@article {pmid40030935,
year = {2025},
author = {Meng, J and Li, X and Li, S and Fan, X and Xu, M and Ming, D},
title = {High-Frequency Power Reflects Dual Intentions of Time and Movement for Active Brain-Computer Interface.},
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.2025.3529997},
pmid = {40030935},
issn = {1558-0210},
abstract = {Active brain-computer interface (BCI) provides a natural way for direct communications between the brain and devices. However, its detectable intention is very limited, let alone of detecting dual intentions from a single electroencephalography (EEG) feature. This study aims to develop time-based active BCI, and further investigate the feasibility of detecting time-movement dual intentions using a single EEG feature. A time-movement synchronization experiment was designed, which contained the intentions of both time (500 ms vs. 1000 ms) and movement (left vs. right). Behavioural and EEG data of 22 healthy participants were recorded and analyzed in both the before (BT) and after (AT) timing prediction training sessions. Consequently, compared to the BT sessions, AT sessions led to substantially smaller absolute deviation time behaviourally, along with larger high-frequency event-related desynchronization (ERD) in frontal-motor areas, and significantly improved decoding accuracy of time. Moreover, AT sessions achieved enhanced motor-related contralateral dominance of event-related potentials (ERP) and ERDs than the BT, which illustrated a synergistic relationship between the two intentions. The feature of 20-60 Hz power can simultaneously reflect the time and movement intentions, achieving a 73.27% averaged four-classification accuracy (500 ms-left vs. 500 ms-right vs. 1000 ms-left vs.1000 ms-right), with the highest up to 93.81%. The results initiatively verified the dual role of high-frequency (20-60 Hz) power in representing both the time and movement intentions. It not only broadens the detectable intentions of active BCI, but also enables it to read user's mind concurrently from two information dimensions.},
}

@article {pmid40030934,
year = {2025},
author = {Mahmoudi, A and Khosrotabar, M and Gramann, K and Rinderknecht, S and Sharbafi, MA},
title = {Using passive BCI for personalization of assistive wearable devices: a proof-of-concept study.},
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.2025.3530154},
pmid = {40030934},
issn = {1558-0210},
abstract = {Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in δ and θ activity and decreases in α and β activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience.},
}

@article {pmid40030668,
year = {2024},
author = {Perna, A and Orban, G and Berdondini, L and Ribeiro, JF},
title = {Force Measurements to Advance the Design and Implantation of CMOS-based Neural Probes.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3519763},
pmid = {40030668},
issn = {1558-2531},
abstract = {OBJECTIVE: Tissue penetrating active neural probes provide large and densely packed microelectrode arrays for the fine-grained investigation of brain circuits and for advancing brain-machine interfaces (BMIs). To improve the electrical interfacing performances of such stiff silicon devices, which typically elicit a vigorous foreign body reaction (FBR), here we perform insertion force measurements and derive probe layout and implantation procedure optimizations.

METHODS: We performed in-vivo insertion force measurements to evaluate the impact of probe design and implantation speed on mechanically induced trauma and iatrogenic injury. Because acute damage constitutes the initial trigger of FBR, these experiments allow to characterize and minimize device invasiveness.

RESULTS: Probe sharpness outweighs cross-sectional dimensions during the dimpling stage of the implantation, when the device compresses the brain before penetration. Insertion speed does not display a major effect on dimpling magnitude. A slow speed, however, significantly increases dimpling duration.

CONCLUSION: It is crucial to use sharp devices to reduce mechanical and ischemic damage. Although slow insertion speeds typically improve the quality of acute electrophysiological recordings, we show that slow speeds should only be used upon penetration in the brain parenchyma and not during the dimpling stage. A closed-loop implantation procedure can be used to set the appropriate speed in the different insertion stages.

SIGNIFICANCE: We provide new evidence on the impact of probe layout and insertion speed on insertion force, with implications on the design and implantation procedure for minimally invasive CMOS neural probes. A novel closed-loop methodology to optimize device implantation and reduce FBR is proposed.},
}

@article {pmid40030619,
year = {2024},
author = {Tantawanich, P and Phunruangsakao, C and Izumi, SI and Hayashibe, M},
title = {A Systematic Review of Bimanual Motor Coordination in Brain-Computer Interface.},
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.2024.3522168},
pmid = {40030619},
issn = {1558-0210},
abstract = {Advancements in neuroscience and artificial intelligence are propelling rapid progress in brain-computer interfaces (BCIs). These developments hold significant potential for decoding motion intentions from brain signals, enabling direct control commands without reliance on conventional neural pathways. Growing interest exists in decoding bimanual motor tasks, crucial for activities of daily living. This stems from the need to restore motor function, especially in individuals with deficits. This review aims to summarize neurological advancements in bimanual BCIs, encompassing neuroimaging techniques, experimental paradigms, and analysis algorithms. Thirty-six articles were reviewed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search result revealed diverse experimental paradigms, protocols, and research directions, including enhancing the decoding accuracy, advancing versatile prosthesis robots, and enabling real-time applications. Notably, within BCI studies on bimanual movement coordination, a shared objective is to achieve naturalistic movement and practical applications with neurorehabilitation potential.},
}

@article {pmid40030617,
year = {2024},
author = {Jiang, Y and Li, K and Liang, Y and Chen, D and Tan, M and Li, Y},
title = {Daily Assistance for Amyotrophic Lateral Sclerosis Patients Based on a Wearable Multimodal Brain-Computer Interface Mouse.},
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.2024.3520984},
pmid = {40030617},
issn = {1558-0210},
abstract = {Amyotrophic lateral sclerosis (ALS) is a chronic, progressive neurodegenerative disease that mainly causes damage to upper and lower motor neurons. This leads to a progressive deterioration in the voluntary mobility of the upper and lower extremities in ALS patients, which underscores the pressing need for an assistance system to facilitate communication and body movement without relying on neuromuscular function. In this paper, we developed a daily assistance system for ALS patients based on a wearable multimodal brain-computer interface (BCI) mouse. The system comprises two subsystems: a mouse system assisting the upper extremity and a wheelchair system based on the mouse system assisting the lower extremity. By wearing a BCI headband, ALS patients can control a computer cursor on the screen with slight head rotation and eye blinking, and further operate a computer and drive a wheelchair with specially designed graphical user interfaces (GUIs). We designed operating tasks that simulate daily needs and invited ALS patients to perform the tasks. In total, 15 patients with upper extremity limitations performed the mouse system task and 9 patients with lower extremity mobility issues performed the wheelchair system task. To our satisfaction, all the participants fully accomplished the tasks and average accuracies of 83.9% and 87.0% for the two tasks were achieved. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate that the proposed system provides ALS patients with effective daily assistance and shows promising long-term application prospects.},
}

@article {pmid40030607,
year = {2024},
author = {Gao, Z and Xu, B and Wang, X and Zhang, W and Ping, J and Li, H and Song, A},
title = {Multilayer Brain Networks for Enhanced Decoding of Natural Hand Movements and Kinematic Parameters.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3519348},
pmid = {40030607},
issn = {1558-2531},
abstract = {Decoding natural hand movements using Movement-Related Cortical Potentials (MRCPs) features is crucial for the natural control of neuroprosthetics. However, current research has primarily focused on the characteristics of individual channels or on brain networks within a single frequency or time segment, overlooking the potential of brain networks across multiple time-frequency domains. To address this problem, our study investigates the application of multilayer brain networks (MBNs) in decoding natural hand movements and kinematic parameters, using a combination of MRCPs features and MBNs metrics. Based on grasp taxonomy, we selected four natural movements for our study: Large Diameter (LD), Sphere 3-Finger (SF), Precision Disk (PD), and Parallel Extension (PE), each incorporating two levels of speed and force parameters. The results demonstrate that a combination of MRCPs features and MBNs metrics can successfully decode not only the types of movements and kinematic parameters but also differentiate between different grasp taxonomy characteristics, such as the number of fingers exerting force and the type of grasp. In terms of movement type, we achieved a peak four-class accuracy of 60.56%. For grasp type and number of fingers exerting force, binary classification peak accuracies reached 79.25% and 79.28%, respectively. In the case of kinematic parameters, the Precision Disk movement exhibited the highest binary classification peak accuracy at 84.65%. Moreover, our research also found the changes and patterns in brain region connectivity across both time and frequency domains. We believe that our research highlights the potential of MBNs to enhance the functionality of Brain-Computer Interface (BCI) systems for more intuitive control mechanisms and contributes valuable insights into the brain's operational mechanisms.},
}

@article {pmid40030575,
year = {2024},
author = {Xu, L and Jiang, X and Wang, R and Lin, P and Yang, Y and Leng, Y and Zheng, W and Ge, S},
title = {Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3510740},
pmid = {40030575},
issn = {2168-2208},
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning techniques, particularly convolutional neural network (CNN) architectures, have gained prominence in EEG (e.g., SSVEP) decoding because of their nonlinear modeling capabilities and autonomy from manual feature extraction. However, most studies using CNNs employ temporal signals as the input and cannot directly mine the implicit frequency information, which may cause crucial frequency details to be lost and challenges in decoding. By contrast, the prevailing supervised recognition algorithms rely on a lengthy calibration phase to enhance algorithm performance, which could impede the popularization of SSVEP based BCIs. To address these problems, this study proposes the Time-Frequency Attention Network (TFA-Net), a novel CNN model tailored for SSVEP signal decoding without the calibration phase. Additionally, we introduce the Frequency Attention and Channel Recombination modules to enhance ability of TFA-Net to infer finer frequency- wise attention and extract features efficiently from SSVEP in the time-frequency domain. Classification results on a public dataset demonstrated that the proposed TFA-Net outperforms all the compared models, achieving an accuracy of 79.00% 0.27% and information transfer rate of 138.82 0.78 bits/min with a 1-s data length. TFA-Net represents a novel approach to SSVEP identification as well as time-frequency signal analysis, offering a calibration-free solution that enhances the generalizability and practicality of SSVEP based BCIs.},
}

@article {pmid40030518,
year = {2024},
author = {Meng, J and Li, S and Li, G and Luo, R and Sheng, X and Zhu, X},
title = {Improving Reliability of Life Applications Using Model-based Brain Switches via SSVEP.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3516733},
pmid = {40030518},
issn = {1558-2531},
abstract = {The brain switch improves the reliability of asynchronous brain-computer interface (aBCI) systems by switching the control state of the BCI system. Traditional brain switch research focuses on extracting advanced electroencephalography (EEG) features. However, a low signal-to-noise ratio (SNR) of EEG signals resulted in limited feature information and low performance of brain switches. Here, we design a virtual physical system to build the brain switch, allowing users to trigger the system through periodic brainwave modulation, fully integrating limited feature information and improving reliability. Furthermore, we designed multiple experiments to validate the effectiveness of the proposed brain switch based on steady-state visual evoked potentials (SSVEP). The results verified the performance of SSVEP brain switches based on virtual physical systems, improving the reliability of brain switches to 0.1FP/h or even better with acceptable triggering time and calibration-free for most subjects. This represents that the proposed virtual physical model-based brain switch can utilize SSVEP features and output the reliable commands required to control external devices, promoting BCI real applications.},
}

@article {pmid40030472,
year = {2024},
author = {Ke, Y and Chen, X and Xu, W and Wang, T and Shen, S and Ming, D},
title = {High-Frequency SSVEP-BCI with Row-Column Dual-Frequency Encoding and Decoding Strategy for Reduced Training Data.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3514794},
pmid = {40030472},
issn = {2168-2208},
abstract = {Steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) have the potential to be utilized in various fields due to their high accuracies and information transfer rates (ITR). High-frequency (HF) visual stimuli have shown promise in reducing visual fatigue and enhancing user comfort. However, these HF-SSVEP-BCIs often face limitations in the number of commands and typically require extensive individual training data to achieve high performance. In this study, we proposed a row-column dual-frequency encoding and decoding method using HF stimulation to develop a comfortable BCI system that supports multiple commands and reduces training costs. We arranged 20 targets in a matrix of five rows and four columns, with each target modulated by left-and-right field stimulation using two frequency-phase combinations. Targets in each row or column share a unique frequency-phase combination, allowing EEG data from the same row or column to be used collectively to train a row/column index decoding model for target identification. To evaluate the performance of our method, we constructed a 20-target asynchronous robotic arm control system with the adaptive window method. With only four training trials per target, the online system achieved an ITR of 105.14±14.15 bits/min, a true positive rate of 98.18±2.87%, a false positive rate of 7.39±6.73%, and a classification accuracy of 91.88±5.75%, with an average data length of 925.70±45.44 ms. These results indicate that the proposed protocol can deliver accurate and rapid command outputs for a comfortable SSVEP-based BCI with minimal training data and fewer frequencies.},
}

@article {pmid40030451,
year = {2024},
author = {Zhang, X and Wei, W and Qiu, S and Li, X and Wang, Y and He, H},
title = {Enhancing SSVEP-Based BCI Performance via Consensus Information Transfer Among Subjects.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2024.3506998},
pmid = {40030451},
issn = {2162-2388},
abstract = {The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has received considerable attention for its high communication speed. While large datasets provide an important opportunity to enhance decoding accuracies, the key challenge lies in the exploration of existing data to extract valuable information based on the distinctive characteristics of brain responses. In this study, we introduce ConsenNet, a framework designed to enhance SSVEP classification performance by leveraging information from the diverse perspectives of existing subjects. First, this study exploits the diversity of existing subjects to generate new samples, which retain both task-related components and variability. This effectively enhances the network generalization capability on new subjects. Second, the structured knowledge that encapsulates the interrelationships between categories has been constructed and then transferred from the teacher network to the student network, guiding the student network to extract invariant features across subjects. Finally, our model incorporates a small amount of new subject data for model calibration in the final stage. Offline experiments conducted on three public datasets demonstrate the superiority of ConsenNet over 19 methods compared in this study, while online experiments validate its feasibility for real-world applications.},
}

@article {pmid40030403,
year = {2024},
author = {Tan, J and Zhang, X and Wu, S and Song, Z and Wang, Y},
title = {Hidden Brain State-based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-machine Interfaces.},
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.2024.3503713},
pmid = {40030403},
issn = {1558-0210},
abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) assist paralyzed people in controlling neural prostheses without the need for real limb movement as supervised signals. The design of reward signal significantly impacts the learning efficiency of the RL-based decoders. Existing reward designs in the RL-based BMI framework rely on external rewards or manually labeled internal rewards, unable to accurately extract subjects' internal evaluation. In this paper, we propose a hidden brain state-based kernel inverse reinforcement learning (HBS-KIRL) method to accurately infer the subject-specific internal evaluation from neural activity during the BMI task. The state-space model is applied to project the neural state into low-dimensional hidden brain state space, which greatly reduces the exploration dimension. Then the kernel method is applied to speed up the convergence of policy, reward, and Q-value networks in reproducing kernel Hilbert space (RKHS). We tested our proposed algorithm on the data collected from the medial prefrontal cortex (mPFC) of rats when they were performing a two-lever-discrimination task. We assessed the state-value estimation performance of our proposed method and compared it with naïve IRL and PCA-based IRL. To validate that the extracted internal evaluation could contribute to the decoder training, we compared the decoding performance of decoders trained by different reward models, including manually designed reward, naïve IRL, PCA-IRL, and our proposed HBS-KIRL. The results show that the HBS-KIRL method can give a stable and accurate estimation of state-value distribution with respect to behavior. Compared with other methods, the decoder guided by HBS-KIRL achieves consistent and better decoding performance over days. This study reveals the potential of applying the IRL method to better extract subject-specific evaluation and improve the BMI decoding performance.},
}

@article {pmid40030380,
year = {2024},
author = {Chen, D and Cao, C and Gong, J and Huang, J and Xiao, J and Huang, Q and Guo, Y and Li, Y},
title = {Decoding Single-Pellet Retrieval Task From Local Field Potentials in Pre- and Post-Stroke Motor Areas: Insights Into Interhemispheric Connectivity Difference.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3499319},
pmid = {40030380},
issn = {1558-2531},
abstract = {OBJECTIVE: Intracortical brain-machine interfaces (iBMIs) hold promise for restoring communication and movement in stroke-paralyzed individuals. Recent studies have demonstrated the potential of using local field potentials (LFPs) for decoding single-pellet retrieval (SPR) tasks in iBMIs. However, most research has relied on LFPs from healthy rats rather than those affected by stroke. This study aimed to investigate the feasibility of utilizing LFPs from both the right and left (stroke) cortical forelimb areas (CFAs) for the SPR tasks decoding under both pre- and post-stroke conditions.

METHODS: LFPs were recorded via microelectrode arrays implanted into CFAs of eight rats trained to perform the SPR tasks. The relative spectral power method was used to represent frequency information, and random forest classification differentiated SPR tasks from resting states. We also assessed interhemispheric connectivity, including correlation, coherence, and phase-amplitude coupling (PAC), to compare differences between the SPR tasks and the resting states under both pre- and post-stroke conditions.

RESULTS: Our findings indicated that the relative PS method with LFPs achieves 87.10% 9.2% accuracy in post-stoke SPR decoding, where high gamma is crucial. Additionally, we observed changes in PACs from the right to the left sensorimotor cortex post-stroke during the SPR tasks compared to the resting states.

