@article {pmid42309989,
year = {2026},
author = {Di Liberto, GM and Ip, EYJ},
title = {Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?.},
journal = {The European journal of neuroscience},
volume = {63},
number = {12},
pages = {e70496},
doi = {10.1111/ejn.70496},
pmid = {42309989},
issn = {1460-9568},
support = {13/RC/2106_P2//Research Ireland/ ; 18/CRT/6224//Research Ireland/ ; },
mesh = {Humans ; *Speech/physiology ; *Speech Perception/physiology ; *Brain/physiology ; Brain Mapping/methods ; *Neurophysiology/methods ; },
abstract = {Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field towards more ecologically valid approaches. Here, we describe the evolving landscape of experimental designs in speech neurophysiology, from discrete to continuous stimuli and from socially isolated listening to dynamic, multiagent communication. Realistic paradigms in that space challenge conventional methods, offering richer insights into neural encoding, functional brain mapping and neural entrainment. At the same time, they introduce significant analytical and technical complexities, particularly when incorporating social interaction. By synthesising findings across studies, we highlight how these ecologically valid speech paradigms have been contributing to refining theories of language processing and open new avenues for research. In doing so, this review critically evaluates of whether the move towards realism in speech neurophysiology represents a technological trend or a transformative leap in understanding the neural underpinnings of speech communication.},
}

Humans
*Speech/physiology
*Speech Perception/physiology
*Brain/physiology
Brain Mapping/methods
*Neurophysiology/methods

@article {pmid42310346,
year = {2026},
author = {Yeom, HG and Choi, WS and An, KM},
title = {A novel transformer architecture for EEG decoding and neuroscientific analysis.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-56405-9},
pmid = {42310346},
issn = {2045-2322},
support = {K207700005//Chosun University/ ; },
abstract = {Deep learning has significantly advanced brain-computer interface (BCI) technology. However, most deep learning models operate as black boxes, limiting their clinical applicability and scientific interpretability. This lack of transparency makes it difficult to determine whether predictions are driven by genuine neural activity or artifacts. To address this limitation, we propose Analformer, a novel Transformer-based architecture designed to achieve both high predictive performance and neuroscientific interpretability. The core component of Analformer is an Analytical Patch Embedding module, which employs fixed, non-trainable Morlet wavelet kernels to extract explainable spatio-temporal-frequency features from raw EEG signals. This structure enables standard neurophysiological analyses-including time-frequency analysis, topography, and F-value time-frequency (FTF) analysis-to be derived directly from the model's internal representations. Furthermore, by analyzing attention weights over these interpretable features, Analformer provides attention-based connectivity that may reflect functional relationships between brain regions. We evaluated Analformer on two large public datasets covering three representative BCI paradigms: Motor Imagery (MI), event-related potentials (ERP), and steady-state visually evoked potentials (SSVEP). Experimental results demonstrate that Analformer achieves competitive performance across all paradigms while producing analysis outputs consistent with established neuroscientific findings. These results suggest that Analformer provides a unified framework that bridges high-performance BCI decoding with interpretable, data-driven scientific analysis.},
}

@article {pmid42310450,
year = {2026},
author = {Deo, DR and Okorokova, EV and Pritchard, AL and Hahn, NV and Card, NS and Nason-Tomaszewski, SR and Jude, J and Hosman, T and Choi, EY and Qiu, D and Meng, Y and Wairagkar, M and Nicolas, C and Kamdar, FB and Iacobacci, C and Acosta, A and Hochberg, LR and Cash, SS and Williams, ZM and Rubin, DB and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Downey, JE and Bensmaia, SJ and Henderson, JM and Willett, FR},
title = {A mosaic of whole-body representations on the human precentral gyrus.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {42310450},
issn = {1476-4687},
abstract = {Understanding how the body is represented in the motor cortex is key to understanding how the brain controls movement. Although the motor cortex has been mapped in animal models at a fine scale[1-10], characterization in humans remains primarily limited to low-resolution recording[11-16] and stimulation techniques[17-20]. Here we created a comprehensive map of the human motor cortex at single-neuron resolution, spanning microelectrode array recordings from 20 arrays across 8 individuals with paralysis from spinal cord injury, amyotrophic lateral sclerosis or brainstem stroke, all enrolled in brain-computer interface clinical trials. These arrays broadly sample the crown of the precentral gyrus (PCG; thought to be composed largely of the premotor cortex (Brodmann area 6)). We found that body parts were highly intermixed, such that the entire body was represented in all sampled locations of the PCG, although the relative strength of body parts was roughly consistent with the motor homunculus[17,18]. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them. Throughout the PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (for example, toe curl and hand close) having correlated representations. These data provide evidence consistent with an intermixed, interrelated and behaviour-centred organization of the motor cortex[3,21]. The resulting map also provides important targeting information for brain-computer interfaces that seek to restore motor function.},
}

