member in the Zoology and Biological Science departments at Michigan State University. He is currently exploring the intersection between genomics, microbial ecology, and biodiversity — an area that promises to transform our understanding of the biosphere.
Educator
Robbins has extensive experience in college-level education: At MSU he taught introductory biology, genetics, and population genetics. At JHU, he was an instructor for a special course on biological database design. At FHCRC, he team-taught a graduate-level course on the history of genetics. At Bellevue College he taught medical informatics.
Administrator
Robbins has been involved in science administration at both the federal and the institutional levels. At NSF he was a program officer for database activities in the life sciences, at DOE he was a program officer for information infrastructure in the human genome project. At the Fred Hutchinson Cancer Research Center, he served as a vice president for fifteen years.
Technologist
Robbins has been involved with information technology since writing his first Fortran program as a college student. At NSF he was the first program officer for database activities in the life sciences. At JHU he held an appointment in the CS department and served as director of the informatics core for the Genome Data Base. At the FHCRC he was VP for Information Technology.
Publisher
While still at Michigan State, Robbins started his first publishing venture, founding a small company that addressed the short-run publishing needs of instructors in very large undergraduate classes. For more than 20 years, Robbins has been operating The Electronic Scholarly Publishing Project, a web site dedicated to the digital publishing of critical works in science, especially classical genetics.
Speaker
Robbins is well-known for his speaking abilities and is often called upon to provide keynote or plenary addresses at international meetings. For example, in July, 2012, he gave a well-received keynote address at the Global Biodiversity Informatics Congress, sponsored by GBIF and held in Copenhagen. The slides from that talk can be seen HERE.
Facilitator
Robbins is a skilled meeting facilitator. He prefers a participatory approach, with part of the meeting involving dynamic breakout groups, created by the participants in real time: (1) individuals propose breakout groups; (2) everyone signs up for one (or more) groups; (3) the groups with the most interested parties then meet, with reports from each group presented and discussed in a subsequent plenary session.
Designer
Robbins has been engaged with photography and design since the 1960s, when he worked for a professional photography laboratory. He now prefers digital photography and tools for their precision and reproducibility. He designed his first web site more than 20 years ago and he personally designed and implemented this web site. He engages in graphic design as a hobby.
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