SIGNIFICANCE: Our work provides a comprehensive insight into the role of different frequency band from LFPs in motor function recovery mechanisms, highlighting the importance of the high gamma in motor function. This research lays the foundation for developing post-stoke SPR-related BMIs.},
}

@article {pmid40030325,
year = {2024},
author = {Saadatmand, H and Akbarzadeh-T, MR},
title = {Multiobjective Evolutionary Sequential Channel/ Feature Selection for EEG Motor Imagery Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3508277},
pmid = {40030325},
issn = {2168-2208},
abstract = {Motor imagery (MI) analysis from EEG signals constitutes a class of emerging brain-computer interface (BCI) applications that face EEG's predominant complexities arising from the multitude of channels and the vast number of possible features. This study presents a two-step multiobjective set-based integer-coded fuzzy-initialized evolutionary algorithm (MOSIFE) for efficient EEG-based MI signal analysis. The two-step process is a non-dominant wrapper strategy that sequentially identifies the optimal channels and the minimal set of features, thereby reducing MI's combinatorial search complexity. We also employ a reptile-based search algorithm (RSA), a recent metaheuristic for efficient search in multimodal continuous domains, to optimize the classifier's hyper-parameters. The proposed MOSIFE-RSA algorithm is benchmarked against 12 representative algorithms on four standard BCI Competition databases, including IV-I, III-IVa, III-IIIa, and II. The results show that MOSIFE-RSA improves accuracy by 20%, with channel selection contributing as much as 15% and feature selection as much as 5% towards these results. Furthermore, it reduces computational complexity by 81% through channel selection and 16% through feature selection, demonstrating its effectiveness in advancing EEG-based MI signal analysis. This research has practical implications for developing more accurate and efficient brain-computer interface systems.},
}

@article {pmid40030277,
year = {2024},
author = {Ding, Y and Li, Y and Sun, H and Liu, R and Tong, C and Liu, C and Zhou, X and Guan, C},
title = {EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3504604},
pmid = {40030277},
issn = {2168-2208},
abstract = {Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer (HCT) block that integrates a Fine-grained Temporal Learning (FTL) branch into Transformers, effectively discerning coarse-to-fine temporal patterns; and (2) a Dense Information Purification (DIP) module, which utilizes multi-level, purified temporal information to enhance decoding accuracy. Comprehensive experiments on three representative cognitive tasks-cognitive attention, driving fatigue, and mental workload detection-consistently confirm the generalizability of our proposed EEG-Deformer, demonstrating that it either outperforms or performs comparably to existing state-of-the-art methods. Visualization results show that EEG-Deformer learns from neurophysiologically meaningful brain regions for the corresponding cognitive tasks. The source code can be found at https://github.com/yi-ding-cs/EEG-Deformer.},
}

@article {pmid40030249,
year = {2024},
author = {Kokorin, K and Zehra, SR and Mu, J and Yoo, P and Grayden, DB and John, SE},
title = {Semi-Autonomous Continuous Robotic Arm Control Using an Augmented Reality Brain-Computer Interface.},
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.2024.3500217},
pmid = {40030249},
issn = {1558-0210},
abstract = {Noninvasive augmented-reality (AR) brain-computer interfaces (BCIs) that use steady-state visually evoked potentials (SSVEPs) typically adopt a fully-autonomous goal-selection framework to control a robot, where automation is used to compensate for the low information transfer rate of the BCI. This scheme improves task performance but users may prefer direct control (DC) of robot motion. To provide users with a balance of autonomous assistance and manual control, we developed a shared control (SC) system for continuous control of robot translation using an SSVEP AR-BCI, which we tested in a 3D reaching task. The SC system used the BCI input and robot sensor data to continuously predict which object the user wanted to reach, generated an assistance signal, and regulated the level of assistance based on prediction confidence. Eighteen healthy participants took part in our study and each completed 24 reaching trials using DC and SC. Compared to DC, SC significantly improved (paired two-tailed t-test, Holm-corrected α<0.05) mean task success rate (p<0.0001, μ=36.1%, 95% CI [25.3%, 46.9%]), normalised reaching trajectory length (p<0.0001, μ=-26.8%, 95% CI [-36.0%, -17.7%]), and participant workload (p=0.02, μ=-11.6, 95% CI [-21.1, -2.0]) measured with the NASA Task Load Index. Therefore, users of SC can control the robot effectively, while experiencing increased agency. Our system can personalise assistive technology by providing users with the ability to select their preferred level of autonomous assistance.},
}

@article {pmid40030248,
year = {2024},
author = {Ke, S and Yang, B and Qin, Y and Rong, F and Zhang, J and Zheng, Y},
title = {FACT-Net: a Frequency Adapter CNN with Temporal-periodicity Inception for Fast and Accurate MI-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.2024.3499998},
pmid = {40030248},
issn = {1558-0210},
abstract = {Motor imagery brain-computer interface (MI-BCI) based on non-invasive electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing decoding methods face significant challenges in terms of signal decoding accuracy, real-time processing, and deployment. To overcome these challenges, we propose FACT-Net, an innovative deep-learning network for the fast and accurate decoding of MI-EEG signals. FACT-Net incorporates a Frequency Adapter (FA) module designed for processing the frequency features of MI-EEG data, as well as a Temporal-Periodicity Inception (TPI) module specifically for handling global periodic signals in MI. To evaluate the proposed model, we conduct the experiments on the cross-day dataset collected from 67 subjects and the BCIC-IV-2a dataset. The FACT-Net achieved an accuracy of 48.32% and 80.67% higher than the state-of-the-art (SOTA) approaches, demonstrating excellent performance in MI decoding. Additionally, it exhibits exceptional memory efficiency and inference time, indicating significant potential for practical applications. We anticipate that FACT-Net will set a new baseline for MI-EEG decoding. The code is available in https://github.com/Ktn1ga/EEG_FACT.},
}

@article {pmid40030247,
year = {2024},
author = {Rao, Z and Zhu, J and Lu, Z and Zhang, R and Li, K and Guan, Z and Li, Y},
title = {A Wearable Brain-Computer Interface with Fewer EEG Channels for Online Motor Imagery Detection.},
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.2024.3502135},
pmid = {40030247},
issn = {1558-0210},
abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) have significant potential for neurorehabilitation and motor recovery. However, most BCI systems employ multi-channel electroencephalogram (EEG) recording devices, during which the pre-experimental preparation and post-experimental hair cleaning are time-consuming and inconvenient for stroke patients, and potentially affect their motivation for rehabilitation training. In this paper, we introduced a wearable MI-BCI system for online MI classification using a wireless headband device with four EEG channels to reduce setup time while enhancing portability. To validate the performance of the system in decoding MI-EEG signals, extensive experiments and comparisons were performed on sixty-six healthy subjects. Specifically, an offline and an online experiment with forty-six subjects were conducted, with the system achieving average offline and online accuracies of 85.21% and 76.54%, respectively. Furthermore, a comparison experiment involving another twenty subjects showed that the online performance of our headband device (77.84%) was comparable to that of a mature commercial Neuroscan device (76.50%). Compared to several existing portable systems, our wearable system achieved superior performance with fewer channels and was validated on a larger number of subjects. These results demonstrated that our wearable BCI system can reduce preparation time, enhance portability, and meet the classification performance requirements for BCI-based rehabilitation intervention, indicating its substantial potential for large-scale clinical applications in enhancing motor recovery of stroke patients.},
}

@article {pmid40030217,
year = {2024},
author = {Xu, M and Jiao, J and Chen, D and Ding, Y and Chen, Q and Wu, J and Gu, P and Pan, Y and Peng, X and Xiao, N and Yang, B and Li, Q and Guo, J},
title = {REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3498916},
pmid = {40030217},
issn = {2168-2208},
abstract = {High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.},
}

@article {pmid40030141,
year = {2025},
author = {Pankka, H and Lehtinen, J and Ilmoniemi, RJ and Roine, T},
title = {Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.},
journal = {Neural computation},
volume = {},
number = {},
pages = {1-22},
doi = {10.1162/neco_a_01743},
pmid = {40030141},
issn = {1530-888X},
abstract = {Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task; however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies 150 ms ahead, with mean absolute errors of 1.0 $\pm$ 1.1 $\mu$V (theta) and 0.9 $\pm$ 1.1 $\mu$V (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be used to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.},
}

@article {pmid40027274,
year = {2024},
author = {Cai, Y and Li, Q and Wesselmann, U and Zhao, C},
title = {Exosomal Bupivacaine: Integrating Nerve Barrier Penetration Capability and Sustained Drug Release for Enhanced Potency in Peripheral Nerve Block and Reduced Toxicity.},
journal = {Advanced functional materials},
volume = {34},
number = {42},
pages = {},
pmid = {40027274},
issn = {1616-301X},
abstract = {Peripherally injected local anesthetics exhibit limited ability to penetrate peripheral nerve barriers (PNBs), which limits their effectiveness in peripheral nerve block and increases the risk of adverse effects. In this work, we demonstrated that exosomes derived from Human Embryo Kidney (HEK) 293 cells can effectively traverse the perineurium, which is the rate-limiting barrier within PNBs that local anesthetics need to cross before acting on axons. Based on this finding, we use these exosomes as a carrier for bupivacaine (BUP), a local anesthetic commonly used in clinical settings. The in vitro assessments revealed that the prepared exosomal bupivacaine (BUP@EXO) achieves a BUP loading capacity of up to 82.33% and sustained release of BUP for over 30 days. In rats, a single peripheral injection of BUP@EXO, containing 0.75 mg of BUP, which is ineffective for BUP alone, induced a 2-hour sensory nerve blockade without significant motor impairments. Increasing the BUP dose in BUP@EXO to 2.5 mg, a highly toxic dose for BUP alone, extended the sensory nerve blockade to 12 hours without causing systemic cardiotoxicity and local neurotoxicity and myotoxicity.},
}

@article {pmid40026891,
year = {2025},
author = {Moreno-Alcayde, Y and Ruotsalo, T and Leiva, LA and Traver, VJ},
title = {Brainsourcing for temporal visual attention estimation.},
journal = {Biomedical engineering letters},
volume = {15},
number = {2},
pages = {311-326},
pmid = {40026891},
issn = {2093-985X},
abstract = {The concept of temporal visual attention in dynamic contents, such as videos, has been much less studied than its spatial counterpart, i.e., visual salience. Yet, temporal visual attention is useful for many downstream tasks, such as video compression and summarisation, or monitoring users' engagement with visual information. Previous work has considered quantifying a temporal salience score from spatio-temporal user agreements from gaze data. Instead of gaze-based or content-based approaches, we explore to what extent only brain signals can reveal temporal visual attention. We propose methods for (1) computing a temporal visual salience score from salience maps of video frames; (2) quantifying the temporal brain salience score as a cognitive consistency score from the brain signals from multiple observers; and (3) assessing the correlation between both temporal salience scores, and computing its relevance. Two public EEG datasets (DEAP and MAHNOB) are used for experimental validation. Relevant correlations between temporal visual attention and EEG-based inter-subject consistency were found, as compared with a random baseline. In particular, effect sizes, measured with Cohen's d, ranged from very small to large in one dataset, and from medium to very large in another dataset. Brain consistency among subjects watching videos unveils temporal visual attention cues. This has relevant practical implications for analysing attention for visual design in human-computer interaction, in the medical domain, and in brain-computer interfaces at large.},
}

@article {pmid40026370,
year = {2025},
author = {Pišot, R and Marušič, U and Šlosar, L},
title = {Addressing the Paradox of Rest with Innovative Technologies.},
journal = {Zdravstveno varstvo},
volume = {64},
number = {2},
pages = {68-72},
pmid = {40026370},
issn = {0351-0026},
abstract = {The paradox of rest lies in its dual nature: essential for recovery yet potentially harmful when prolonged. Prolonged physical inactivity (PI) significantly contributes to non-communicable diseases (NCDs). Studies show nearly a third of adults worldwide were insufficiently active in 2022, with the economic costs of PI projected to reach INT$520 billion by 2030. Bedrest models have illuminated the rapid onset of insulin resistance, general functional decline and muscle atrophy associated with PI, particularly in hospitalised older adults. Innovative technologies, such as extended reality (XR), offer promising solutions for mitigating the effects of PI and can enhance non-physical rehabilitation techniques such as motor imagery and action observation. These technologies provide immersive, personalised therapeutic experiences that engage multiple senses, transforming passive recovery into an active process and addressing both the physical and cognitive consequences of inactivity. Results of bedrest study showed significant preservation of muscle mass, improved strength and enhanced insulin sensitivity in the intervention group compared to controls. These findings highlight the potential of XR-based strategies in addressing structural and functional declines during inactivity. As part of the Interreg VI-A Italia-Slovenija project X-BRAIN.net, advanced XR-equipped active rooms were developed to aid post-stroke rehabilitation in acute care settings. XR technologies, particularly VR, have shown promise in providing dynamic and adaptable therapeutic environments that facilitate early and targeted interventions. Future advancements focus on integrating XR with brain-computer interfaces (BCIs) and synchronised visual-haptic neurofeedback, enhancing sensorimotor cortical activation and improving rehabilitation outcomes. Comprehensive multimodal approaches, including nutritional, physical and non-physical interventions, are emerging as effective strategies to personalise and optimise patient recovery.},
}

@article {pmid40026248,
year = {2025},
author = {Ge, Q and Yang, J and Huang, F and Dai, X and Chen, C and Guo, J and Wang, M and Zhu, M and Shao, Y and Xia, Y and Zhou, Y and Peng, J and Deng, S and Shi, J and Hu, Y and Zhang, H and Wang, Y and Wang, X and Li, XM and Chen, Z and Shu, Y and Zhu, JM and Zhang, J and Shen, Y and Duan, S and Xu, S and Shen, L and Chen, J},
title = {Multimodal single-cell analyses reveal molecular markers of neuronal senescence in human drug-resistant epilepsy.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {5},
pages = {},
doi = {10.1172/JCI188942},
pmid = {40026248},
issn = {1558-8238},
mesh = {Humans ; *Single-Cell Analysis ; *Cellular Senescence ; Mice ; Animals ; *Drug Resistant Epilepsy/metabolism/pathology/genetics ; Male ; Pyramidal Cells/metabolism/pathology ; Female ; Biomarkers/metabolism ; Cyclin-Dependent Kinase Inhibitor p21/metabolism/genetics ; },
abstract = {The histopathological neurons in the brain tissue of drug-resistant epilepsy exhibit aberrant cytoarchitecture and imbalanced synaptic circuit function. However, the gene expression changes of these neurons remain unknown, making it difficult to determine the diagnosis or to dissect the mechanism of drug-resistant epilepsy. By integrating whole-cell patch clamp recording and single-cell RNA-seq approaches, we identified a transcriptionally distinct subset of cortical pyramidal neurons. These neurons highly expressed genes CDKN1A (P21), CCL2, and NFKBIA, which associate with mTOR pathway, inflammatory response, and cellular senescence. We confirmed the expression of senescent marker genes in a subpopulation of cortical pyramidal neurons with enlarged soma size in the brain tissue of drug-resistant epilepsy. We further revealed the expression of senescent cell markers P21, P53, COX2, γ-H2AX, and β-Gal, and reduction of nuclear integrity marker Lamin B1 in histopathological neurons in the brain tissue of patients with drug-resistant epilepsy with different pathologies, but not in control brain tissue with no history of epilepsy. Additionally, chronic, but not acute, epileptic seizures induced senescent marker expression in cortical neurons in mouse models of drug-resistant epilepsy. These results provide important molecular markers for histopathological neurons and what we believe to be new insights into the pathophysiological mechanisms of drug-resistant epilepsy.},
}

Humans
*Single-Cell Analysis
*Cellular Senescence
Mice
Animals
*Drug Resistant Epilepsy/metabolism/pathology/genetics
Male
Pyramidal Cells/metabolism/pathology
Female
Biomarkers/metabolism
Cyclin-Dependent Kinase Inhibitor p21/metabolism/genetics

@article {pmid40025635,
year = {2025},
author = {Chen, J and Cheng, Y and Chen, L and Yang, B},
title = {Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off-the-shelf features: A dual-dataset study.},
journal = {Journal of applied clinical medical physics},
volume = {},
number = {},
pages = {e70061},
doi = {10.1002/acm2.70061},
pmid = {40025635},
issn = {1526-9914},
abstract = {BACKGROUND: This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image-based HPV prediction methods are hindered by high computational demands or suboptimal performance.

METHODS: To address these issues, we propose a methodology that employs a Siamese Neural Network architecture, integrating multi-modality off-the-shelf features-handcrafted features and 3D deep features-to enhance the representation of information. We assessed the incremental benefit of combining 3D deep features from various networks and introduced manufacturer normalization. Our method was also designed for computational efficiency, utilizing transfer learning and allowing for model execution on a single-CPU platform. A substantial dataset comprising 1453 valid samples was used as internal validation, a separate independent dataset for external validation.

RESULTS: Our proposed model achieved superior performance compared to other methods, with an average area under the receiver operating characteristic curve (AUC) of 0.791 [95% (confidence interval, CI), 0.781-0.809], an average recall of 0.827 [95% CI, 0.798-0.858], and an average accuracy of 0.741 [95% CI, 0.730-0.752], indicating promise for clinical application. In the external validation, proposed method attained an AUC of 0.581 [95% CI, 0.560-0.603] and same network architecture with pure deep features achieved an AUC of 0.700 [95% CI, 0.682-0.717]. An ablation study confirmed the effectiveness of incorporating manufacturer normalization and the synergistic effect of combining different feature sets.