@article {pmid42311454,
year = {2026},
author = {D'Aquino, A and Schack, T},
title = {Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1849674},
pmid = {42311454},
issn = {1662-5161},
abstract = {Motor Imagery (MI) Brain-Computer Interfaces (BCIs) represent a promising technology for neurorehabilitation and assistive control. However, the clinical viability of these systems is frequently hindered by the inherent limitations of electroencephalography (EEG) with regard to its low signal-to-noise ratio (SNR), non-stationarity, and high inter-subject variability. Standard decoding methods often fail to capture the complexity of user intention leading to unreliable performance and user frustration. This review proposes a solution to these challenges by advocating for the integration of passive eye movements (EM) as a complementary data stream. The theoretical rationale for this approach rests on the neurocognitive principle of functional equivalence. Because imagined actions recruit similar visuomotor networks to those used in physical execution, EM constitute a robust correlate of the underlying neural simulation. We distinguish this approach from conventional hybrid systems that use gaze coordinates for active control. Instead, we argue for a framework of passive monitoring where oculomotor metrics, including pupil diameter, fixation patterns, and saccadic dynamics, serve as a continuous window into the user's cognitive state. We synthesize evidence demonstrating that these passive signals can reliably index cognitive load, attentional allocation, and covert motor planning. By fusing these behavioral metrics with EEG, a BCI can disambiguate uncertain neural patterns and verify user intent without imposing additional task demands. Furthermore, we discuss how this multimodal integration enables the development of adaptive classifiers that respond to fluctuations in user fatigue and engagement. Bridging the gap between cognition and control through passive EM monitoring offers a pathway to create BCI systems that are intrinsically responsive to the user's internal state.},
}

@article {pmid42135191,
year = {2026},
author = {Carleton, M and Oesch, NW},
title = {Inhibitory Circuits Can Restore OFF Pathway Responses in Retinal Prostheses.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {46},
number = {24},
pages = {},
doi = {10.1523/JNEUROSCI.0083-26.2026},
pmid = {42135191},
issn = {1529-2401},
mesh = {Animals ; *Visual Prosthesis ; Mice ; Male ; *Retinal Ganglion Cells/physiology ; *Retinitis Pigmentosa/physiopathology/therapy ; Female ; *Visual Pathways/physiology ; *Neural Inhibition/physiology ; Electric Stimulation ; Amacrine Cells/physiology ; Mice, Inbred C57BL ; Photic Stimulation ; },
abstract = {One of the first steps in processing visual information is to split the light signal captured by photoreceptors into complementary ON and OFF pathways, which separately encode increases and decreases in luminance. In blind patients with retinal degeneration, optoelectronic prostheses can successfully activate the ON pathway and evoke bright percepts; however, patients do not perceive dark features. This indicates that the OFF pathway is not being activated by existing prosthetics. To quantify OFF pathway deficits, we stimulated retinal ganglion cells (RGCs) in a mouse model of retinitis pigmentosa of either sex with electrical stimulation mimicking retinal prosthetic activation and recorded their voltage responses using whole-cell recording. We found that most OFF RGCs respond with incorrect ON responses, except for one specific subtype of RGC, the OFFα, which retained correct OFF-type responses following the termination of stimulation. We found that these preserved OFF responses were driven by postinhibitory rebound excitation, mediated by hyperpolarization-activated cyclic nucleotide-gated channels. Using a combinatorial genetic approach to achieve chemogenetic control, we identified AII amacrine cells as the presynaptic source driving these electrically evoked OFF responses. These insights into how the OFF pathway responds to artificial stimulation suggest new opportunities to improve prosthetic vision restoration through tuning of stimulation parameters.},
}

Animals
*Visual Prosthesis
Mice
Male
*Retinal Ganglion Cells/physiology
*Retinitis Pigmentosa/physiopathology/therapy
Female
*Visual Pathways/physiology
*Neural Inhibition/physiology
Electric Stimulation
Amacrine Cells/physiology
Mice, Inbred C57BL
Photic Stimulation

@article {pmid42303138,
year = {2026},
author = {Gao, X and Lai, Y and Hu, W and Wang, L and Liu, Y and Liao, J},
title = {Spatial variations and risk factors of multimorbidity in China: A population-based spatial modelling study.},
journal = {American journal of preventive medicine},
volume = {},
number = {},
pages = {108481},
doi = {10.1016/j.amepre.2026.108481},
pmid = {42303138},
issn = {1873-2607},
abstract = {INTRODUCTION: Multimorbidity burden is likely to vary across China, but relevant evidence is insufficient. The extent to which individual and provincial factors may affect spatial variations of multimorbidity has not been fully examined. This study aims to estimate the provincial multimorbidity burden among Chinese aged 45 and older, identifying which risk factors remain constant and which vary across China.

METHODS: This study included 18,561 adults aged 45 and older from the China Health and Retirement Longitudinal Study in 2020. A Bayesian spatial varying coefficients model was adopted to estimate the multimorbidity burden and 95% Bayesian credible intervals, using the Chinese Multimorbidity-Weighted Index (CMWI) as a measurement. Partial correlation coefficients between covariates and CMWI in each region were calculated to investigate the need for varying coefficients. Spatial autocorrelation analyses were used to identify clusters of high and low multimorbidity burden.

RESULTS: The estimated CMWI across the 27 provinces in China ranged from 1.76 (95% BCI: 1.64, 1.89) to 4.42 (95% BCI: 4.16, 4.70). High multimorbidity burden areas were clustered in North and Northeast China, while areas with relatively low burden were in southern China. The top three provinces by median CMWI estimates were Neimenggu, Heilongjiang, and Jilin, whereas Guangdong, Zhejiang, and Beijing were among the lowest CMWI estimates. The effect of age and sex showed spatial variation across China, while other risk factors showed fixed effects.