CONCLUSION: Overall, our proposed model not only outperforms existing counterparts for HPV status prediction but is also computationally accessible for use on a single-CPU platform, which reduces resource requirements and enhances clinical usability.},
}

@article {pmid40025160,
year = {2025},
author = {Ye, Z and Ai, Q and Liu, Y and de Rijke, M and Zhang, M and Lioma, C and Ruotsalo, T},
title = {Generative language reconstruction from brain recordings.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {346},
pmid = {40025160},
issn = {2399-3642},
support = {CHIST-ERA-20-BCI-001//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging ; *Language ; Male ; Female ; Adult ; Brain Mapping/methods ; Young Adult ; },
abstract = {Language reconstruction from non-invasive brain recordings has been a long-standing challenge. Existing research has addressed this challenge with a classification setup, where a set of language candidates are pre-constructed and then matched with the representation decoded from brain recordings. Here, we propose a method that addresses language reconstruction through auto-regressive generation, which directly uses the representation decoded from functional magnetic resonance imaging (fMRI) as the input for a large language model (LLM), mitigating the need for pre-constructed candidates. While an LLM can already generate high-quality content, our approach produces results more closely aligned with the visual or auditory language stimuli in response to which brain recordings are sampled, especially for content deemed "surprising" for the LLM. Furthermore, we show that the proposed approach can be used in an auto-regressive manner to reconstruct a 10 min-long language stimulus. Our method outperforms or is comparable to previous classification-based methods under different task settings, with the added benefit of estimating the likelihood of generating any semantic content. Our findings demonstrate the effectiveness of employing brain language interfaces in a generative setup and delineate a powerful and efficient means for mapping functional representations of language perception in the brain.},
}

Humans
*Magnetic Resonance Imaging
*Brain/physiology/diagnostic imaging
*Language
Male
Female
Adult
Brain Mapping/methods
Young Adult

@article {pmid40022809,
year = {2025},
author = {Luo, TJ and Wu, T},
title = {Sum of similarity-regularized squared correlations for enhancing SSVEP detection.},
journal = {Artificial intelligence in medicine},
volume = {162},
number = {},
pages = {103100},
doi = {10.1016/j.artmed.2025.103100},
pmid = {40022809},
issn = {1873-2860},
abstract = {A brain-computer interface (BCI) provides a direct control pathway between human brain and external devices. Steady-state visual evoked potential based BCI (SSVEP-BCI) has been proven to be a valuable solution due to its advantages of high information transfer rate (ITR) and minimal calibration requirement. Recently, some methods have been proposed based on calibration-training techniques to compute optimal spatial filters from covariances, and have achieved good detection performance. However, these methods ignore the temporally-varying and spatially-coupled characteristics of the EEG signals, which is essentially an important clue for enhancing ITR. More importantly, existing methods cannot well deal with intrinsic noise components of electroencephalogram (EEG) signals, greatly affecting their detection performance. In this paper, we propose a novel method, termed as Sum of Similarity-Regularized Squared Correlations (SSRSC), which is extended and regularized from the sum of squared correlations. We simultaneously compute the squared correlations for both calibration data and sine-cosine harmonics templates, and mitigate variations by the similarity regularization. Moreover, we extend the SSRSC by adopting the ranking weighted ensemble strategy, termed as weSSCOR. Extensive experiments have been conducted on two benchmark SSVEP datasets, and the results demonstrated that the proposed SSRSC/weSSRSC can significantly improve accuracy and ITR of SSVEP detection with less calibration data, which has great potential in designing high ITR SSVEP-BCIs with less calibration efforts.},
}

@article {pmid40020404,
year = {2025},
author = {Wang, R and Fang, T and Zhang, Y and Cheng, Y and Wang, C and Chen, Y and Fan, Q and Zhao, X and Ming, D},
title = {The overgrowth of structure-function coupling in premature brain during infancy.},
journal = {Developmental cognitive neuroscience},
volume = {72},
number = {},
pages = {101535},
doi = {10.1016/j.dcn.2025.101535},
pmid = {40020404},
issn = {1878-9307},
abstract = {Although the rapid growth of brain structure and function during infancy has been well documented, relatively little is known about how these two developmental processes couple-an aspect that exhibits distinct patterns in adult brain. In this study, the multimodal MRI data from the dHCP database were used to investigate the coupling between brain structure and function in infants, with a particular focus on how prematurity influences this relationship. A similar pattern of the coupling distribution between preterm and full-term infants was identified with coupling index varying across unimodal cortices such as visual and sensorimotor regions and transmodal cortices including default mode network. Notably, a widespread overgrowth of structure-function coupling and a slow developmental trajectory towards full-term infants in preterm infants at term-equivalent age were found. Collectively, the study quantified the development of structure-function relationships in preterm infants, offering new insights into the information transmission processes and developmental patterns of the early-life brain.},
}

@article {pmid40014925,
year = {2025},
author = {Dold, M and Pereira, J and Sajonz, B and Coenen, VA and Thielen, J and Janssen, M and Tangermann, M},
title = {Dareplane: a modular open-source software platform for BCI research with application in closed-loop deep brain stimulation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbb20},
pmid = {40014925},
issn = {1741-2552},
abstract = {Objective - This work introduces Dareplane, a modular and broad technology- agnostic open source software platform for brain-computer interface research with an application focus on adaptive deep brain stimulation (aDBS). One difficulty for investigating control approaches for aDBS resides with the complex setups required for aDBS experiments, a challenge Dareplane tries to address. Approach - The key features of the platform are presented and the composition of modules into a full experimental setup is discussed in the context of a Python- based orchestration module. The performance of a typical experimental setup on Dareplane for aDBS is evaluated in three benchtop experiments, covering (a) an easy- to-replicate setup using an Arduino microcontroller, (b) a setup with hardware of an implantable pulse generator, and (c) a setup using an established and CE certified external neurostimulator. The full technical feasibility of the platform in the aDBS context is demonstrated in a first closed-loop session with externalized leads on a patient with Parkinson's disease receiving DBS treatment and further in a non-invasive BCI speller application using code-modulated visual evoked responses (c-VEP). Main results - The platform is implemented and open-source accessible on https://github.com/bsdlab/Dareplane. Benchtop results show that performance of the platform is sufficient for current aDBS latencies, and the platform could successfully be used in the aDBS experiment. The timing-critical c-VEP speller could be successfully implemented on the platform achieving expected information transfer rates. Significance - The Dareplane platform supports aDBS setups, and more generally the research on neurotechnological systems such as brain-computer interfaces. It provides a modular, technology-agnostic, and easy-to-implement software platform to make experimental setups more resilient and replicable. Clinical trial number - DRKS000287039.},
}

@article {pmid40013095,
year = {2025},
author = {Yuan, Z and Huang, Z and Li, C and Li, S and Ren, Q and Xia, X and Jiang, Q and Zhang, D and Zhu, Q and Meng, X},
title = {Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease.},
journal = {Frontiers in aging neuroscience},
volume = {17},
number = {},
pages = {1527323},
pmid = {40013095},
issn = {1663-4365},
abstract = {OBJECTIVES: To propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.

METHODS: The performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity.

RESULTS: Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).

CONCLUSION: The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.},
}

@article {pmid40011760,
year = {2025},
author = {Qin, R and Zhang, Y and Shi, J and Wu, P and An, C and Li, Z and Liu, N and Wan, Z and Hua, T and Li, X and Lou, J and Yin, W and Chen, W},
title = {TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity.},
journal = {Cell research},
volume = {},
number = {},
pages = {},
pmid = {40011760},
issn = {1748-7838},
support = {T2394511//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31971237//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12172371//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2394512//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32101052//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12102389//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272216//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32090044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272348//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31600751//National Natural Science Foundation of China (National Science Foundation of China)/ ; KJ2070000094//Chinese Academy of Sciences (CAS)/ ; KY9100000092//University of Science and Technology of China (USTC)/ ; },
abstract = {Naturally evolved T-cell receptors (TCRs) exhibit remarkably high specificity in discriminating non-self antigens from self-antigens under dynamic biomechanical modulation. In contrast, engineered high-affinity TCRs often lose this specificity, leading to cross-reactivity with self-antigens and off-target toxicity. The underlying mechanism for this difference remains unclear. Our study reveals that natural TCRs exploit mechanical force to form optimal catch bonds with their cognate antigens. This process relies on a mechanically flexible TCR-pMHC binding interface, which enables force-enhanced CD8 coreceptor binding to MHC-α1α2 domains through sequential conformational changes induced by force in both the MHC and CD8. Conversely, engineered high-affinity TCRs create rigid, tightly bound interfaces with cognate pMHCs of their parental TCRs. This rigidity prevents the force-induced conformational changes necessary for optimal catch-bond formation. Paradoxically, these high-affinity TCRs can form moderate catch bonds with non-stimulatory pMHCs of their parental TCRs, leading to off-target cross-reactivity and reduced specificity. We have also developed comprehensive force-dependent TCR-pMHC kinetics-function maps capable of distinguishing functional and non-functional TCR-pMHC pairs and identifying toxic, cross-reactive TCRs. These findings elucidate the mechano-chemical basis of the specificity of natural TCRs and highlight the critical role of CD8 in targeting cognate antigens. This work provides valuable insights for engineering TCRs with enhanced specificity and potency against non-self antigens, particularly for applications in cancer immunotherapy and infectious disease treatment, while minimizing the risk of self-antigen cross-reactivity.},
}

@article {pmid40010648,
year = {2025},
author = {Spadacenta, S and Dicke, PW and Thier, P},
title = {Minimally invasive electrocorticography (ECoG) recording in common marmosets.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110409},
doi = {10.1016/j.jneumeth.2025.110409},
pmid = {40010648},
issn = {1872-678X},
abstract = {BACKGROUND: Electrocorticography (ECoG) provides a valuable compromise between spatial and temporal resolution for recording brain activity with excellent signal quality, crucial for presurgical epilepsy mapping and advancing neuroscience, including brain-machine interface development. ECoG is particularly effective in the common marmoset (Callithrix jacchus), whose lissencephalic (unfolded) brain surface provides broad cortical access. One of the key advantages of ECoG recordings is the ability to study interactions between distant brain regions. Traditional methods rely on large electrode arrays, necessitating extensive trepanations and a trade-off between size and electrode spacing.

NEW METHOD: This study introduces a refined ECoG technique for examining interactions among multiple cortical areas in marmosets, combining circumscribed trepanations with high-density electrode arrays at specific sites of interest.

Standard ECoG techniques typically require large electrode arrays and extensive trepanation, which heighten surgical risks and the likelihood of infection, while potentially compromising spatial resolution. In contrast, our method facilitates detailed and stable recordings across multiple cortical areas with minimized invasiveness and reduced complication risks, all while preserving high spatial resolution.

RESULTS: Two adult marmosets underwent ECoG implantation in frontal, temporal, and parietal regions. Postoperative monitoring confirmed rapid recovery, long-term health, and stable, high-quality neural recordings during various behavioral tasks.

CONCLUSIONS: This refined ECoG method enhances the study of cortical interactions in marmosets while minimizing surgical invasiveness and complication risks. It offers potential for broader application in other species and opens new avenues for long-term data collection, ultimately advancing both neuroscience and brain-machine interface research.},
}

@article {pmid40010602,
year = {2025},
author = {OuYang, Z and Yang, R and Wang, Y},
title = {Hotspots and Trends in Spinal Cord Stimulation Research for Spinal Cord Injury: A Bibliometric Analysis with Emphasis on Motor Function Recovery (2014-2024).},
journal = {World neurosurgery},
volume = {},
number = {},
pages = {123832},
doi = {10.1016/j.wneu.2025.123832},
pmid = {40010602},
issn = {1878-8769},
abstract = {BACKGROUND: Spinal cord stimulation (SCS) has emerged as a key therapeutic strategy for enhancing motor recovery in spinal cord injury (SCI). This study employs bibliometric analysis to explore research trends and hotspots in SCS for motor recovery, highlighting advances and emerging directions over the past decade.

METHODS: This cross-sectional bibliometric study retrieved publications on SCS for motor recovery from the Web of Science Core Collection database (2014-2024). Key information, including annual publication trends, contributing countries, institutions, authors, journals, keywords, and highly cited references, was analyzed using CiteSpace and VOSviewer.

RESULTS: A total of 1,033 publications were analyzed, demonstrating exponential growth in SCS research since 2014. The United States and Switzerland were identified as leading contributors, with prominent institutions such as the Swiss Federal Institute of Technology and the University of California System driving advancements. Key authors included Grégoire Courtine and Susan J. Harkema. Research themes have evolved through four phases: foundational studies on spinal cord mechanisms, exploration of neural circuits, application of electrical stimulation for motor recovery, and advancements in non-invasive therapies such as transcutaneous SCS. Highly cited journals, including Nature and Lancet, have published transformative studies, underscoring the field's clinical and academic significance.

CONCLUSIONS: This bibliometric analysis provides a comprehensive overview of SCS research for motor recovery post-SCI over the past decade. Interdisciplinary collaboration and technological innovation have positioned SCS as a cornerstone of SCI rehabilitation. Future efforts should focus on optimizing approaches, leveraging advanced imaging and AI technologies, and broadening rehabilitation goals to improve outcomes for SCI patients.},
}

@article {pmid40009882,
year = {2025},
author = {Ding, L and Zou, Q and Zhu, J and Wang, Y and Yang, Y},
title = {Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adba8d},
pmid = {40009882},
issn = {1741-2552},
abstract = {OBJECTIVE: Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.

APPROACH: Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.

MAIN RESULTS: We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.

SIGNIFICANCE: Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.},
}

@article {pmid40009879,
year = {2025},
author = {Yan, Y and Li, J and Yin, M},
title = {EEG-based recognition of hand movement and its parameter.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adba8a},
pmid = {40009879},
issn = {1741-2552},
abstract = {Brain-computer interface (BCI) is a cutting-edge technology that enables interaction with external devices by decoding human intentions, and is highly valuable in the fields of medical rehabilitation and human-robot collaboration. The technique of decoding motor intent for motor execution (ME) based on electroencephalographic (EEG) signals is in the feasibility study stage. There are still insufficient studies on the accuracy of motor execution EEG signal recognition in between-subjects classification to reach the level of realistic applications. This paper aims to investigate EEG signal-driven hand movement recognition by analyzing low-frequency time-domain (LFTD) information. Experiments with four types of hand movements, two force parameter (extraction and pushing) tasks, and a four-target directional displacement task were designed and executed, and the EEG data from thirteen healthy volunteers was collected. Sliding window approach is used to expand the dataset in order to address the issue of EEG signal overfitting. Furtherly, CNN-BiLSTM model, an end-to-end serial combination of a Bidirectional Long Short-Term Memory Network (BiLSTM) and Convolutional Neural Network (CNN) is constructed to classify the raw EEG data to recognize the hand movement. According to experimental data, the model is able to categorize four types of hand movements, extraction movements, pushing movements, and four target direction displacement movements with an accuracy of 99.14%±0.49%, 99.29%±0.11%, 99.23%±0.60%, and 98.11%± 0.23%, respectively. Furthermore, comparative tests conducted with alternative deep learning models (LSTM, CNN, EEGNet, CNN-LSTM) demonstrates that the CNN-BiLSTM model is with practicable accuracy in terms of EEG-based hand movement recognition and its parameter decoding.},
}

@article {pmid40008568,
year = {2025},
author = {Jelescu, IO and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Schilling, KG},
title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging.},
journal = {Magnetic resonance in medicine},
volume = {},
number = {},
pages = {},
doi = {10.1002/mrm.30429},
pmid = {40008568},
issn = {1522-2594},
abstract = {Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.},
}

@article {pmid40008460,
year = {2025},
author = {Schilling, KG and Howard, AFD and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Jelescu, IO},
title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography.},
journal = {Magnetic resonance in medicine},
volume = {},
number = {},
pages = {},
doi = {10.1002/mrm.30424},
pmid = {40008460},
issn = {1522-2594},
support = {2020 BP 00117//Government of Catalonia/ ; P30DA048742/DA/NIDA NIH HHS/United States ; R01EB031765/EB/NIBIB NIH HHS/United States ; R56EB031765/EB/NIBIB NIH HHS/United States ; K01EB032898/NH/NIH HHS/United States ; R01AG057991/NH/NIH HHS/United States ; R01CA160620/NH/NIH HHS/United States ; R01EB017230/NH/NIH HHS/United States ; R01EB019980/NH/NIH HHS/United States ; R01EB031954/NH/NIH HHS/United States ; R01NS109090/NH/NIH HHS/United States ; R01NS125020/NH/NIH HHS/United States ; //NSERC/ ; //Research Center of Excellence of the University of Antwerp/ ; FWO//Research Foundation Flanders/ ; 12M3119N//Research Foundation Flanders/ ; LCF/BQ/PR22/11920010//la Caixa/ ; 32454//Canada Foundation for Innovation/ ; 34824//Canada Foundation for Innovation/ ; FDN-143263//CIHR/ ; CIHRFDN-143263//Canadian Institute of Health Research/ ; 101044180/ERC_/European Research Council/International ; /SNSF_/Swiss National Science Foundation/Switzerland ; 5886,35450//Quebec BioImaging Network/ ; RGPIN-2019-07244//Natural Sciences and Engineering Research Council of Canada/ ; 322736//Fonds de Recherche du Québec - Santé/ ; 202788/Z/16/A/WT_/Wellcome Trust/United Kingdom ; 203139/A/16/Z/WT_/Wellcome Trust/United Kingdom ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; PCEFP2_194260//Eccellenza Fellowship/ ; },
abstract = {Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.},
}

@article {pmid40007882,
year = {2025},
author = {Crell, MR and Kostoglou, K and Sterk, K and Müller-Putz, GR},
title = {A novel paradigm for fast training data generation in asynchronous movement-based BCIs.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1540155},
pmid = {40007882},
issn = {1662-5161},
abstract = {INTRODUCTION: Movement-based brain-computer interfaces (BCIs) utilize brain activity generated during executed or attempted movement to provide control over applications. By relying on natural movement processes, these BCIs offer a more intuitive control compared to other BCI systems. However, non-invasive movement-based BCIs utilizing electroencephalographic (EEG) signals usually require large amounts of training data to achieve suitable accuracy in the detection of movement intent. Additionally, patients with movement impairments require cue-based paradigms to indicate the start of a movement-related task. Such paradigms tend to introduce long delays between trials, thereby extending training times. To address this, we propose a novel experimental paradigm that enables the collection of 300 cued movement trials in 18 min.