CONCLUSIONS: The burden of multimorbidity varies across China and not all risk factors associated with multimorbidity are consistent across regions, providing valuable insights for chronic disease management.},
}

@article {pmid42303715,
year = {2026},
author = {Zhou, J and Li, W and Luo, S and Chen, K and Xu, S and Liu, Q and Chen, H and Liao, W and Li, J},
title = {Mapping the functional connectome between grey matter and white matter.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-10483-7},
pmid = {42303715},
issn = {2399-3642},
support = {62473082, 82121003, and 62036003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Brain white matter (WM) has traditionally been viewed as a passive conduit for neural transmission. However, evidence of blood oxygen level-dependent (BOLD) signals measured from the WM suggests its active participation in grey matter (GM) functional networks. Using 7-Tesla functional MRI (fMRI) data, we constructed a GM-WM functional connectome. We found that GM-WM functional architecture follows the unimodal-transmodal hierarchy of GM and is shaped by distributions of neurotransmitter receptors. Distinct WM networks exhibit unique connectivity profiles with GM, reflecting their roles in specific cognitive domains. Individual variations in this connectome correlated with cognitive performance. Notably, compared with the traditional GM-GM functional connectome, the GM-WM functional connectome shows stronger associations with brain disorders, suggesting greater diagnostic sensitivity as a neuromarker. These findings are replicated in a 3-Tesla fMRI cohort. Our work establishes WM as an integral component of the brain's functional architecture, contributing to hierarchical architecture and supporting higher-order cognition.},
}

@article {pmid42303998,
year = {2026},
author = {Zhang, H and Zhang, D and Wu, J and Li, Y and Lu, J},
title = {A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07619-z},
pmid = {42303998},
issn = {2052-4463},
support = {32371146//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371154//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128-256-channel subdural grids and synchronized with the audio; ECoG signals were down-sampled to 400 Hz, filtered in the high-gamma range (70-150 Hz), and used to derive high-gamma amplitude. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including high-gamma amplitude; word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.},
}

@article {pmid42304912,
year = {2026},
author = {Gong, L},
title = {Neuroimaging Mechanisms and Neuromodulation Strategies for Neuropsychiatric Disorders.},
journal = {Current neuropharmacology},
volume = {},
number = {},
pages = {},
doi = {10.2174/011570159X512508260603044250},
pmid = {42304912},
issn = {1875-6190},
}

@article {pmid42305049,
year = {2026},
author = {Li, C and Hu, R},
title = {Caught the 'Catch' of midnolin: structural basis for broad substrate specificity in ubiquitin-independent proteasomal degradation.},
journal = {Acta biochimica et biophysica Sinica},
volume = {58},
number = {6},
pages = {1431-1432},
doi = {10.3724/abbs.2026006},
pmid = {42305049},
issn = {1745-7270},
}

@article {pmid42305781,
year = {2026},
author = {Wang, J and Xu, G and Du, C and Li, Z and Li, H and Chen, S and Han, C and Zhang, S},
title = {Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1851006},
pmid = {42305781},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have been increasingly adopted to further improve MI-EEG decoding performance, thereby facilitating the practical deployment of BCIs. In transfer learning, the similarity between the source and target domains is a critical factor influencing its effectiveness. Given the analogous cortical activation patterns observed in MI and motor execution (ME) tasks, cross-task transfer learning from ME to MI presents a promising yet underexplored direction.

METHODS: To tackle the underexplored problem of cross-task transfer learning from ME to MI, we propose a domain-aware domain-class adaptation network (DDCA Net), which consists of a domain-shared feature extractor, two classifiers, and two domain-specific feature re-weighting blocks. Domain-level alignment is achieved by minimizing the maximum mean discrepancy between source and target feature distributions, while domain-specific feature re-weighting preserves discriminative characteristics unique to each task. In addition, a bi-classifier adversarial learning framework is employed to encourage consistency of decision boundaries across domains, thereby enabling implicit class-level alignment.

RESULTS: Extensive experiments were conducted on a public dataset with over 100 subjects under varying proportions of target-domain training samples. When 80% of target-domain samples are used for training, the proposed DDCA Net significantly outperforms the within-task baseline, achieving a 7.71% improvement in classification accuracy and converting approximately 80% of previously BCI-illiterate subjects into BCI-literate users.

DISCUSSION: To the best of our knowledge, this is the first work to verify the feasibility of applying domain adaptation for cross-task transfer learning in MI-EEG classification. The findings of this study provide new insights for integrating ME and MI in advanced BCIs.},
}

@article {pmid42305825,
year = {2024},
author = {DeRoo, SC and George, I},
title = {Aortic dissection after transcatheter aortic valve replacement.},
journal = {JTCVS structural and endovascular},
volume = {1-2},
number = {},
pages = {100009},
pmid = {42305825},
issn = {2950-6050},
}

@article {pmid42306198,
year = {2026},
author = {Li, X and Zhang, S and Song, Y and Zhang, S and Chen, X and Wang, Y and Gao, X},
title = {Text Sequence Stimulation for High-Speed and Comfortable SSVEP-BCI.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0612},
pmid = {42306198},
issn = {2692-7632},
abstract = {Steady-state visual evoked potential brain-computer interfaces offer a high-speed communication channel. However, traditional steady-state visual evoked potential paradigms often rely on strong flickering visual stimulation, which can lead to substantial visual fatigue. Moreover, the electroencephalography responses evoked by brightness flicker are spatially constrained and are primarily associated with occipital visual processing. This study presents a novel text sequence stimulation paradigm that combines periodic visual stimulation with orthographic information and elicits distinct occipital and occipitotemporal scalp response patterns relative to conventional brightness flicker. Frequency-sweep experiments were conducted to investigate the temporal, spatial, and spectral characteristics of the evoked responses. A comparison experiment further showed that text sequence stimulation is less sensitive to variations in stimulus size and luminance than conventional brightness flicker. Based on these findings, a 40-target speller was developed and validated through online experiments. The proposed paradigm achieved an information transfer rate of 235.12 ± 30.12 bits/min while significantly improving user comfort, as confirmed by questionnaire evaluations. These results suggest that text sequence stimulation offers a practical design direction for high-speed and more comfortable visual brain-computer interface.},
}

@article {pmid42306266,
year = {2026},
author = {Tagliafierro, M and Hassanabad, AF and Nickles, J and Sevensky, R and Geirsson, A and George, I and Takayama, H and Argenziano, M and Pirelli, L},
title = {A decade of cardiac surgery after transcatheter aortic valve replacement: Short-term clinical outcomes at a high-volume center.},
journal = {JTCVS structural and endovascular},
volume = {10},
number = {},
pages = {100128},
pmid = {42306266},
issn = {2950-6050},
abstract = {BACKGROUND: The growing adoption of transcatheter aortic valve replacement (TAVR) in younger and lower-risk patients has increased the number of patients with other cardiac diseases requiring surgical intervention. Such surgeries pose unique technical challenges due to the presence of the TAVR, often requiring explantation. Despite increasing clinical relevance, outcomes of these operations remain poorly characterized. We sought to assess the incidence, indications, and short-term results of cardiac surgery following TAVR at a high-volume institution.

METHODS: This was a retrospective single-center analysis of patients undergoing any cardiac surgery post-TAVR between 2015 and 2024. Primary endpoints were perioperative all-cause mortality and stroke; secondary endpoints included cardiopulmonary bypass and cross-clamp times, as well as in-hospital and 30-day outcomes.

RESULTS: Among 10,898 surgeries, 61 (0.5%) involved patients with prior TAVR (median age, 72 years; 59% male), including 85% with hypertension, 28% with diabetes, 43% with chronic lung disease, and 15% with cerebrovascular disease. The median time between TAVR and surgery was 20 months, and 57% of the surgeries were urgent or emergent/salvage procedures. Major indications included TAVR dysfunction (28%), infective endocarditis (26%), and aortic pathology (13%). Common procedures were TAVR explant and surgical aortic valve replacement (n = 49), mitral surgery (n = 19), aortic root/arch surgery (n = 12), and multivessel coronary artery bypass grafting (n = 5). The median aortic cross-clamp and cardiopulmonary bypass times were 121 minutes and 160 minutes, respectively. The mortality rate was 13%. Other outcomes included stroke (3%), prolonged ventilation (31%), tracheostomy (7%), de novo dialysis (8%), need for postoperative blood products (61%), cardiac reintervention (10%), discharge to rehabilitation facility (34%), and readmission (13%).

CONCLUSIONS: Cardiac surgery post-TAVR is uncommon and associated with significant morbidity and mortality. Prosthesis dysfunction and endocarditis are the leading indications, and TAVR explant remains a common although highly morbid intervention.},
}

@article {pmid42308156,
year = {2026},
author = {Hui, W and Wang, Y and Wei, M and Yu, S and Zhou, Y and Ka-Meng, L and Yang, N and Zhu, Z},
title = {Cells on IC Chip.},
journal = {ACS sensors},
volume = {},
number = {},
pages = {},
doi = {10.1021/acssensors.6c00959},
pmid = {42308156},
issn = {2379-3694},
abstract = {Cellular signals are essential for sensing the microenvironment and coordinating physiological functions. Their multimodal nature, encompassing electrophysiological, chemical, mechanical, and optical components, helps define cellular functional states and fate. High-resolution spatiotemporal analysis of these weak, dynamic, and heterogeneous signals is critical for elucidating fundamental life processes, uncovering disease mechanisms, and advancing precision medicine. Recent advances in integrated circuit (IC) technology, particularly the co-integration of complementary metal-oxide-semiconductor (CMOS) and micro-electro-mechanical systems (MEMS), have enabled unprecedented capabilities for cellular signal analysis, driving a transition from conventional instruments and microfluidic platforms to chip-level cellular analysis. This review summarizes the key technological foundations, multimodal sensing mechanisms, and emerging applications of IC technology for cellular signal analysis. It explores the core principles of high-sensitivity, long-term stable signal acquisition, focusing on bioelectronic interfaces, biocompatible packaging, and low-noise signal processing. It also reviews breakthroughs in microelectrode arrays, field-effect transistors, CMOS image sensors, MEMS sensors, and multi-parameter chemical sensing chips for single-cell and population-level detection. The review highlights applications in drug screening, clinical diagnostics, single-cell analysis, and brain-computer interfaces. Finally, it addresses challenges such as biocompatibility, crosstalk suppression, and energy efficiency, while outlining future directions in material innovation, three-dimensional integration, and brain-inspired computing.},
}