METHODS: By obtaining measurements from ten participants, we demonstrate that the data produced by this paradigm exhibits characteristics similar to those observed during self-paced movement.

RESULTS AND DISCUSSION: We also show that classifiers trained on this data can be used to accurately detect executed movements with an average true positive rate of 31.8% at a maximum rate of 1.0 false positives per minute.},
}

@article {pmid40007617,
year = {2025},
author = {Wu, H and Feng, E and Yin, H and Zhang, Y and Chen, G and Zhu, B and Yue, X and Zhang, H and Liu, Q and Xiong, L},
title = {Biomaterials for neuroengineering: applications and challenges.},
journal = {Regenerative biomaterials},
volume = {12},
number = {},
pages = {rbae137},
pmid = {40007617},
issn = {2056-3418},
abstract = {Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.},
}

@article {pmid40007259,
year = {2025},
author = {Sirakov, A and Ninov, K and Sirakova, K and Sirakov, SS},
title = {Blazing the trail! Commentary on "Intra-arterial lidocaine administration in middle meningeal artery for short-term treatment of subarachoid hemorrhage-related headaches" by Qureshi et al.},
journal = {Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences},
volume = {},
number = {},
pages = {15910199251324039},
doi = {10.1177/15910199251324039},
pmid = {40007259},
issn = {2385-2011},
abstract = {In their recently published INR study, Qureshi et al. present their results on intra-arterial lidocaine administration in the middle meningeal artery for the short-term treatment of subarachnoid hemorrhage (SAH)-related headaches. The authors demonstrate that their proposed intra-arterial treatment consistently alleviates headaches in patients with SAH. The purpose of this commentary is to commend the authors on their paper and the notable results they have achieved. It is always pleasant to encounter studies that not only make it to the "Latest Online" section of neurointerventional journals but also push the boundaries, advancing our understanding and care for patients in the most meaningful ways. There is no doubt that our field has witnessed remarkable progress and an expanding spectrum of interventions that endovascular neuroservices can offer. Several therapeutic approaches have emerged from similarly constructive articles, including intra-arterial chemotherapy for malignant cerebral tumors, innovative treatments for cerebrospinal fluid-venous fistulas, hydrocephalus, and chronic subdural hematomas, as well as the implantation of brain-computer interface devices.},
}

@article {pmid40006451,
year = {2025},
author = {Zaidi, SR and Khan, NA and Hasan, MA},
title = {Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {4},
pages = {},
doi = {10.3390/s25041222},
pmid = {40006451},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Emotions/physiology ; *Machine Learning ; Adult ; *Guilt ; Young Adult ; Neurosciences/methods ; Brain/physiology ; Support Vector Machine ; Sex Factors ; },
abstract = {This study explores the link between the emotion "guilt" and human EEG data, and investigates the influence of gender differences on the expression of guilt and neutral emotions in response to visual stimuli. Additionally, the stimuli used in the study were developed to ignite guilt and neutral emotions. Two emotions, "guilt" and "neutral", were recorded from 16 participants after these emotions were induced using storyboards as pictorial stimuli. These storyboards were developed based on various guilt-provoking events shared by another group of participants. In the pre-processing step, collected data were de-noised using bandpass filters and ICA, then segmented into smaller sections for further analysis. Two approaches were used to feed these data to the SVM classifier. First, the novel approach employed involved feeding the data to SVM classifier without computing any features. This method provided an average accuracy of 83%. In the second approach, data were divided into Alpha, Beta, Gamma, Theta and Delta frequency bands using Discrete Wavelet Decomposition. Afterward, the computed features, including entropy, Hjorth parameters and Band Power, were fed to SVM classifiers. This approach achieved an average accuracy of 63%. The findings of both classification methodologies indicate that females are more expressive in response to depicted stimuli and that their brain cells exhibit higher feature values. Moreover, females displayed higher accuracy than males in all bands except the Delta band.},
}

Humans
*Electroencephalography/methods
Male
Female
*Emotions/physiology
*Machine Learning
Adult
*Guilt
Young Adult
Neurosciences/methods
Brain/physiology
Support Vector Machine
Sex Factors

@article {pmid40006375,
year = {2025},
author = {Wu, Y and Cao, P and Xu, M and Zhang, Y and Lian, X and Yu, C},
title = {Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {4},
pages = {},
doi = {10.3390/s25041147},
pmid = {40006375},
issn = {1424-8220},
support = {CEIEC-2023-ZM02-0090//Industrial Internet identification analysis System/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology ; },
abstract = {Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial-spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time-spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain-computer interface (BCI) systems.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
*Neural Networks, Computer
Imagination/physiology
Signal Processing, Computer-Assisted
Algorithms
Brain/physiology

@article {pmid40003400,
year = {2025},
author = {Teteh-Brooks, DK and Ericson, M and Bethea, TN and Dawkins-Moultin, L and Sarkaria, N and Bailey, J and Llanos, AAM and Montgomery, S},
title = {Validating the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) Among Black Breast Cancer Survivors.},
journal = {International journal of environmental research and public health},
volume = {22},
number = {2},
pages = {},
doi = {10.3390/ijerph22020174},
pmid = {40003400},
issn = {1660-4601},
mesh = {Humans ; *Breast Neoplasms ; Female ; *Cancer Survivors/psychology/statistics & numerical data ; Middle Aged ; *Hair Preparations ; Adult ; *Black or African American ; Surveys and Questionnaires ; Aged ; White ; },
abstract = {UNLABELLED: Personal care products containing toxic chemicals (e.g., endocrine-disrupting chemicals) may increase breast cancer risk, especially for Black women who use these products more than other racial groups. There are limited tools that examine the intersections of identity, behaviors, and attitudes surrounding product use, perceived safety, and breast cancer risk; thus, the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) was developed to bridge this gap. While initial validations lacked diverse survivor representation, this study seeks to validate the BHBS among Black survivors.

METHODS: This study is a part of the Bench to Community Initiative (BCI), where respondents (n = 167) completed a 41-item survey including the BHBS between 2020 and 2022. The use of Principal Component Analysis (PCA) and confirmatory factor analysis (CFA) established the underlying component structures and model fit. CFA measures used to confirm component structures included the Root Mean Square Error of Approximation, the Comparative Fit Index, and the Tucker Lewis Index.

RESULTS: Black survivors on average were diagnosed with breast cancer before age 40 (37.41 ± 8.8) with Stage 1 (45%) disease. Sixty-three percent of the total variance resulted in a two-component structure. Subscale 1 (S1) measures the sociocultural perspectives about hair and identity (28% of the total variance; α = 0.73; 95% CI = 0.71-0.82). Subscale 2 (S2) can be used to assess perceived breast cancer risk related to hair product use (35% of the total variance; α = 0.86; 95% CI = 0.81-0.94). The two-component structure was confirmed with Root Mean Square Error of Approximation = 0.034, Comparative Fit Index = 0.93, and Tucker Lewis Index = 0.89.

DISCUSSION/CONCLUSIONS: The BHBS is a valid tool to measure identity, attitudes, and behaviors about product use and breast cancer risk among survivors. Hair is a significant cultural identity expression, and the health effects of styling products should be considered in future interventions.},
}

Humans
*Breast Neoplasms
Female
*Cancer Survivors/psychology/statistics & numerical data
Middle Aged
*Hair Preparations
Adult
*Black or African American
Surveys and Questionnaires
Aged
White

@article {pmid40002607,
year = {2025},
author = {Halkiopoulos, C and Gkintoni, E and Aroutzidis, A and Antonopoulou, H},
title = {Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/diagnostics15040456},
pmid = {40002607},
issn = {2075-4418},
abstract = {Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.},
}

@article {pmid40002501,
year = {2025},
author = {Wu, C and Yao, B and Zhang, X and Li, T and Wang, J and Pu, J},
title = {The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
doi = {10.3390/brainsci15020168},
pmid = {40002501},
issn = {2076-3425},
support = {RS2024X007//Key Project of Construction of Drug Regulatory Science System/ ; 2021ZD0200406//STI 2030-MajorProjects under grant/ ; 2021-I2M-1-042, 2021-I2M-1-058//the Medical and Health Innovation Project/ ; 20JCJQIC00230//the Tianjin Outstanding Youth Fund Project/ ; },
abstract = {Background: In motor imagery brain-computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8-10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.},
}

@article {pmid40002462,
year = {2025},
author = {Wu, P and Fei, K and Chen, B and Pan, L},
title = {MSEI-ENet: A Multi-Scale EEG-Inception Integrated Encoder Network for Motor Imagery EEG Decoding.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
doi = {10.3390/brainsci15020129},
pmid = {40002462},
issn = {2076-3425},
support = {61773078//the National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Due to complex signal characteristics and distinct individual differences, the decoding of a motor imagery electroencephalogram (MI-EEG) is limited by the unsatisfactory performance of suboptimal traditional models.

METHODS: A subject-independent model named MSEI-ENet is proposed for multiple-task MI-EEG decoding. It employs a specially designed multi-scale structure EEG-inception module (MSEI) for comprehensive feature learning. The encoder module further helps to detect discriminative information by its multi-head self-attention layer with a larger receptive field, which enhances feature representation and improves recognition efficacy.

RESULTS: The experimental results on Competition IV dataset 2a showed that our proposed model yielded an overall accuracy of 94.30%, MF1 score of 94.31%, and Kappa of 0.92.

CONCLUSIONS: A performance comparison with state-of-the-art methods demonstrated the effectiveness and generalizability of the proposed model on challenging multi-task MI-EEG decoding.},
}

@article {pmid40002457,
year = {2025},
author = {Gu, H and Chen, T and Ma, X and Zhang, M and Sun, Y and Zhao, J},
title = {CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
doi = {10.3390/brainsci15020124},
pmid = {40002457},
issn = {2076-3425},
abstract = {BACKGROUND: Brain-computer interface (BCI) technology opens up new avenues for human-machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application.

METHODS: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer's self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer.

RESULTS: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods.

CONCLUSIONS: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.},
}

@article {pmid40002431,
year = {2025},
author = {Yang, Y and Li, M and Liu, J},
title = {Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
doi = {10.3390/brainsci15020098},
pmid = {40002431},
issn = {2076-3425},
support = {62173010//Mingai Li/ ; },
abstract = {BACKGROUND/OBJECTIVES: Motor neurorehabilitation can be realized by gradually learning diverse motor imagery (MI) tasks. EEG-based brain-computer interfaces (BCIs) provide an effective solution. Nevertheless, existing MI decoding methods cannot balance plasticity for unseen tasks and stability for old tasks. This paper proposes a generative diffusion-based task Incremental Learning (IL) method called GD-TIL.

METHODS: First, data augmentation is employed to increase data diversity by segmenting and recombining EEG signals. Second, to capture temporal-spatial features (TSFs) from different temporal resolutions, a multi-scale temporal-spatial feature extractor (MTSFE) is developed via integrating multiscale temporal-spatial convolutions, a dual-branch pooling operation, multiple multi-head self-attention mechanisms, and a dynamic convolutional encoder. The proposed self-supervised task generalization (SSTG) mechanism introduces a regularization constraint to guide MTSFE and unified classifier updating, which combines labels and semantic similarity between the augmentation with original views to enhance model generalizability for unseen tasks. In the IL phase, a prototype-guided generative replay module (PGGR) is used to generate old tasks' TSFs by training a lightweight diffusion model based on the prototype and label of each task. Furthermore, the generated TSF is merged with a new TSF to fine-tune the convolutional encoder and update the classifier and PGGR. Finally, GD-TIL is evaluated on a self-collected ADL-MI dataset with two MI pairs and a public dataset with four MI tasks.

RESULTS: The continuous decoding accuracy reaches 80.20% and 81.32%, respectively. The experimental results exhibit the excellent plasticity and stability of GD-TIL, even beating the state-of-the-art IL methods.

CONCLUSIONS: Our work illustrates the potential of MI-based BCI and generative AI for continuous neurorehabilitation.},
}

@article {pmid40001978,
year = {2025},
author = {Al-Nafjan, A and Alshehri, H and Aldayel, M},
title = {Objective Pain Assessment Using Deep Learning Through EEG-Based Brain-Computer Interfaces.},
journal = {Biology},
volume = {14},
number = {2},
pages = {},
doi = {10.3390/biology14020210},
pmid = {40001978},
issn = {2079-7737},
support = {(13461-imamu-2023-IMIU-R-3-1-HW-)//The Research, Development, and Innovation Authority (RDIA)-Kingdom of Saudi Arabia/ ; },
abstract = {Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain-computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising two main components: (1) pain/no-pain detection and (2) pain severity classification across three levels: low, moderate, and high. Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time-frequency domain analysis. Furthermore, we compared the performance of our system against conventional machine learning models, such as support vector machines and random forest classifiers. Our deep learning approach outperformed the baseline models, achieving accuracies of 91.84% for pain/no-pain detection and 87.94% for pain severity classification, respectively.},
}

@article {pmid40000219,
year = {2024},
author = {He, W and Wang, D and Meng, Q and He, F and Xu, M and Ming, D},
title = {[Applications and prospects of electroencephalography technology in neurorehabilitation assessment and treatment].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1271-1278},
doi = {10.7507/1001-5515.202404046},
pmid = {40000219},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography ; *Neurological Rehabilitation/methods/instrumentation ; *Brain-Computer Interfaces ; *Transcranial Magnetic Stimulation/methods ; Transcranial Direct Current Stimulation/methods ; Nervous System Diseases/rehabilitation ; Epilepsy/rehabilitation ; },
abstract = {With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.},
}

Humans
*Electroencephalography
*Neurological Rehabilitation/methods/instrumentation
*Brain-Computer Interfaces
*Transcranial Magnetic Stimulation/methods
Transcranial Direct Current Stimulation/methods
Nervous System Diseases/rehabilitation
Epilepsy/rehabilitation

@article {pmid40000217,
year = {2024},
author = {Yang, H and Li, T and Zhao, L and Chen, X and Pan, J and Fu, Y},
title = {[An emerging major: brain-computer interface major].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1257-1264},
doi = {10.7507/1001-5515.202409050},
pmid = {40000217},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Brain/physiology ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a revolutionizing technology that disrupts traditional human-computer interaction by establishing direct communication and control between the brain and computer, bypassing the peripheral nervous and muscular systems. With the rapid advancement of BCI technology, growing application demands, and an increasing need for specialized BCI professionals, a new academic major-BCI major-has gradually emerged. However, few studies to date have discussed the interdisciplinary nature and training framework of this emerging major. To address this gap, this paper first introduced the application demands of BCI, including the demand for BCI technology in both medical and non-medical fields. The paper also described the interdisciplinary nature of the BCI major and the urgent need for specialized professionals in this field. Subsequently, a training program of the BCI major was presented, with careful consideration of the multidisciplinary nature of BCI research and development, along with recommendations for curriculum structure and credit distribution. Additionally, the facing challenges of the construction of the BCI major were analyzed, and suggested strategies for addressing these challenges were offered. Finally, the future of the BCI major was envisioned. It is hoped that this paper will provide valuable reference for the development and construction of the BCI major.},
}

*Brain-Computer Interfaces
Humans
Electroencephalography
Brain/physiology
User-Computer Interface

@article {pmid40000192,
year = {2025},
author = {Chen, Z and Huang, Y and Yu, H and Cao, C and Xu, M and Ming, D},
title = {[Research progress on the characteristics of magnetoencephalography signals in depression].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {1},
pages = {189-196},
doi = {10.7507/1001-5515.202406072},
pmid = {40000192},
issn = {1001-5515},
mesh = {*Magnetoencephalography ; Humans ; *Brain/physiopathology/diagnostic imaging ; *Depression/diagnosis/physiopathology ; Electroencephalography ; Magnetic Resonance Imaging ; },
abstract = {Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.},
}

*Magnetoencephalography
Humans
*Brain/physiopathology/diagnostic imaging
*Depression/diagnosis/physiopathology
Electroencephalography
Magnetic Resonance Imaging

@article {pmid39999624,
year = {2025},
author = {King, SE and Waddell, JT and McDonald, AE and Corbin, WR},
title = {Are you feeling what I'm feeling? Momentary interactions between personal and perceived peer subjective response predict craving and continued drinking in young adults.},
journal = {Drug and alcohol dependence},
volume = {270},
number = {},
pages = {112601},
doi = {10.1016/j.drugalcdep.2025.112601},
pmid = {39999624},
issn = {1879-0046},
abstract = {BACKGROUND: Subjective response to alcohol is a robust predictor of alcohol outcomes. It is possible that the perceived subjective response of others may influence concurrent experiences of one's own subjective response. However, no studies have examined how the perceived subjective response of others might interact with personal subjective response and how such interactions may influence levels of craving and subsequent drinking.