@article {pmid42308306,
year = {2026},
author = {Sun, R and Wang, W and Liu, W and Yang, M and Zhang, D and Zou, Z and Yang, K and Gao, C and She, J and Zhang, Z and Hu, Z and Hu, F and Jiang, W and Shu, K and Xie, M and Tang, Z and Zhang, L and Yu, C and Luo, Z},
title = {Untethered thin-film neurostimulator wrapped around tiny nerve trunks for wireless neuromodulation.},
journal = {Science advances},
volume = {12},
number = {25},
pages = {eaec9247},
doi = {10.1126/sciadv.aec9247},
pmid = {42308306},
issn = {2375-2548},
mesh = {Animals ; Rats ; *Wireless Technology/instrumentation ; *Vagus Nerve Stimulation/instrumentation/methods ; Vagus Nerve ; *Implantable Neurostimulators ; Disease Models, Animal ; Cytokines/metabolism ; },
abstract = {Electrical stimulation of tiny peripheral nerve trunks for near-organ neuromodulation enables precise electroceutical therapy for refractory diseases, but long-term stable neuromodulation of these delicate, fragile nerve trunks remains an engineering challenge. Here, we report the development of NeuroWrap Ultrasonic Stimulator (NWUS), an untethered, self-adhesive thin-film neurostimulator capable of conformally wrapping around tiny nerve trunks. The untethered architecture can help avoid the damage or rupture of tiny nerves due to uncontrollable micromotion of lead wires during bodily movements. The NWUS is an ultrasound-responsive acoustoelectric converter with optimized impedance matching, which enables highly effective wireless electrical stimulation. The electroceutical application of NWUS was further illustrated in chronic vagus nerve stimulation for immunomodulation therapy in a rat model. The wireless neuromodulation therapy of rat experimental autoimmune myocarditis was proven to restore left ventricular function, suppress proinflammatory cytokine expression as well as macrophage infiltration within cardiac tissue, and promote regulatory T cell recruitment along with increased anti-inflammatory cytokine levels.},
}

Animals
Rats
*Wireless Technology/instrumentation
*Vagus Nerve Stimulation/instrumentation/methods
Vagus Nerve
*Implantable Neurostimulators
Disease Models, Animal
Cytokines/metabolism

@article {pmid42309008,
year = {2026},
author = {Xin, Q and Hu, H},
title = {Two circuits, one stress: Dissecting the neural logic of comorbid fear and anhedonia.},
journal = {Neuron},
volume = {114},
number = {12},
pages = {2081-2083},
doi = {10.1016/j.neuron.2026.05.003},
pmid = {42309008},
issn = {1097-4199},
mesh = {*Fear/physiology/psychology ; Animals ; *Anhedonia/physiology ; Humans ; *Stress, Psychological/physiopathology ; *Prefrontal Cortex/physiology ; Neural Pathways/physiology ; Interneurons/physiology ; },
abstract = {In this issue of Neuron, Li and colleagues[1] unveil a circuit-based framework in which parallel insula-prefrontal circuits independently govern stress-induced social fear and novelty preference deficits, and lateral inhibition between these circuits via local parvalbumin interneurons drives their comorbidity.},
}

*Fear/physiology/psychology
Animals
*Anhedonia/physiology
Humans
*Stress, Psychological/physiopathology
*Prefrontal Cortex/physiology
Neural Pathways/physiology
Interneurons/physiology

@article {pmid42309130,
year = {2026},
author = {Patel, RJ and Bryson, B and Carlson, T and Demosthenous, A and Jiang, D},
title = {Stability and neurophysiological validity of graph connectivity features for non-stationary motor imagery BCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7ead},
pmid = {42309130},
issn = {1741-2552},
abstract = {Motor imagery (MI) Electroencephalography (EEG) Brain-computer interfaces (BCI) degrade under longitudinal non-stationarity, especially in amyotrophic lateral sclerosis (ALS). Functional connectivity (FC) has been proposed as an alternative feature space, but it remains unclear which FC estimators yield stable, class-informative features across sessions. Approach: Using a multi-session ALS EEG dataset, we computed a broad family of FC estimators per trial to form weighted graphs. We extracted edge weights and node strength features, and quantified (i) feature reproducibility and (ii) LH-RH separability using coefficient of variation and symmetric Kullback-Leibler divergence, respectively. We assessed neurophysiological plausibility via spatial topographies, distance-dependence controls, and evaluated selected feature sets in a strictly temporal cross-session decoding protocol against Common Spatial Patterns, Band Power and Riemannian Methods. Main Results: Coherence-based estimators, particularly magnitude-squared coherence, most consistently produced features exhibiting favourable reproducibility-separability trade-offs across subjects. Node-strength discriminability maps showed lateralised sensorimotor structure consistent with known MI physiology. In temporal generalisation, Magnitude Squared Coherence derived features achieved more consistent test performance than baseline methods for most subjects. Significance: Joint reproducibility-separability profiling provides a principled way to select FC feature spaces for longitudinal MI-BCIs and suggests coherence-based connectivity is a stronger sensor-space candidate under drift.},
}

@article {pmid42309345,
year = {2026},
author = {Tozzi, A},
title = {Embedding EEG trajectories in a Möbius-like manifold: An exploratory study.},
journal = {Neuroscience letters},
volume = {},
number = {},
pages = {138665},
doi = {10.1016/j.neulet.2026.138665},
pmid = {42309345},
issn = {1872-7972},
abstract = {Time-frequency decompositions and nonlinear dynamical methods analyze electroencephalographic (EEG) signals as time series evolving in Euclidean state spaces. We explore an alternative representation of EEG dynamics in which neural activity evolves within a Möbius-like state space. While conventional amplitude-phase descriptions represent oscillatory activity within a cylindrical state space, we additionally consider that a shift of half an oscillatory cycle reverses the sign of the waveform, transforming positive amplitudes into negative ones and vice versa. This symmetry introduces a twist into the cylindrical representation, yielding a non-orientable topology analogous to a Möbius strip in which EEG activity evolves as a continuous cyclic trajectory. Using normalized signal amplitude and instantaneous phase derived from the Hilbert transform, we reconstructed three-dimensional trajectories from EEG recordings of a healthy young adult. Our Möbius-like approach describes the geometry of the embedded EEG trajectory in terms of cyclic evolution, phase-dependent symmetry, winding number and torsion. The winding number quantifies cumulative oscillatory phase progression by measuring the number of rotations performed by the trajectory around the manifold, whereas torsion captures local changes in amplitude-phase organization by characterizing how strongly the trajectory twists in three-dimensional space. Together, these descriptors provide complementary assessment of global and local neural dynamics that are not represented by conventional EEG measures based solely on temporal, spectral or statistical properties. Potential applications include characterization of physiological and pathological brain activity, trajectory-based EEG feature extraction, integration with brain-computer interface approaches and comparative analysis of neural dynamics across cognitive and behavioral conditions.},
}