METHOD: Emerging adults (ages 18-25, N = 131, 53.4 % female) completed 21 days of ecological momentary assessments. During drinking events (N = 1335) both personal and perceived peer subjective response (four domains encompassing high- and low-arousal positive & negative effects) were assessed at drink initiation and two subsequent surveys 60 and 120min later. Current craving and drinking quantity since last report were also collected. Three-level multilevel structural equation models with Bayesian estimation tested indirect relations between subjective response and drinking continuation via craving and whether perceived subjective response moderated such relations.

RESULTS: Levels of both personal (b=0.029,95 %BCI:[0.012,0.053]) and perceived (b=0.027,95 %BCI:[0.012,0.051]) experiences of alcohol's rewarding, stimulating effects indirectly predicted drinking continuation via increased craving, and relations were potentiated when perceptions of peer reward were highest (b=0.015,95 %BCI:[0.008,0.020]). Personal experiences of alcohol's relaxing, calming effects indirectly predicted a lower likelihood of drinking continuation via decreased craving (b=-0.017,95 %BCI:[-0.036,-0.003]) whereas perceived effects directly predicted lower likelihoods of drinking (b=-0.133,95 %CI:[-0.239, -0.031]).

CONCLUSION: Results suggest both personal and perceived peer subjective response independently influence drinking behavior even when controlling for one another. Targeted interventions focused on altering interpretations of peer subjective effects may be effective at reducing momentary risk.},
}

@article {pmid40000203,
year = {2024},
author = {Wu, X and Chu, Y and Zhao, X and Zhao, Y},
title = {[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1145-1152},
doi = {10.7507/1001-5515.202407038},
pmid = {40000203},
issn = {1001-5515},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions, leading to low decoding recognition rates with traditional neural networks. To address this, this paper proposed a three-dimensional (3D) convolutional neural network (CNN) method that learns spatial-frequency feature maps, using Welch method to calculate the power spectrum of EEG frequency bands, converted time-series EEG into a brain topographical map with spatial-frequency information. A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features. Comparative experiments demonstrated that the average decoding recognition rate reached 86.89%, outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.},
}

*Electroencephalography/methods
*Brain-Computer Interfaces
Humans
*Neural Networks, Computer
Imagination/physiology
Brain/physiology
Signal Processing, Computer-Assisted

@article {pmid40000170,
year = {2025},
author = {Zhang, Y and Zhang, C and Sun, S and Xu, G},
title = {[Research on motor imagery recognition based on feature fusion and transfer adaptive boosting].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {1},
pages = {9-16},
doi = {10.7507/1001-5515.202304067},
pmid = {40000170},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; *Support Vector Machine ; Humans ; *Algorithms ; *Neural Networks, Computer ; Electroencephalography ; Imagination/physiology ; Wavelet Analysis ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; },
abstract = {This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 [th] International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.},
}

*Brain-Computer Interfaces
*Support Vector Machine
Humans
*Algorithms
*Neural Networks, Computer
Electroencephalography
Imagination/physiology
Wavelet Analysis
Pattern Recognition, Automated/methods
Reproducibility of Results

@article {pmid39997141,
year = {2025},
author = {Ha, J and Park, S and Han, Y and Kim, L},
title = {Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
doi = {10.3390/biomimetics10020118},
pmid = {39997141},
issn = {2313-7673},
support = {RS-2024-00340293//the National Research Foundation of Korea (NRF)/ ; },
abstract = {Brain-computer interface (BCI)-based assistive technologies enable intuitive and efficient user interaction, significantly enhancing the independence and quality of life of elderly and disabled individuals. Although existing wet EEG-based systems report high accuracy, they suffer from limited practicality. This study presents a hybrid BCI system combining dry-type EEG-based flash visual-evoked potentials (FVEP) and pupillary light reflex (PLR) designed to control an LED-based meal-assist robot. The hybrid system integrates dry-type EEG and eyewear-type infrared cameras, addressing the preparation challenges of wet electrodes, while maintaining practical usability and high classification performance. Offline experiments demonstrated an average accuracy of 88.59% and an information transfer rate (ITR) of 18.23 bit/min across the four target classifications. Real-time implementation uses PLR triggers to initiate the meal cycle and EMG triggers to detect chewing, indicating the completion of the cycle. These features allow intuitive and efficient operation of the meal-assist robot. This study advances the BCI-based assistive technologies by introducing a hybrid system optimized for real-world applications. The successful integration of the FVEP and PLR in a meal-assisted robot demonstrates the potential for robust and user-friendly solutions that empower the users with autonomy and dignity in their daily activities.},
}

@article {pmid39997117,
year = {2025},
author = {Premchand, B and Zhang, Z and Ang, KK and Yu, J and Tan, IO and Lam, JPW and Choo, AXY and Sidarta, A and Kwong, PWH and Chung, LHC},
title = {A Personalized Multimodal BCI-Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
doi = {10.3390/biomimetics10020094},
pmid = {39997117},
issn = {2313-7673},
support = {//Institute for Infocomm Research/ ; Research Grant RRG4/2008//Rehabilitation Research Institute of Singapore/ ; },
abstract = {Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient's specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant's respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements.},
}

@article {pmid39996608,
year = {2025},
author = {Hjortkjær, J and Wong, DDE and Catania, A and Märcher-Rørsted, J and Ceolini, E and Fuglsang, SA and Kiselev, I and Di Liberto, G and Liu, SC and Dau, T and Slaney, M and de Cheveigné, A},
title = {Real-time control of a hearing instrument with EEG-based attention decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad867c},
pmid = {39996608},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Attention/physiology ; *Hearing Aids ; *Brain-Computer Interfaces ; *Speech Perception/physiology ; Computer Systems ; Acoustic Stimulation/methods ; Male ; Female ; Adult ; },
abstract = {Enhancing speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly, but hearing devices cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of neurosteering, in which a hearing instrument selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present and discuss a real-time brain-computer interface system that combines a stimulus-response model based on canonical correlation analysis for real-time EEG attention decoding, coupled with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. We provide an overview of the system and its various components, discuss prospects and limitations of the technology, and illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.},
}

Humans
*Electroencephalography/methods/instrumentation
*Attention/physiology
*Hearing Aids
*Brain-Computer Interfaces
*Speech Perception/physiology
Computer Systems
Acoustic Stimulation/methods
Male
Female
Adult

@article {pmid39996071,
year = {2025},
author = {Samadi, E and Rahatabad, FN and Nasrabadi, AM and Dabanlou, NJ},
title = {Brain analysis to approach human muscles synergy using deep learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {44},
pmid = {39996071},
issn = {1871-4080},
abstract = {Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.},
}

@article {pmid39994209,
year = {2025},
author = {Guan, S and Dong, T and Cong, LK},
title = {Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {6601},
pmid = {39994209},
issn = {2045-2322},
support = {20220508014RC//Project supported by Jilin Provincial Science and Technology Development Plan Project/ ; },
abstract = {In response to the current issues of one-sided effective feature extraction and low classification accuracy in multi-class motor imagery recognition, this study proposes an Electroencephalogram (EEG) signal recognition method based on multi-domain feature fusion and optimized multi-kernel extreme learning machine. Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. Subsequently, kernel principal component analysis (KPCA) is employed to fuse and reduce the dimensionality of the joint features, resulting in a reduced-dimensional fused feature vector. Finally, these feature vectors are input into a Radius-incorporated multi-kernel extreme learning machine (RIO-MKELM) for classification. The experimental results indicate that through multi-domain feature fusion and the incorporation of radius in a multi-kernel extreme learning machine, feature selection can be performed more effectively, eliminating redundant or irrelevant features and retaining the most useful information for classification. This approach enhances classification accuracy and other evaluation metrics, with the final classification accuracy reaching 95.49%, sensitivity at 97.88%, specificity at 98.12%, recall at 97.88%, and F1 Score at 96.67%. The findings of this study are of significant importance for the development and practical application of brain-computer interface (BCI) systems.},
}

@article {pmid39993988,
year = {2025},
author = {Guo, J and Yang, J and Li, Y},
title = {[Analysis of Brain-Computer Interface Technology in the Medical Field and the Regulation of the US FDA].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {49},
number = {1},
pages = {96-102},
doi = {10.12455/j.issn.1671-7104.240187},
pmid = {39993988},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; United States ; *United States Food and Drug Administration ; Humans ; Electroencephalography ; Device Approval ; },
abstract = {Brain-computer interface (BCI) technology is an innovative and cutting-edge medical advancement that enables direct interaction between the brain and external devices, facilitating the reconstruction of daily functions for patients or serving as a method for neuro-regulation therapy. Although this technology offers a broad range of clinical applications, there are problems as potential risks, individual variations, and the need for long-term monitoring of its effects during utilization. Consequently, the comprehensive evaluation of its safety and effectiveness poses a considerable challenge for regulatory agencies. This study provides a concise introduction to the development history and various types of BCI technology, followed by a summary of the regulatory situation for different types of BCI medical devices in the United States. Furthermore, the regulatory requirements imposed by the US FDA on this product category are analyzed. Finally, the article concludes by presenting a summary and future perspective on the current development of BCI technology, with the aim of offering beneficial insights and guidance for the regulation of BCI medical devices.},
}

*Brain-Computer Interfaces
United States
*United States Food and Drug Administration
Humans
Electroencephalography
Device Approval

@article {pmid39993333,
year = {2025},
author = {Kunigk, NG and Schone, HR and Gontier, C and Hockeimer, W and Tortolani, AF and Hatsopoulos, N and Downey, J and Chase, SM and Boninger, ML and Dekleva, BM and Collinger, J},
title = {Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adb995},
pmid = {39993333},
issn = {1741-2552},
abstract = {The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control. Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder. We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g., reaching vs. wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex. These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.},
}

@article {pmid39992333,
year = {2024},
author = {Qin, HJ and Liu, YY and Fu, EH and Liu, YW and Tian, ZL and Dong, HW and Liu, TA and Zou, DH and Cheng, YB and Liu, NG},
title = {Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.},
journal = {Fa yi xue za zhi},
volume = {40},
number = {5},
pages = {419-429},
doi = {10.12116/j.issn.1004-5619.2024.440801},
pmid = {39992333},
issn = {1004-5619},
mesh = {Humans ; *Tomography, X-Ray Computed/methods ; *Neural Networks, Computer ; *Head Injuries, Closed/diagnostic imaging ; Deep Learning ; Image Processing, Computer-Assisted/methods ; Craniocerebral Trauma/diagnostic imaging ; Forensic Medicine/methods ; Skull Fractures/diagnostic imaging ; },
abstract = {OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.

METHODS: A total of 5 486 CT images of BCI from living persons were collected as the training set, validation set and test set for model training and performance evaluation. Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to segment the five types of craniocerebral injuries including scalp hematoma, skull fracture, epidural hematoma, subdural hematoma, and brain contusion. Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers. The five types CT images of all BCI except the blind test set were manually labeled; then, each dataset was inputted into the model to train the model. The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set, and the generalization ability was evaluated based on the Dice value of the test set. According to the accuracy, precision and F1 value of the blind test set, the segmentation performance of the model for five types of BCI was evaluated.

RESULTS: After training and optimizing the model, the average Dice values of the final optimal model to scalp hematoma, skull fracture, epidural hematoma, subdural hematoma and brain contusion segmentation were 0.766 4, 0.812 3, 0.938 7, 0.782 7 and 0.858 1, respectively, all greater than 0.75, meeting the expected requirements. External validation showed that the F1 values were 93.02%, 89.80%, 87.80%, 92.93% and 86.57% in living CT images, respectively; 83.92%, 44.90%, 76.47%, 64.29% and 48.89% in cadaveric CT images, respectively. The above suggested that the model was able to accurately segment various types of craniocerebral injury on living CT images, while its segmentation ability was relatively poor on cadaveric CT images, but still able to accurately segment scalp hematoma, epidural hematoma and subdural hematoma.

CONCLUSIONS: Deep learning model trained on CT images can be used for BCI segmentation. However, the direct use of living persons' BCI models for the identification of cadaveric BCI has some limitations. This study provides a new approach for intelligent segmentation of virtual anatomical data for BCI.},
}

Humans
*Tomography, X-Ray Computed/methods
*Neural Networks, Computer
*Head Injuries, Closed/diagnostic imaging
Deep Learning
Image Processing, Computer-Assisted/methods
Craniocerebral Trauma/diagnostic imaging
Forensic Medicine/methods
Skull Fractures/diagnostic imaging

@article {pmid39992067,
year = {2025},
author = {Wan, C and Zhang, W and Nie, Y and Qian, Y and Wang, J and Xu, H and Li, Z and Su, B and Zhang, Y and Li, Y},
title = {Impact of motor imagery-based brain-computer interface combined with virtual reality on enhancing attention, executive function, and lower-limb function in stroke: A pilot study.},
journal = {PM & R : the journal of injury, function, and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1002/pmrj.13324},
pmid = {39992067},
issn = {1934-1563},
support = {No.303103136AA22//National Clinical Medical Research Centre Cultivation Program of Nanjing/ ; No.ST242102//Major sports research projects of Jiangsu Sports Bureau/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; },
abstract = {BACKGROUND: Brain-computer interface combined with virtual reality (BCI-VR) can reduce the difficulty of motor imagery execution and improve training performance. Few studies have focused on the effects of BCI-VR on attention, executive function, and lower-limb function in stroke.

OBJECTIVE: To evaluate feasibility and preliminary efficacy of BCI-VR pedaling training on the attention, executive function, and lower-extremity function in people after stroke. It will also provide data support for future research, especially sample size calculations.

DESIGN: A single group before-after trial design was used. All participants had a stable level of function over a 2-week period to ensure that their functional recovery was all attributable to BCI-VR training.

SETTING: The study was conducted in a specialized rehabilitation hospital.

PARTICIPANTS: Twelve participants with stroke, a certain level of motor imagery ability, capable of walking 10 meters continuously.

INTERVENTIONS: All participants received a 4-week BCI-VR pedaling training program, 5 days per week, 30 minutes each session.

OUTCOME MEASURES: Primary outcomes are feasibility and safety. Secondary outcomes were lower-extremity mobility, attention, and executive functions.

RESULTS: Twelve patients were recruited from inpatient rehabilitation and nine completed the study (six males/three females; 56.6 ± 11.6 years). Recruitment and retention rates were 34% and 75%, respectively. Excellent adherence rate (97.7%) was obtained. No adverse events or equipment issues were reported. Following the intervention, significant improvements were found in the lower-extremity strength, balance, walking stability, attention, and general cognitive function (p < .05). A significant correlation was found between improved Berg balance scale change values and symbol digit modalities test change values (p < .05, r = 0.677).

CONCLUSIONS: BCI-VR pedaling training provides a depth of feasibility and safety data, methodological detail, and preliminary results. This could provide a useful basis for future studies of BCI-VR pedaling training for stroke rehabilitation.

CLINICALTRIALS: gov registration number: ChiCTR2300071522 (http://www.chictr.org.cn).},
}

@article {pmid39990438,
year = {2025},
author = {Lin, Z and Marin-Llobet, A and Baek, J and He, Y and Lee, J and Wang, W and Zhang, X and Lee, AJ and Liang, N and Du, J and Ding, J and Li, N and Liu, J},
title = {Spike sorting AI agent.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.02.11.637754},
pmid = {39990438},
issn = {2692-8205},
abstract = {Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.},
}

@article {pmid39990331,
year = {2025},
author = {Pescatore, CRC and Zhang, H and Hadjinicolaou, AE and Paulk, AC and Rolston, JD and Richardson, RM and Williams, ZM and Cai, J and Cash, SS},
title = {Decoding semantics from natural speech using human intracranial EEG.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.02.10.637051},
pmid = {39990331},
issn = {2692-8205},
abstract = {Brain-computer interfaces (BCIs) hold promise for restoring natural language production capabilities in patients with speech impairments, potentially enabling smooth conversation that conveys meaningful information via synthesized words. While considerable progress has been made in decoding phonetic features of speech, our ability to extract lexical semantic information (i.e. the meaning of individual words) from neural activity remains largely unexplored. Moreover, most existing BCI research has relied on controlled experimental paradigms rather than natural conversation, limiting our understanding of semantic decoding in ecological contexts. Here, we investigated the feasibility of decoding lexical semantic information from stereo-electroencephalography (sEEG) recordings in 14 participants during spontaneous conversation. Using multivariate pattern analysis, we were able to decode word level semantic features during language production with an average accuracy of 21% across all participants compared to a chance level of 10%. This semantic decoding remained robust across different semantic representations while maintaining specificity to semantic features. Further, we identified a distributed left-lateralized network spanning precentral gyrus, pars triangularis, and middle temporal cortex, with low-frequency oscillations showing stronger contributions. Together, our results establish the feasibility of extracting word meanings from neural activity during natural speech production and demonstrate the potential for decoding semantic content from unconstrained speech.},
}

@article {pmid39989959,
year = {2025},
author = {Shoffstall, A and Li, L and Hartzler, A and Menendez-Lustri, D and Zhang, J and Chen, A and Lam, D and Traylor, B and Quill, E and Hoeferlin, G and Pawlowski, C and Bruckman, M and Gupta, SA and Capadona, J},
title = {Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance.},
journal = {Research square},
volume = {},
number = {},
pages = {},
doi = {10.21203/rs.3.rs-6018202/v1},
pmid = {39989959},
issn = {2693-5015},
abstract = {Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by the IME insertions lead to the increased neuroinflammation and reduced neural recording performance. Additionally, a sustained presence of activated platelets and coagulation factors is found near the insertion site. Thus, we hypothesized that the systemic administration of dexamethasone sodium phosphate-loaded platelet-inspired nanoparticle (SPPINDEX) can improve the neural recording performance of intracortical microelectrodes (IMEs) by promoting hemostasis, facilitating blood-brain barrier (BBB) healing, and achieving implant-targeted drug delivery. Leveraging the hemostatic and coagulation factor-binding properties of the platelet-inspired nanoparticle (PIN) drug delivery platform, SPPINDEX treatment can initially attenuate the invasion of neuroinflammatory triggers into the brain parenchyma caused by insertion-induced microhemorrhages or a compromised BBB. Furthermore, targeted delivery of the anti-inflammatory drug dexamethasone sodium phosphate (DEXSP) to the implant site via these nanoparticles can attenuate ongoing neuroinflammation, enhancing overall therapeutic efficacy. Weekly treatment with SPPINDEX for 8 weeks significantly improved the recording capabilities of IMEs compared to platelet-inspired nanoparticles alone (PIN), free dexamethasone sodium phosphate (Free DEXSP), and a diluent control trehalose buffer (TH), as assessed through extracellular single-unit recordings. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggest that the improved neural recording performance may be attributed to reduced neuron degeneration, activated microglia and astrocytes at the implant interface caused by the decreased infiltration of blood-derived proteins that trigger neuroinflammation and the therapeutic effects from DEXSP. Overall, SPPINDEX treatment promotes an anti-inflammatory environment that improves neuronal density and enhances recording performance.},
}

@article {pmid39988822,
year = {2025},
author = {Park, J and Ahn, J and Choi, J and Kim, J},
title = {Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation.},
journal = {Journal of chemical information and modeling},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.jcim.4c01669},
pmid = {39988822},
issn = {1549-960X},
abstract = {Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates improved performance over existing approaches in generating molecules having the desired properties, including penalized LogP, QED, and celecoxib similarity, without any prior knowledge. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.},
}

@article {pmid39987217,
year = {2025},
author = {Wang, X and Chen, S and Wang, K and Cao, L},
title = {Predicted action-effects shape action representation through pre-activation of alpha oscillations.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {275},
pmid = {39987217},
issn = {2399-3642},
support = {32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; Male ; Female ; Adult ; *Alpha Rhythm/physiology ; Young Adult ; Visual Cortex/physiology ; Attention/physiology ; Psychomotor Performance/physiology ; Feedback, Sensory/physiology ; },
abstract = {Actions are typically accompanied by sensory feedback (or action-effects). Action-effects, in turn, influence the action. Theoretical accounts of action control assume a pre-activation of action-effects prior to action execution. Here we show that when participants were asked to report the time of their voluntary keypress using the position of a fast-rotating clock hand, a predictable action-effect (i.e. a 250 ms delayed sound after keypress) led to a shift of visuospatial attention towards the clock hand position of action-effect onset, thus demonstrating an influence of action-effects on action representation. Importantly, the attention shift occurred about 1 second before the action execution, which was further preceded and predicted by a lateralisation of alpha oscillations in the visual cortex. Our results indicate that when the spatial location is the key feature of action-effects, the neural implementation of the action-effect pre-activation is achieved through alpha lateralisation.},
}

Humans
Male
Female
Adult
*Alpha Rhythm/physiology
Young Adult
Visual Cortex/physiology
Attention/physiology
Psychomotor Performance/physiology
Feedback, Sensory/physiology

@article {pmid39986990,
year = {2025},
author = {Ding, N},
title = {Sequence chunking through neural encoding of ordinal positions.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2025.01.014},
pmid = {39986990},
issn = {1879-307X},
abstract = {Grouping sensory events into chunks is an efficient strategy to integrate information across long sequences such as speech, music, and complex movements. Although chunks can be constructed based on diverse cues (e.g., sensory features, statistical patterns, internal knowledge) recent studies have consistently demonstrated that the chunks constructed by different cues are all tracked by low-frequency neural dynamics. Here, I review evidence that chunking cues drive low-frequency activity in modality-dependent networks, which interact to generate chunk-tracking activity in broad brain areas. Functionally, this work suggests that a core computation underlying sequence chunking may assign each event its ordinal position within a chunk and that this computation is causally implemented by chunk-tracking neural activity during predictive sequence chunking.},
}

@article {pmid39986550,
year = {2025},
author = {Zhao, W and Rao, J and Wang, R and Chai, Y and Mao, T and Quan, P and Deng, Y and Chen, W and Wang, S and Guo, B and Zhang, Q and Rao, H},
title = {Test-retest reliability of coupling between cerebrospinal fluid flow and global brain activity after normal sleep and sleep deprivation.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121097},
doi = {10.1016/j.neuroimage.2025.121097},
pmid = {39986550},
issn = {1095-9572},
abstract = {The glymphatic system (GS) plays a key role in maintaining brain homeostasis by clearing metabolic waste during sleep, with the coupling between global blood-oxygen-level-dependent (gBOLD) and cerebrospinal fluid (CSF) signals serving as a potential marker for glymphatic clearance function. However, the test-retest reliability and spatial heterogeneity of gBOLD-CSF coupling after different sleep conditions remain unclear. In this study, we assessed the test-retest reliability of gBOLD-CSF coupling following either normal sleep or total sleep deprivation (TSD) in 64 healthy adults under controlled laboratory conditions. The reliability was high after normal sleep (ICC = 0.763) but decreased following TSD (ICC = 0.581). Moreover, spatial heterogeneity was evident in participants with normal sleep, with lower-order networks (visual, somatomotor, and attention) showing higher ICC values compared to higher-order networks (default-mode, limbic, and frontoparietal). This spatial variation was less distinct in the TSD group. These results demonstrate the robustness of the gBOLD-CSF coupling method and emphasize the significance of considering sleep history in glymphatic function research.},
}

@article {pmid39985774,
year = {2025},
author = {Zhang, D and Wang, Z and Qian, Y and Zhao, Z and Liu, Y and Lu, J and Li, Y},
title = {Protocol to perform offline ECoG brain-to-text decoding for natural tonal sentences.},
journal = {STAR protocols},
volume = {6},
number = {1},
pages = {103650},
doi = {10.1016/j.xpro.2025.103650},
pmid = {39985774},
issn = {2666-1667},
abstract = {Here, we present a protocol to decode Mandarin sentences from invasive neural recordings using a brain-to-text framework. We describe steps for preparing materials, including designing the sentence corpus and setting up electrocorticography (ECoG) recording systems. We then detail procedures for decoding, such as data preprocessing, selection of speech-responsive electrodes, speech detection, syllable and tone decoding, and language modeling. We also outline performance evaluation metrics. For complete details on the use and execution of this protocol, please refer to Zhang et al.[1].},
}

@article {pmid39984530,
year = {2025},
author = {Liu, Y and Gui, Z and Yan, D and Wang, Z and Gao, R and Han, N and Chen, J and Wu, J and Ming, D},
title = {Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {314},
pmid = {39984530},
issn = {2052-4463},
support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Lower Extremity/physiopathology ; *Stroke Rehabilitation ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; Male ; Female ; Middle Aged ; Imagination ; Neuronal Plasticity ; },
abstract = {Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems.},
}

Humans
*Electroencephalography
*Lower Extremity/physiopathology
*Stroke Rehabilitation
*Brain-Computer Interfaces
*Stroke/physiopathology
Male
Female
Middle Aged
Imagination
Neuronal Plasticity

@article {pmid39983772,
year = {2025},
author = {Wang, M and Zhang, Y and Wang, A and Gan, Z and Zhang, L and Kang, X},
title = {Soft Neural Interface with color adjusted PDMS encapsulation layer for Spinal Cord Stimulation.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110402},
doi = {10.1016/j.jneumeth.2025.110402},
pmid = {39983772},
issn = {1872-678X},
abstract = {BACKGROUND: Spinal cord stimulation (SCS) plays a crucial role in treating various neurological diseases. Utilizing soft spinal cord electrodes in SCS allows for a better fit with the physiological structure of the spinal cord and reduces tissue damage. Polydimethylsiloxane (PDMS) has emerged as an ideal material for soft bioelectronics. However, micromachining soft PDMS bioelectronics devices with low thermal effects and high uniformity remains challenging.

NEW METHOD: Here, we demonstrated a fully laser-micromachined soft neural interface for SCS. The native and color adjusted PDMS with variable absorbance characteristics were investigated in laser processing. In addition, we systematically evaluated the impact of electrode sizes on the electrochemical performance of neural interface. By fitting the equivalent circuit model, the electrochemical process of neural interface was revealed and the performance of the electrode was evaluated. The biocompatibility of color adjusted PDMS was confirmed by cytotoxicity assays. Finally, we validated the neural interface in mice.

RESULTS: Color adjusted PDMS has good biocompatibility and can significantly reduce the damage caused by thermal effects, enhancing the electrochemical performance of bioelectronic devices. The soft neural interface with color adjusted PDMS encapsulation layer can activate the motor function safely.

The fully laser-micromachined soft neural interface was proposed for the first time. Compared with existing methods, this method showed low thermal effects, high uniformity, and could be easily scaled up.

CONCLUSIONS: The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.},
}

@article {pmid39983235,
year = {2025},
author = {T A, S and Ramakrishnan, S and Vinod, AP and Alladi, S},
title = {On the Feasibility of an Online Brain-Computer Interface-based Neurofeedback Game for Enhancing Attention and Working Memory in Stroke and Mild Cognitive Impairment Patients.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/adb8ef},
pmid = {39983235},
issn = {2057-1976},
abstract = {BACKGROUND: Neurofeedback training (NFT) using Electroencephalogram-based Brain Computer Interface (EEG-BCI) is an emerging therapeutic tool for enhancing cognition. Methods: We developed an EEG-BCI-based NFT game for enhancing attention and working memory of stroke and Mild cognitive impairment (MCI) patients. The game involves a working memory task during which the players memorize locations of images in a matrix and refill them correctly using their attention levels. The proposed NFT was conducted across fifteen subjects (6 Stroke, 7 MCI, and 2 healthy). The effectiveness of the NFT was evaluated using the percentage of correctly filled matrix elements and EEG-based attention score. EEG varitions during working memory tasks were also investigated using EEG topographs and EEG-based indices.

RESULTS: The EEG-based attention score showed an enhancement ranging from 4.29-32.18% in the Stroke group from the first session to the third session, while in the MCI group, the improvement ranged from 4.32% to 48.25%. We observed significant differences in EEG band powers during working memory operation between the stroke and MCI groups.

SIGNIFICANCE: The proposed neurofeedback game operates based on attention and aims to improve multiple cognitive functions, including attention and working memory, in patients with stroke and MCI.

CONCLUSIONS: The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.},
}

@article {pmid39981822,
year = {2025},
author = {Cai, Y and Li, Q and Banga, AK and Wesselmann, U and Zhao, C},
title = {Tetrodotoxin Delivery Pen Safely Uses Potent Natural Neurotoxin to Manage Severe Cutaneous Pain.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e2401549},
doi = {10.1002/adhm.202401549},
pmid = {39981822},
issn = {2192-2659},
support = {R61NS123196/NS/NINDS NIH HHS/United States ; R01GM144388/GM/NIGMS NIH HHS/United States ; R15GM139193/GM/NIGMS NIH HHS/United States ; },
abstract = {Clinically available therapies often inadequately address severe chronic cutaneous pain due to short anesthetic duration, insufficient intensity, or side effects. This study introduces a pen device delivering tetrodotoxin (TTX), a potent neurotoxin targeting nerve voltage-gated sodium channels, as a safe and effective topical anesthetic to treat severe chronic cutaneous pain. Chemical permeation enhancers, such as sodium dodecyl sulfate (SDS) and limonene (LIM), are incorporated to enhance TTX skin permeability. The device ensures precise TTX dosing down to the nanogram level, essential to avoid TTX overdose. In rats, the pen device treatment produces TTX-dose-dependent anesthetic effectiveness. An administration of 900 ng of TTX with SDS and LIM to the rat back skin produces a 393.25% increase (measurement limit) in the nociceptive skin pressure threshold, and the hypoalgesia lasts for 11.25 h, outperforming bupivacaine (28 µg), of which are 25.24% and under 1 h. Moreover, the pen device provides on-demand therapy for multiple treatments, consistently achieving prolonged anesthesia over ten sessions (1 treatment per day) without noted toxicity. Furthermore, a single topical administration of 16 µg of TTX exhibits no TTX-related toxicity in rats. The TTX delivery pen paves the way for clinical trials, offering a promising solution for severe cutaneous pain.},
}

@article {pmid39981403,
year = {2025},
author = {Liao, W and Miao, Z and Liang, S and Zhang, L and Li, C},
title = {A composite improved attention convolutional network for motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1543508},
pmid = {39981403},
issn = {1662-4548},
abstract = {INTRODUCTION: A brain-computer interface (BCI) is an emerging technology that aims to establish a direct communication pathway between the human brain and external devices. Motor imagery electroencephalography (MI-EEG) signals are analyzed to infer users' intentions during motor imagery. These signals hold potential for applications in rehabilitation training and device control. However, the classification accuracy of MI-EEG signals remains a key challenge for the development of BCI technology.

METHODS: This paper proposes a composite improved attention convolutional network (CIACNet) for MI-EEG signals classification. CIACNet utilizes a dual-branch convolutional neural network (CNN) to extract rich temporal features, an improved convolutional block attention module (CBAM) to enhance feature extraction, temporal convolutional network (TCN) to capture advanced temporal features, and multi-level feature concatenation for more comprehensive feature representation.

RESULTS: The CIACNet model performs well on both the BCI IV-2a and BCI IV-2b datasets, achieving accuracies of 85.15 and 90.05%, respectively, with a kappa score of 0.80 on both datasets. These results indicate that the CIACNet model's classification performance exceeds that of four other comparative models.

CONCLUSION: Experimental results demonstrate that the proposed CIACNet model has strong classification capabilities and low time cost. Removing one or more blocks results in a decline in the overall performance of the model, indicating that each block within the model makes a significant contribution to its overall effectiveness. These results demonstrate the ability of the CIACNet model to reduce time costs and improve performance in motor imagery brain-computer interface (MI-BCI) systems, while also highlighting its practical applicability.},
}

@article {pmid39981127,
year = {2025},
author = {Ghosh, S and Yadav, RK and Soni, S and Giri, S and Muthukrishnan, SP and Kumar, L and Bhasin, S and Roy, S},
title = {Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1532783},
pmid = {39981127},
issn = {1662-5161},
abstract = {Understanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective control. EEG-based BMI systems show promise due to their non-invasive nature and potential for inducing neural plasticity, enhancing motor rehabilitation outcomes. While EEG-based BMIs show potential for decoding motor intention and kinematics, studies indicate inconsistent correlations with actual or planned movements, posing challenges for achieving precise and reliable prosthesis control. Further, the variability in predictive EEG patterns across individuals necessitates personalized tuning to improve BMI efficiency. Integrating multiple physiological signals could enhance BMI precision and reliability, paving the way for more effective motor rehabilitation strategies. Studies have shown that brain activity adapts to gravitational and inertial constraints during movement, highlighting the critical role of neural adaptation to biomechanical changes in creating control systems for assistive devices. This review aims to provide a comprehensive overview of recent progress in deciphering neural activity patterns associated with both physiological and assisted upper limb movements, highlighting avenues for future exploration in neurorehabilitation and brain-machine interface development.},
}

@article {pmid39980021,
year = {2025},
author = {Angulo-Sherman, IN and León-Domínguez, U and Martinez-Torteya, A and Fragoso-González, GA and Martínez-Pérez, MV},
title = {Proficiency in motor imagery is linked to the lateralization of focused ERD patterns and beta PDC.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {30},
pmid = {39980021},
issn = {1743-0003},
support = {Not applicable//Universidad de Monterrey/ ; Not applicable//Universidad de Monterrey/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; Female ; *Electroencephalography ; Adult ; *Functional Laterality/physiology ; Young Adult ; Beta Rhythm/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Motor Cortex/physiology ; Cortical Synchronization/physiology ; },
abstract = {BACKGROUND: Motor imagery based brain-computer interfaces (MI-BCIs) are systems that detect the mental rehearsal of movement from brain activity signals (EEG) for controlling devices that can potentiate motor neurorehabilitation. Considering the problem that MI proficiency requires training and it is not always achieved, EEG desirable features should be investigated to propose indicators of successful MI training.

METHODS: Nine healthy right-handed subjects trained with a MI-BCI for four sessions. In each session, EEG was recorded for 30 trials that consisted of a rest and a dominant-hand MI sequence, which were used for calibrating the system. Then, the subject participated in 160 trials in which a cursor was displaced on a screen by performing MI or relaxing to hit a target. The session's accuracy was calculated. For each trial from the calibration phase of the first session, the power spectral density (PSD) and the partial directed coherence (PDC) of the rest and MI EEG segments were obtained to estimate the event-related synchronization changes (ERS) and the connectivity patterns of the θ , α , β and γ bands that are associated with high BCI control (accuracy above 70% in at least one session). Finally, t-tests and rank-sum tests (p < 0.05 , with Benjamini-Hochberg correction) were used to compare the ERS/ERD and PDC values of subjects with high and low accuracy, respectively.