@article {pmid42309710,
year = {2026},
author = {Sun, H and Wang, R and Karako, K and Song, P and He, J},
title = {Clinical translation and accessibility of brain-computer interfaces: From technology development to clinical application.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2026.01118},
pmid = {42309710},
issn = {1881-7823},
abstract = {Brain-computer interface (BCI) technology establishes a direct communication pathway between neural activity and external devices. Driven by advances in neuroscience, artificial intelligence (AI), neural signal acquisition, decoding algorithms, and implantable system design, BCIs have progressed rapidly from experimental prototypes toward clinically relevant neurotechnologies. However, the translation of these technical advances into routine clinical practice and equitable real-world access remains substantially slower than technological innovation. This review summarizes the major technological pathways of BCIs and their clinical applications, and it then examines BCI development from the perspective of clinical translation and accessibility. We focus on key barriers across the translational chain, including long-term technical stability, quality of clinical evidence, evaluation standards, reimbursement mechanisms, health-economic evidence, and the feasibility of implementation in real-world healthcare settings. We argue that the central challenge in BCI development has shifted from improving technical performance alone to building the translational infrastructure required for safe, effective, affordable, and sustainable clinical integration.},
}

@article {pmid42309711,
year = {2026},
author = {Asakawa, T},
title = {Brain-computer interfaces: A lifeline for paralysis or a Pandora's box for humanity?.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2026.01142},
pmid = {42309711},
issn = {1881-7823},
abstract = {Recent advances in computerized technologies, neuroscience, and materials and engineering have transformed brain‑computer interfaces (BCIs) from conventional unidirectional signal recording systems (brain-to-device) to bidirectional closed-loop neuromodulation systems (brain-device-brain). BCI-based devices enable direct information exchange between the human central nervous system and external electronic devices, and they are widely used in scenarios such as rehabilitation of patients with dyskinesia or enhancement of the self-care ability of disabled individuals. This editorial discusses the rapidly evolving field of BCIs, highlighting both their transformative potential to restore neurological function and the emerging ethical concerns associated with neural data access, cognitive enhancement, and human autonomy. The academic consensus and future translational prospects are also discussed. This article attempts to provide insightful, balanced, and critical viewpoints to help BCI-related research. Indeed, the future of BCIs will depend not only on technological innovation but also on society's ability to establish robust ethical and regulatory frameworks. Whether BCIs become a lifeline for millions of patients or a source of new societal risks will be determined by the choices made today.},
}

@article {pmid42296267,
year = {2026},
author = {Banta, AR and Fedorov, D and Altilia, A and Malliaras, GG and Barone, DG},
title = {Brain-computer interfaces and neuroprosthetics in the next era of neurosurgery.},
journal = {British journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/02688697.2026.2682817},
pmid = {42296267},
issn = {1360-046X},
abstract = {PURPOSE: Brain-computer interfaces (BCIs) and neuroprosthetic systems are rapidly advancing from experimental concepts to clinically meaningful technologies capable of restoring communication, movement, sensation, and therapeutic neuromodulation. This review examines the current state of BCI and neuroprosthetic technologies, their neurosurgical applications, emerging frontiers, and the evolving role of neurosurgeons in their clinical translation.

MATERIALS AND METHODS: A narrative review of the contemporary literature was performed, focusing on neural signal acquisition technologies, including intracortical microelectrode arrays, electrocorticography, depth electrodes, and endovascular recording systems. The review also evaluates advances in neural decoding algorithms, closed-loop stimulation paradigms, neuroprosthetic applications, long-term implant stability, cognitive and affective BCIs, and ethical and regulatory considerations relevant to neurosurgical practice.

RESULTS: Recent developments in neural interface design, implantable electronics, adaptive decoding algorithms, and closed-loop neuromodulation have enabled substantial progress in motor restoration, sensory feedback, speech decoding, and therapeutic neuromodulation. Intracortical and minimally invasive recording systems have expanded the range of achievable clinical applications, while adaptive deep brain stimulation and responsive neurostimulation demonstrate the growing importance of closed-loop approaches. Key challenges remain, including foreign body reactions, long-term signal instability, neural signal drift, and ethical concerns related to cognitive applications, privacy, and data security.

CONCLUSION: BCIs and neuroprosthetics are transforming the neurosurgical landscape by providing new opportunities to restore lost neurological function and deliver personalized neuromodulation therapies. Continued advances in biological integration, system adaptivity, and cognitive applications are expected to accelerate clinical adoption. As these technologies mature, neurosurgeons will play a central role in implantation, long-term management, and the responsible clinical translation of neural interface technologies.},
}

@article {pmid42297203,
year = {2026},
author = {Jin, Z and Liu, C and Liu, G and Feng, G and Li, J and Wu, Y and Jia, H and Keefe, DL and Liu, L},
title = {An Asynchronous Production Line of Meiotic Prophase I in the Mouse Fetal Ovary.},
journal = {Experimental cell research},
volume = {},
number = {},
pages = {115099},
doi = {10.1016/j.yexcr.2026.115099},
pmid = {42297203},
issn = {1090-2422},
abstract = {The initiation of meiosis in the female germline of mammals is a gradual process, but there is currently no clear quantitative framework for determining the precise timing of its onset. Here, we attempt to standardize the description of meiotic entry timing through a systematic, quantitative analysis of meiotic entry and progression in the mouse fetal ovary. Using dynamic expression profiling of key regulators Stra8, Sycp1, and Sycp3 alongside proliferation markers, we demonstrate that germ cells enter meiosis asynchronously and continuously between embryonic days E12.5 and E16.5. During this extended period, mitotic proliferation persists, indicating that germ cells are progressively recruited into the meiotic pathway rather than halting division simultaneously. Homologous chromosome synapsis, marked by Sycp1/Sycp3 co-localization, initiates at E14.5 and is completed prenatally by E18.5. Using stage-composition data, we constructed a continuous-time Markov chain model to infer a population-level meiotic stage clock. This model estimates approximately conserved population-level effective intervals from the modeled early-prophase L compartment to pachytene-stage synapsis (∼72 h) and to the late-prophase/dictyate-associated D-state transition (∼91 h) across modeled cohort-start times. Our findings refine the conventional view by quantitatively defining the extended window of meiotic entry and subsequent progression through prophase I.},
}

@article {pmid42297978,
year = {2026},
author = {Card, NS and Singer-Clark, T and Peracha, H and Iacobacci, C and Hou, X and Wairagkar, M and Fogg, Z and Offenberg, EC and Hochberg, LR and Stavisky, SD and Brandman, DM},
title = {Long-term independent use of an intracortical brain-computer interface for speech and cursor control.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {42297978},
issn = {1546-170X},
support = {N/A//Burroughs Wellcome Fund (BWF)/ ; N/A//Burroughs Wellcome Fund (BWF)/ ; N/A//Achievement Rewards for College Scientists Foundation (ARCS Foundation)/ ; A2295-R//VHA Office of Research and Development | Rehabilitation Research and Development Service (Rehabilitation Research & Development Service)/ ; 1DP2DC021055//U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD)/ ; AL220043//United States Department of Defense | Office of the Secretary of Defense (OSD)/ ; 23-SGP-652//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; },
abstract = {Brain-computer interfaces (BCIs) can provide naturalistic communication and digital access to people with severe paralysis by decoding neural activity associated with attempted speech and movement. Recent work has demonstrated highly accurate intracortical BCIs for speech and cursor control, but two critical capabilities needed for practical viability were unmet: independent at-home operation without researcher assistance and reliable long-term performance supporting accurate speech and cursor decoding. Here we demonstrate the independent and near-daily use of a multimodal BCI with novel brain-to-text speech and computer cursor decoders by a man with paralysis and severe dysarthria due to amyotrophic lateral sclerosis. Over nearly 2 years, the participant used the BCI for more than 3,800 h at home with no researchers present to maintain rich interpersonal communication with his family and friends, independently control his personal computer and sustain full-time employment-despite being paralyzed. He communicated 183,060 sentences-totaling 1,960,163 words-at an average rate of 56 words per minute. He labeled 92% of sentences as being decoded at least mostly correctly. In formal quantifications of performance where he was asked to say words presented on a screen, attempted speech was consistently decoded with more than 99% word accuracy (125,000 word vocabulary). The participant also used the speech BCI as keyboard input and the cursor BCI as mouse input to control his personal computer, enabling him to send text messages and emails and to browse the internet. These results demonstrate that intracortical BCIs have the potential to support independent use in the home, marking a critical step toward practical assistive technology for people with severe motor impairment.},
}

@article {pmid42300820,
year = {2026},
author = {Nazif, TM and Simonato, M and Makkar, RR and Thourani, VH and Desai, ND and Babaliaros, V and Greason, K and Rovin, J and Waxman, S and Davidson, C and Kereiakes, DJ and Gupta, A and Satler, L and Schwartz, R and Kapadia, S and Wong, SC and Smalling, RW and Ghani, M and Teirstein, P and George, I and Potluri, S and Szerlip, M and Xu, K and Cohen, DJ and Sharma, RP and Pibarot, P and Hahn, RT and Mack, MJ and Leon, MB and , },
title = {10-Year Outcomes of SAPIEN 3 Transcatheter Aortic Valve Replacement or Surgery in Intermediate-Risk Patients.},
journal = {Journal of the American College of Cardiology},
volume = {87},
number = {23},
pages = {3296-3308},
doi = {10.1016/j.jacc.2026.03.170},
pmid = {42300820},
issn = {1558-3597},
mesh = {Humans ; *Transcatheter Aortic Valve Replacement/methods/mortality ; Female ; *Aortic Valve Stenosis/surgery/mortality/diagnosis ; Male ; Treatment Outcome ; Registries ; Prospective Studies ; Aged, 80 and over ; Follow-Up Studies ; *Heart Valve Prosthesis ; Risk Assessment ; Aged ; Time Factors ; Risk Factors ; Postoperative Complications ; *Aortic Valve/surgery/diagnostic imaging ; },
abstract = {BACKGROUND: Transcatheter aortic valve replacement (TAVR) is an alternative to surgical aortic valve replacement for patients with symptomatic severe aortic stenosis. However, long-term outcomes data are lacking for TAVR, particularly with newer-generation transcatheter heart valves.