RESULTS: Proficient users showed greater α ERD on the right-hand motor cortex (left hemisphere). Furthermore, the β PDC related to the ipsilateral motor cortex is commonly weakened during motor imagery, while the contralateral motor cortex γ PDC is enhanced.

CONCLUSIONS: Motor imagery proficiency is related to the focused and lateralized event-related α desynchronization patterns and the lateralization of β and γ PDC. Future analysis of these features could allow complimenting the information for assessment of subject-specific BCI control and the prediction of the effectiveness of motor-imagery training.},
}

Humans
*Brain-Computer Interfaces
Male
*Imagination/physiology
Female
*Electroencephalography
Adult
*Functional Laterality/physiology
Young Adult
Beta Rhythm/physiology
Movement/physiology
Psychomotor Performance/physiology
Motor Cortex/physiology
Cortical Synchronization/physiology

@article {pmid39979463,
year = {2025},
author = {Bhadra, K and Giraud, AL and Marchesotti, S},
title = {Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {271},
pmid = {39979463},
issn = {2399-3642},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Electroencephalography ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Learning/physiology ; Imagination/physiology ; Brain/physiology ; },
abstract = {Brain-Computer Interfaces (BCI) will revolutionize the way people with severe impairment of speech production can communicate. While current efforts focus on training classifiers on vast amounts of neurophysiological signals to decode imagined speech, much less attention has been given to users' ability to adapt their neural activity to improve BCI-control. To address whether BCI-control improves with training and characterize the underlying neural dynamics, we trained 15 healthy participants to operate a binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI-control was globally observed. Using a control experiment, we show that a continuous feedback about the decoded activity is necessary for learning to occur. Performance improvement was associated with a broad EEG power increase in frontal theta activity and focal enhancement in temporal low-gamma activity, showing that learning to operate an imagined-speech BCI involves dynamic changes in neural features at different spectral scales. These findings demonstrate that combining machine and human learning is a successful strategy to enhance BCI controllability.},
}

Humans
*Brain-Computer Interfaces
Male
*Electroencephalography
Female
Adult
*Speech/physiology
Young Adult
*Learning/physiology
Imagination/physiology
Brain/physiology

@article {pmid39979293,
year = {2025},
author = {Hoeferlin, GF and Grabinski, SE and Druschel, LN and Duncan, JL and Burkhart, G and Weagraff, GR and Lee, AH and Hong, C and Bambroo, M and Olivares, H and Bajwa, T and Coleman, J and Li, L and Memberg, W and Sweet, J and Hamedani, HA and Acharya, AP and Hernandez-Reynoso, AG and Donskey, C and Jaskiw, G and Ricky Chan, E and Shoffstall, AJ and Bolu Ajiboye, A and von Recum, HA and Zhang, L and Capadona, JR},
title = {Bacteria invade the brain following intracortical microelectrode implantation, inducing gut-brain axis disruption and contributing to reduced microelectrode performance.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1829},
pmid = {39979293},
issn = {2041-1723},
mesh = {Animals ; *Microelectrodes ; Mice ; *Electrodes, Implanted/adverse effects ; *Brain-Computer Interfaces ; *Brain ; *Blood-Brain Barrier ; *Mice, Inbred C57BL ; Gastrointestinal Microbiome ; Male ; Brain-Gut Axis/physiology ; Bacteria ; Anti-Bacterial Agents/pharmacology ; },
abstract = {Brain-machine interface performance can be affected by neuroinflammatory responses due to blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings suggest that certain gut bacterial constituents might enter the brain through damaged BBB. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could facilitate microbiome entry into the brain. In our study, we found bacterial sequences, including gut-related ones, in the brains of mice with implanted microelectrodes. These sequences changed over time. Mice treated with antibiotics showed a reduced presence of these bacteria and had a different inflammatory response, which temporarily improved microelectrode recording performance. However, long-term antibiotic use worsened performance and disrupted neurodegenerative pathways. Many bacterial sequences found were not present in the gut or in unimplanted brains. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.},
}

Animals
*Microelectrodes
Mice
*Electrodes, Implanted/adverse effects
*Brain-Computer Interfaces
*Brain
*Blood-Brain Barrier
*Mice, Inbred C57BL
Gastrointestinal Microbiome
Male
Brain-Gut Axis/physiology
Bacteria
Anti-Bacterial Agents/pharmacology

@article {pmid39978072,
year = {2025},
author = {Lim, MJR and Lo, JYT and Tan, YY and Lin, HY and Wang, YH and Tan, D and Wang, E and Naing, MYY and Ng, JJW and Jefree, RAB and Yeo, TT},
title = {The state-of-the-art of invasive brain-computer interfaces in humans: a systematic review and individual patient meta-analysis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adb88e},
pmid = {39978072},
issn = {1741-2552},
abstract = {Objective Invasive brain-computer interfaces (iBCIs) have evolved significantly since the first neurotrophic electrode was implanted in a human subject three decades ago. Since then, both hardware and software advances have increased the iBCI performance to enable tasks such as decoding conversations in real-time and manipulating external limb prostheses with haptic feedback. In this systematic review, we aim to evaluate the advances in iBCI hardware, software and functionality and describe challenges and opportunities in the iBCI field. Approach Medline, EMBASE, and Cochrane databases were searched from inception until 13 April 2024. Primary studies reporting the use of iBCI in human subjects to restore function were included. Endpoints extracted include iBCI electrode type, iBCI implantation, decoder algorithm, iBCI effector, testing and training methodology and functional outcomes. Narrative synthesis of outcomes was done with a focus on hardware and software development trends over time. Individual patient data (IPD) was also collected and an IPD meta-analysis was done to identify factors significant to iBCI performance. Main results 93 studies involving 214 patients were included in this systematic review. The median task performance accuracy for cursor control tasks was 76.00% (Interquartile range [IQR] = 21.2) , for motor tasks was 80.00% (IQR = 23.3), and for communication tasks was 93.27% (IQR = 15.3). Current advances in iBCI software include use of recurrent neural network architectures as decoders, while hardware advances such as intravascular stentrodes provide a less invasive alternative for neural recording. Challenges include the lack of standardized testing paradigms for specific functional outcomes and issues with portability and chronicity limiting iBCI usage to laboratory settings. Significance Our systematic review demonstrated the exponential rate at which iBCIs have evolved over the past two decades. Yet, more work is needed for widespread clinical adoption and translation to long-term home-use. .},
}

@article {pmid39979351,
year = {2025},
author = {Gupta, E and Sivakumar, R},
title = {Response coupling with an auxiliary neural signal for enhancing brain signal detection.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {6227},
pmid = {39979351},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Brain/physiology ; Adult ; Male ; Signal Processing, Computer-Assisted ; Female ; Algorithms ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) offer an implicit, non-linguistic communication channel between users and machines. Despite their potential, BCIs are far from becoming a mainstream communication modality like text and speech. While non-invasive BCIs, such as Electroencephalography, are favored for their ease of use, their broader adoption is limited by challenges related to signal noise, artifacts, and variability across users. In this paper, we propose a novel method called response coupling, aimed at enhancing brain signal detection and reliability by pairing a brain signal with an artificially induced auxiliary signal and leveraging their interaction. Specifically, we use error-related potentials (ErrPs) as the primary signal and steady-state visual evoked potentials (SSVEPs) as the auxiliary signal. SSVEPs, known for their phase-locked responses to rhythmic stimuli, are selected because rhythmic neural activity plays a critical role in sensory and cognitive processes, with evidence suggesting that reinforcing these oscillations can improve neural performance. By exploring the interaction between these two signals, we demonstrate that response coupling significantly improves the detection accuracy of ErrPs, especially in the parietal and occipital regions. This method introduces a new paradigm for enhancing BCI performance, where the interaction between a primary and an auxiliary signal is harnessed to enhance the detection performance. Additionally, the phase-locking properties of SSVEPs allow for unsupervised rejection of suboptimal data, further increasing BCI reliability.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
*Evoked Potentials, Visual/physiology
*Brain/physiology
Adult
Male
Signal Processing, Computer-Assisted
Female
Algorithms
Young Adult

@article {pmid39977922,
year = {2024},
author = {Lai, J and Qin, T and Boas, D and Sakadzic, S and Ayata, C and Chung, D},
title = {Anesthetic Effects on Neuronally-based Resting-state Functional Connectivity (S43.008).},
journal = {Neurology},
volume = {102},
number = {7_supplement_1},
pages = {3357},
doi = {10.1212/WNL.0000000000205084},
pmid = {39977922},
issn = {1526-632X},
mesh = {Animals ; *Isoflurane/pharmacology/analogs & derivatives ; *Ketamine/pharmacology/analogs & derivatives ; Mice ; *Magnetic Resonance Imaging ; *Xylazine/pharmacology ; Anesthetics/pharmacology ; Brain/drug effects/diagnostic imaging ; Ethanol/pharmacology/analogs & derivatives ; Neurons/drug effects/physiology ; Male ; Rest ; Mice, Transgenic ; Neural Pathways/drug effects ; },
abstract = {OBJECTIVE: To investigate the impact of tribromoethanol, isoflurane, and ketamine/xylazine on neuronally- and hemodynamically-based functional connectivity.

BACKGROUND: Resting-state functional connectivity (RSFC) captures correlated signals among brain regions while at rest. In humans, RSFC is imaged using fMRI by tracking spontaneous blood-oxygen-level-dependent (BOLD) fluctuations. Although distinct anesthetics have been shown to modulate RSFC in mice via a BOLD-like hemodynamic signal, the exploration of their effects on the neuronally based signals is less well known.

DESIGN/METHODS: We used Thy1-GCaMP6f mice with a genetically encoded calcium indicator in excitatory pyramidal neurons, to detect neuronal calcium activity. We implanted a chronic imaging window, followed by GCaMP fluorescence and optical intrinsic signal imaging. Each mouse sequentially received each of the anesthetics-tribromoethanol, isoflurane, or ketamine/xylazine-in random order. We calculated several connectivity metrics including a bihemispheric connectivity index (BCI) to determine the overall connectivity between homotopic regions on each hemisphere. Correlation coefficients were z-transformed to enable comparisons between groups.

RESULTS: Tribromoethanol consistently exhibited the highest BCI values. Tribromoethanol's z-transformed BCI for neuronal GCaMP and hemodynamic connectivity was significantly higher than the metrics for ketamine/xylazine and isoflurane (tribromoethanol 1.06, ketamine/xylazine 0.67, isoflurane 0.80, p < 0.01 for tribromoethanol vs. others). Ketamine/xylazine displayed reduced variability when compared to tribromoethanol and isoflurane. All anesthetics had high correlations between the GCaMP signal and the oxy-hemoglobin signal, with ketamine/xylazine displaying the highest z-transformed correlation of the group (ketamine/xylazine 1.33, tribromoethanol 1.09, isoflurane 1.01), p < 0.01 for KX vs. others).

CONCLUSIONS: While all three anesthetics demonstrate varied effects, tribromoethanol notably optimized BCI compared to ketamine/xylazine and iso. Isoflurane displayed marked variability. This study underscores the importance of anesthetic selection for studies involving functional connectivity. Disclosure: Mr. Lai has nothing to disclose. Tao Qin has received personal compensation for serving as an employee of Helix Nanotechnologies. Prof. Boas has received personal compensation for serving as an employee of Boston University. An immediate family member of Prof. Boas has received personal compensation for serving as an employee of Massachusetts General Hospital. The institution of Prof. Boas has received research support from NIH. The institution of Dr. Sakadzic has received research support from National Institute of Health. Dr. Ayata has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Quris. Dr. Ayata has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Neurelis. The institution of Dr. Ayata has received research support from NIH. The institution of Dr. Ayata has received research support from Takeda. The institution of Dr. Ayata has received research support from Neurelis. Dr. Chung has received research support from NIH/NINDS. Dr. Chung has received research support from The Aneurysm and AVM Foundation.},
}

Animals
*Isoflurane/pharmacology/analogs & derivatives
*Ketamine/pharmacology/analogs & derivatives
Mice
*Magnetic Resonance Imaging
*Xylazine/pharmacology
Anesthetics/pharmacology
Brain/drug effects/diagnostic imaging
Ethanol/pharmacology/analogs & derivatives
Neurons/drug effects/physiology
Male
Rest
Mice, Transgenic
Neural Pathways/drug effects

@article {pmid39976033,
year = {2025},
author = {Li, L and Li, B and Wang, G and Li, S and Li, X and Santos, J and González, AM and Guo, L and Tu, Y and Qin, Y},
title = {Research on Precision Medicine AI Algorithm for Neuro Immune Gastrointestinal Diseases based on Quantum Biochemistry and Computational Cancer Genetics.},
journal = {Current pharmaceutical biotechnology},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113892010348489241210060447},
pmid = {39976033},
issn = {1873-4316},
abstract = {OBJECTIVE: The objective of this study is to conduct network toxicology analysis based on smoking habits and develop a simpler and more effective toxicology product ingestion control system.

BACKGROUND: Smoking behavior can affect the pathogenesis and prognosis of neuroimmune gastrointestinal diseases.

AIMS: The purpose of developing tools to assist clinical practice is to avoid the harm of cigarettes to the human body.

METHODS: Molecular dynamics method was used to elucidate the biophysical mechanism of TP53 gene mutation caused by harmful ingredients, and the signaling pathway of midbrain edge excitation was determined by molecular dynamics of nicotine and dopamine receptor D3. The possible involvement of nicotine in neuronal damage was determined through the molecular interaction between nicotine and ACHE. Molecular pathways were analyzed based on the aforementioned biological principles, developed artificial intelligence systems and brain computer interface systems.

RESULTS: Several signaling pathways were elucidated, and effective AI algorithms were developed.

CONCLUSION: The accuracy of artificial intelligence systems is over 70%. This study provides clinical doctors with a new precision medicine strategy and tool to regulate patient behavior and reduce disease risk. Other: This project was approved by the Ethics Committee of Chifeng Cancer Hospital and reported to the WHO.},
}

@article {pmid39975237,
year = {2025},
author = {Cubillos, LH and Kelberman, MM and Mender, MJ and Hite, A and Temmar, H and Willsey, M and Kumar, NG and Kung, TA and Patil, PG and Chestek, C and Krishnan, C},
title = {Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.02.03.636273},
pmid = {39975237},
issn = {2692-8205},
abstract = {UNLABELLED: Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements-something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated if brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, alone it may not reveal the "true" control space needed to improve decoder performance or generalizability. Further research is required to determine whether synergies are the optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.

SIGNIFICANCE STATEMENT: Many researchers believe that brain and muscle synergies represent a fundamental control strategy and could enhance brain-machine interface (BMI) decoding performance. These synergies, extracted through dimensionality reduction techniques, are thought to simplify complex neural data, improving the efficiency and accuracy of BMI systems. In our study, we evaluated brain and muscle synergies in a dexterous finger task. We found that while these synergies effectively compressed high-dimensional data, they did not improve performance through denoising or generalize well across different contexts. Instead, the highest performance was achieved when using all available data, suggesting that synergies, although useful for data compression, may not provide the "true" control space needed to enhance decoder robustness or adaptability in implanted BMI systems.},
}

@article {pmid39974888,
year = {2025},
author = {Letner, JG and Lam, JLW and Copenhaver, MG and Barrow, M and Patel, PR and Richie, JM and Lee, J and Kim, HS and Cai, D and Weiland, JD and Phillips, J and Blaauw, D and Chestek, CA},
title = {A method for efficient, rapid, and minimally invasive implantation of individual non-functional motes with penetrating subcellular-diameter carbon fiber electrodes into rat cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.02.05.636655},
pmid = {39974888},
issn = {2692-8205},
abstract = {OBJECTIVE: Distributed arrays of wireless neural interfacing chips with 1-2 channels each, known as "neural dust", could enhance brain machine interfaces (BMIs) by removing the wired connection through the scalp and increasing biocompatibility with their submillimeter size. Although several approaches for neural dust have emerged, a procedure for implanting them in batches that builds upon the safety and performance of currently used electrodes remains to be demonstrated.

APPROACH: Here, we demonstrate the feasibility of implanting batches of wireless motes that rest on the cortical surface with carbon fiber electrodes of subcellular diameter (6.8-8.4 µm) that penetrate to a target brain depth of 1 mm without insertion aids. To simulate their implantation, we assembled more than 230 mechanically equivalent motes and affixed them to insertion tools with polyethylene glycol (PEG), a quickly dissolvable and biocompatible material. Then, we implanted mote grids of multiple configurations into rat cortex in vivo and evaluated insertion success and their arrangement on the brain surface using photos and videos captured during their implantation.

MAIN RESULTS: When placing motes onto the insertion device, we found that they aggregated in molten PEG such that the array pitch was only 5% wider than the dimensions of the mote bases themselves (240 x 240 µm). Overall, we found that motes with this arrangement could be inserted into rat cortex with a high success rate, as 171/186 (92%) motes in 4x4 (N=4) and 5x5 (N=5) square grid configurations were successfully inserted using the insertion device alone. After implantation, measurements of how much motes tilted (22±9°, X̄±S) and had been displaced relative to their original positions were smaller than those measured for devices implanted inside the brain in the literature.

SIGNIFICANCE: Collectively, these data establish the viability of safely implementing motes with ultrasmall electrodes and epicortically-situated chips for use in future BMIs.},
}

@article {pmid39973870,
year = {2025},
author = {Kanagaluru, V and M, S},
title = {Artificial intelligence based BCI using SSVEP signals with single channel EEG.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {},
number = {},
pages = {9287329241302740},
doi = {10.1177/09287329241302740},
pmid = {39973870},
issn = {1878-7401},
abstract = {BACKGROUND: Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices. Steady-state visual-evoked potentials (SSVEPs) are particularly useful in BCIs because of their rapid communication capabilities and minimal calibration requirements. Although SSVEP-based BCIs are highly effective, traditional classification methods face challenges in maintaining high accuracy with minimal EEG channels, especially in real-world applications. There is a growing need for improved classification techniques to enhance performance and efficiency.

OBJECTIVE: The aim of this research is to improve the classification of SSVEP signals using machine-learning algorithms. This involves extracting dominant frequency features from SSVEP data and applying classifiers such as Decision Tree (DT), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to achieve high accuracy while reducing the number of EEG channels required, making the method practical for BCI applications.

METHODS: SSVEP data were collected from the Benchmark Dataset at Tsinghua BCI Lab using 64 EEG channels per subject. The Oz channel was selected as the dominant channel for analysis. Wavelet decomposition (db4) was used to extract frequency features in the range 7.8 Hz to 15.6 Hz. The frequency of the maximum amplitude within a 5-s window was extracted as the key feature, and machine learning models (DT, LDA, and SVM) were applied to classify these features.

RESULTS: The proposed method achieved a high classification accuracy, with 95.8% for DT and 96.7% for both LDA and SVM. These results show significant improvement over existing methods, indicating the potential of this approach for BCI applications.

CONCLUSION: This study demonstrates that SSVEP classification using machine-learning models improves accuracy and efficiency. The use of wavelet decomposition for feature extraction and machine learning for classification offers a robust method for SSVEP-based BCIs. This method is promising for assistive technologies and other BCI applications.},
}

@article {pmid39970032,
year = {2025},
author = {M, AL and R, R},
title = {Comprehensive analysis of prefrontal cortex-directional rhythms categorization for rehabilitation.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2025.2467460},
pmid = {39970032},
issn = {1476-8259},
abstract = {Prefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast to existing machine learning-based approaches, this generalized RBF-SVM classifier effectively identified observed data with an overall 96.91% accuracy validated with a 10-fold repeated random train test split cross validation technique using confusion matrix analysis.},
}

@article {pmid39969013,
year = {2025},
author = {Magalhães, SS and Lucas-Ochoa, AM and Gonzalez-Cuello, AM and Fernández-Villalba, E and Pereira Toralles, MB and Herrero, MT},
title = {The mind-machine connection: adaptive information processing and new technologies promoting mental health in older adults.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {},
number = {},
pages = {10738584251318948},
doi = {10.1177/10738584251318948},
pmid = {39969013},
issn = {1089-4098},
abstract = {The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user's brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to learn and adapt to new technologies such as BCIs, but both (human brain and BCI) demonstrate adaptability in their responses. The human brain is skilled at quickly switching between tasks and regulating emotions, while BCIs can modify signal-processing algorithms to accommodate changes in brain activity. Furthermore, the human brain and BCI participate in knowledge acquisition; the first one strengthens cognitive abilities through exposure to new experiences, and the second one improves performance through ongoing adjustment and improvement. Current research seeks to incorporate emotional states into BCI systems to improve the user experience, despite the exceptional emotional regulation abilities of the human brain. The implementation of BCIs for older adults could be more effective, inclusive, and beneficial in improving their quality of life. This review aims to improve the understanding of brain-machine interfaces and their implications for mental health in older adults.},
}

@article {pmid39968680,
year = {2025},
author = {Hao, W and Yang, S and Sheng, Y and Ye, C and Han, L and Zhou, Z and Cui, W},
title = {Efficient expression of recombinant proteins in Bacillus subtilis using a rewired gene circuit of quorum sensing.},
journal = {Biotechnology progress},
volume = {},
number = {},
pages = {e70007},
doi = {10.1002/btpr.70007},
pmid = {39968680},
issn = {1520-6033},
support = {32171420//National Natural Science Foundation of China/ ; KLIB-KF202307//Open Project of Key Laboratory of Industrial Biotechnology, Ministry of Education/ ; 2023YFC3402402//National Key Research and Development Program of China/ ; },
abstract = {Bacillus subtilis is a favored chassis for high productivity of several high value-added product in synthetic biology. Efficient production of recombinant proteins is critical but challenging using this chassis because these expression systems in use, such as constitutive and inducible expression systems, demand for coordination of cell growth with production and addition of chemical inducers. These systems compete for intracellular resources with the host, eventually resulting in dysfunction of cell survival. To overcome the problem, in this study, LuxRI quorum sensing (QS) system from Aliivibrio fischeri was functionally reconstituted in B. subtilis for achieving coordinated protein overproduction with cell growth in a cell-density-dependent manner. Furthermore, the output-controlling promoter, PluxI, was engineered through two rounds of evolution, by which we identified four mutants, P22, P47, P56, and P58 that exhibited elevated activity compared to the original PluxI. By incorporating a strong terminator (TB5) downstream of the target gene further enhanced expression level. The expression level of this system surpasses commonly used promoter-based systems in B. subtilis like P43 and PylbP. The LuxRI QS system proves to be a potent platform for recombinant protein overproduction in B. subtilis.},
}

@article {pmid39965558,
year = {2025},
author = {Persson, AC and Eeg-Olofsson, M and Sadeghi, A and Lepp, M},
title = {Patients experiences of an active transcutaneous implant: The Bone Conduction Implant.},
journal = {Audiology & neuro-otology},
volume = {},
number = {},
pages = {1-20},
doi = {10.1159/000544774},
pmid = {39965558},
issn = {1421-9700},
abstract = {INTRODUCTION: The aim of this qualitative study was to explore and describe patients' experiences of using and living with the Bone Conduction Implant (BCI).

METHODS: Semi-structured interviews were conducted with 10 BCI users and analyzed according to the phenomenographic approach.

RESULTS: Four conceptual themes were formed during the analysis; (1) conceptions of the process receiving the BCI, (2) conceptions of handling the BCI on a daily basis, (3) conceptions of hearing with the BCI, and (4) Conceptions of health care issues related to the BCI. The participants statements include experiences of improved hearing and self-esteem by using the BCI. Noisy situations and not being able to hear in daily life situations causes frustrations. The participants described anxiety about consequences following an MRI examination. The audio processor is easy to handle but the fact that it is not waterproof raise concerns. Despite some frustration and concerns, participants state that the audio processor has become a part of them, and they cannot imagine being without it.

CONCLUSION: The ability to hear and communicate with other people have a great impact on the participants daily life quality, and their statements shows the importance hearing has on their lives and how they perceive themselves. The BCI seems to be a good hearing rehabilitation alternative for the participants, and they state that the audio processor is easy to use and handle.},
}

@article {pmid39963381,
year = {2025},
author = {Wang, N and He, Y and Zhu, S and Liu, D and Chai, X and He, Q and Cao, T and He, J and Li, J and Si, J and Yang, Y and Zhao, J},
title = {Functional near-infrared spectroscopy for the assessment and treatment of patients with disorders of consciousness.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1524806},
pmid = {39963381},
issn = {1664-2295},
abstract = {BACKGROUND: Advances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.

AIM: This review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.

METHODS: A systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.

RESULTS: Analysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).

CONCLUSION: As a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.},
}

@article {pmid39957341,
year = {2025},
author = {Zhai, H and Li, P and Wang, H and Wang, X},
title = {Temperature and steric hindrance-regulated selective synthesis of ketamine derivatives and 2-aryl-cycloketone-1-carboxamides via nucleophilic substitution and Favorskii rearrangement.},
journal = {Organic & biomolecular chemistry},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4ob02039a},
pmid = {39957341},
issn = {1477-0539},
abstract = {A selective temperature and steric hindrance-regulated method for nucleophilic substitution or Favorskii rearrangement reactions of 2-aryl-2-bromo-cycloketones with aliphatic amines has been developed to prepare ketamine derivatives and 2-aryl-cycloketone-1-carboxamides. In the presence of secondary amines or ortho-substituted 2-aryl-2-bromocycloketones, steric hindrance directs the Favorskii rearrangement to occur. Conversely, with primary amines, the product ratio of nucleophilic substitution to Favorskii rearrangement is temperature-dependent, with higher temperatures favoring the Favorskii rearrangement. At lower temperatures (-25 °C or below), nucleophilic substitution predominates, yielding ketamine derivatives in yields of 60% to 85%. This method effectively utilizes temperature and steric hindrance to control the reaction pathway and optimize product formation.},
}

@article {pmid39957214,
year = {2025},
author = {Chinta, B and Pampana, M and M, M},
title = {An efficient deep learning approach for automatic speech recognition using EEG signals.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/10255842.2025.2456982},
pmid = {39957214},
issn = {1476-8259},
abstract = {Electroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2% accuracy, outperforming baseline methods. This framework advances EEG based speech recognition aiding brain-computer interfaces and assistive technologies for individuals with speech disorders.},
}

@article {pmid39956854,
year = {2025},
author = {Zou, W and Fan, Y and Liu, J and Cheng, H and Hong, H and Al-Sheikh, U and Li, S and Zhu, L and Li, R and He, L and Tang, YQ and Zhao, G and Zhang, Y and Wang, F and Zhan, R and Zheng, X and Kang, L},
title = {Anoctamin-1 is a core component of a mechanosensory anion channel complex in C. elegans.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1680},
pmid = {39956854},
issn = {2041-1723},
support = {2021ZD0203303//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 31771113//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31471023//National Natural Science Foundation of China (National Science Foundation of China)/ ; LZ22C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Caenorhabditis elegans/metabolism/genetics ; *Mechanotransduction, Cellular ; *Caenorhabditis elegans Proteins/metabolism/genetics ; *Anoctamin-1/metabolism/genetics ; Humans ; Touch/physiology ; Neurons/metabolism ; Chloride Channels/metabolism/genetics ; },
abstract = {Mechanotransduction channels are widely expressed in both vertebrates and invertebrates, mediating various physiological processes such as touch, hearing and blood-pressure sensing. While previously known mechanotransduction channels in metazoans are primarily cation-selective, we identified Anoctamin-1 (ANOH-1), the C. elegans homolog of mammalian calcium-activated chloride channel ANO1/TMEM16A, as an essential component of a mechanosensory channel complex that contributes to the nose touch mechanosensation in C. elegans. Ectopic expression of either C. elegans or human Anoctamin-1 confers mechanosensitivity to touch-insensitive neurons, suggesting a cell-autonomous role of ANOH-1/ANO1 in mechanotransduction. Additionally, we demonstrated that the mechanosensory function of ANOH-1/ANO1 relies on CIB (calcium- and integrin- binding) proteins. Thus, our results reveal an evolutionarily conserved chloride channel involved in mechanosensory transduction in metazoans, highlighting the importance of anion channels in mechanosensory processes.},
}

Animals
*Caenorhabditis elegans/metabolism/genetics
*Mechanotransduction, Cellular
*Caenorhabditis elegans Proteins/metabolism/genetics
*Anoctamin-1/metabolism/genetics
Humans
Touch/physiology
Neurons/metabolism
Chloride Channels/metabolism/genetics

@article {pmid39954182,
year = {2025},
author = {Jangir, G and Joshi, N and Purohit, G},
title = {Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {5},
pmid = {39954182},
issn = {2198-4018},
abstract = {Human brain signal processing and finger's movement coordination is a complex mechanism. In this mechanism finger's movement is mostly performed for every day's task. It is well known that to capture such movement EEG or ECoG signals are used. In this order to find the patterns from these signals is important. The BCI competition 4 dataset 4 is one such standard dataset of ECoG signals for individual finger movement provided by University of Washington, USA. In this work, this dataset is, statistically analyzed to understand the nature of data and outliers in it. Effectiveness of pre-processing algorithm is then visualized. The cleaned dataset has dual polarity and gaussian distribution nature which makes Tanh activation function suitable for the neural network BC4D4 model. BC4D4 uses Convolutional neural network for feature extraction, dense neural network for pattern identification and incorporating dropout & regularization making the proposed model more resilient. Our model outperforms the state of the art work on the dataset 4 achieving 0.85 correlation value that is 1.85X (Winner of BCI competition 4, 2012) & 1.25X (Finger Flex model, 2022).},
}

@article {pmid39953165,
year = {2025},
author = {Jordan, S and Buchmann, M and Loss, J and Okan, O},
title = {[Health literacy and health behaviour-insights into a developing field of research and action for public health].},
journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz},
volume = {},
number = {},
pages = {},
pmid = {39953165},
issn = {1437-1588},
abstract = {The research and action field of health literacy and health behaviour is increasingly differentiating. General health literacy is established and focuses on population-based studies. Specific health literacy for health behaviour offers topic-related starting points for interventions and public health strategies.There are various concepts, definitions and measurement instruments for general health literacy and specific health literacy in the areas of nutrition and physical activity. These differ in terms of the levels of action and areas of application of health literacy.Most studies show a positive association between health literacy and various health behaviours. Higher health literacy is more often associated with improved health-promoting behaviour. This applies to both general as well as specific health literacy regarding nutrition and exercise (physical activity). Some studies found no correlation for certain behaviours, while others only found correlations for certain groups, which may be due to the different measuring instruments and research contexts. This points to the importance of always considering the interaction between behaviour and circumstances in order to improve the fit between the individual and the everyday demands of dealing with health information.The behavioural and cultural insights (BCI) approach can provide insights into how to promote health literacy with regard to various health behaviours, individual barriers and facilitators that arise from life circumstances and conditions, and that take social practice into account. BCI and health literacy complement each other and have the potential to make strategies for improving health behaviour more effective and targeted.},
}

@article {pmid39950750,
year = {2025},
author = {Jiang, E and Huang, T and Yin, X},
title = {A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.},
journal = {Journal of medical engineering & technology},
volume = {},
number = {},
pages = {1-14},
doi = {10.1080/03091902.2025.2463577},
pmid = {39950750},
issn = {1464-522X},
abstract = {Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose notable challenges, particularly in mental tasks like motor imagery. Introducing fuzzy components can enhance the system's ability to withstand noisy environments. The emergence of deep learning has significantly impacted artificial intelligence and data analysis, prompting extensive exploration into assessing and understanding brain signals. This work introduces a hybrid series architecture called FCLNET, which combines Compact-CNN to extract frequency and spatial features alongside the LSTM network for temporal feature extraction. The activation functions in the CNN architecture were implemented using type-2 fuzzy functions to tackle uncertainties. Hyperparameters of the FCLNET model are tuned by the Bayesian optimisation algorithm. The efficacy of this approach is assessed through the BCI Competition IV-2a database and the BCI Competition IV-1 database. By incorporating type-2 fuzzy activation functions and employing Bayesian optimisation for tuning, the proposed architecture indicates good classification accuracy compared to the literature. Outcomes showcase the exceptional achievements of the FCLNET model, suggesting that integrating fuzzy units into other classifiers could lead to advancements in motor imagery-based BCI systems.},
}

@article {pmid39949790,
year = {2025},
author = {Ding, F and Ying, Y and Jin, Y and Guo, X and Xu, Y and Yu, Z and Jiang, H},
title = {Reduced frontotemporal connectivity during a verbal fluency task in patients with anxiety, sleep, and major depressive disorders.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1542346},
pmid = {39949790},
issn = {1664-2295},
abstract = {BACKGROUND: It has been well established that psychiatric disorders are often accompanied by cognitive dysfunction. Previous studies have investigated the verbal fluency task (VFT) for detecting executive function impairment in different psychiatric disorders, but the sensitivity and specificity of this task in different psychiatric disorders have not been explored. Furthermore, clarifying the mechanisms underlying variations in executive function impairments across multiple psychiatric disorders will enhance our comprehension of brain activity alternations among these disorders. Therefore, this study combined the VFT and the functional near-infrared spectroscopy (fNIRS) to investigate the neural mechanisms underlying the impairment of executive function across psychiatric disorders including anxiety disorder (AD), sleep disorder (SD) and major depressive disorder (MDD).

METHODS: Two hundred and eight participants were enrolled including 52 AD, 52 SD, 52 MDD and 52 healthy controls (HCs). All participants completed the VFT while being monitored using fNIRS to measure changes in brain oxygenated hemoglobin (Oxy-Hb).

RESULTS: Our results demonstrated that MDD, AD and SD exhibited decreased overall connectivity strength, as well as reduced connected networks involving the frontal and temporal regions during the VFT comparing to HC. Furthermore, the MDD group showed a reduction in connected networks, specifically in the left superior temporal gyrus and precentral gyrus, compared to the AD group.

CONCLUSION: Our study offers neural evidence that the VFT combined with fNIRS could effectively detect executive function impairment in different psychiatric disorders.},
}

@article {pmid39948616,
year = {2025},
author = {Gedela, NSS and Radawiec, RD and Salim, S and Richie, J and Chestek, C and Draelos, A and Pelled, G},
title = {In vivo electrophysiology recordings and computational modeling can predict octopus arm movement.},
journal = {Bioelectronic medicine},
volume = {11},
number = {1},
pages = {4},
pmid = {39948616},
issn = {2332-8886},
abstract = {The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.},
}