OBJECTIVES: The purpose of this study was to compare 10-year outcomes of intermediate-risk patients who underwent TAVR with the third-generation, balloon-expandable SAPIEN 3 valve in the PARTNER 2 SAPIEN 3 Intermediate-risk Registry (P2S3i) with those who underwent surgery in the PARTNER 2A (P2A) randomized trial.

METHODS: Intermediate-risk patients were enrolled in the P2A trial from 2011 through 2013 and in the P2S3i registry in 2014. These prospective, multicenter studies used the same eligibility criteria and stratified patients based on suitability for transfemoral or transthoracic (transapical/transaortic) access. Ten-year outcomes were evaluated, including all-cause mortality, aortic valve reintervention, and core laboratory-adjudicated echocardiographic outcomes. Patient reconsent was required at 5 years for extended 10-year follow-up, and vital status sweeps were implemented to improve data completeness for all-cause mortality. To account for potential baseline differences and reduce confounding, P2S3i TAVR patients were propensity score-matched 1:1 to P2A surgical patients.

RESULTS: Among 2,005 patients who received a valve, 1,069 underwent TAVR in P2S3i and 936 underwent surgery in P2A. After propensity score matching (N = 783 patients in each group), baseline characteristics were similar between groups: mean age was approximately 82 years, 43% were female, and mean Society of Thoracic Surgeons score was 5.5%. At 10 years, all-cause mortality rate was 83.4% after TAVR and 82.3% after surgery, respectively (HR: 1.01 [95% CI: 0.91-1.13]; P = 0.82). Aortic valve reintervention rates adjusted for competing mortality were 2.0% for TAVR and 1.9% for surgery (P = 0.47). Among 32 TAVR and 30 surgical patients with available echocardiographic data at 10 years, mean gradients were 11.0 mm Hg and 12.6 mm Hg, respectively.

CONCLUSIONS: At 10 years, TAVR with the SAPIEN 3 valve and surgery resulted in similar rates of mortality and aortic valve reintervention, and similar hemodynamics in intermediate-risk patients with symptomatic severe aortic stenosis. This analysis highlights challenges associated with extended long-term follow-up of clinical trials, including differential loss to follow-up and the competing risk of mortality in elderly populations. (PARTNER 2A Trial; NCT01314313; PARTNER 2 SAPIEN 3 Intermediate-Risk Registry; NCT03222128).},
}

Humans
*Transcatheter Aortic Valve Replacement/methods/mortality
Female
*Aortic Valve Stenosis/surgery/mortality/diagnosis
Male
Treatment Outcome
Registries
Prospective Studies
Aged, 80 and over
Follow-Up Studies
*Heart Valve Prosthesis
Risk Assessment
Aged
Time Factors
Risk Factors
Postoperative Complications
*Aortic Valve/surgery/diagnostic imaging

@article {pmid42300996,
year = {2026},
author = {Tao, Y and Zhang, F and Zhu, D and Zhang, S and Ma, L},
title = {Engineered Injectable Coaxial Supramolecular Hydrogel for a Minimally Invasive Neural Electrode.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.6c00499},
pmid = {42300996},
issn = {2576-6422},
abstract = {Implantable neural electrodes with long-term stability remain a central challenge for brain-computer interfaces due to the severe mechanical mismatch between rigid electrodes and soft neural tissue, which triggers chronic inflammation and signal degradation. Herein, we report an injectable coaxial supramolecular hydrogel electrode based on dynamic host-guest interactions between β-cyclodextrin and adamantane, combined with silver nanowire incorporation for enhanced electrical conductivity. The resulting hydrogel exhibits shear-thinning and rapid self-recovery behavior, enabling minimally invasive injection and in situ formation of soft, cylindrical neural electrodes without auxiliary insertion devices. By tuning the supramolecular crosslinker density, the hydrogel achieves tissue-matched mechanical properties comparable to those of brain tissue, effectively mitigating a mechanical mismatch at the electrode-tissue interface. The incorporation of silver nanowires establishes a percolated conductive network, leading to low impedance and stable electrochemical performance. In vivo implantation demonstrates stable impedance and reliable neural signal recording over 14 days. Furthermore, the hydrogel electrodes successfully capture stimulus-evoked neural responses and pathological epileptic activity in a rat model. This work provides a versatile strategy for constructing injectable, mechanically compliant, and electrically robust neural electrodes, offering opportunities for next-generation soft neural interfaces.},
}

@article {pmid42301062,
year = {2026},
author = {Pappas, S},
title = {Xiao Yang: Improving brain-machine interfaces to make them more resilient.},
journal = {Scientific American},
volume = {335},
number = {1},
pages = {58},
doi = {10.1038/scientificamerican072026-2AGY9Pv9CG9vZKawSKhyyi},
pmid = {42301062},
issn = {0036-8733},
}

@article {pmid42301478,
year = {2026},
author = {Gu, B and Zhao, H and Long, F and Li, Q and Zhao, Y and Yu, M and Hu, X and Li, G and Han, F and Tian, Q and Liu, Z and Yu, H},
title = {Advances in printable flexible and stretchable thin-film electrodes: materials, interfaces, technologies and bioelectronic applications.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d5nr05522a},
pmid = {42301478},
issn = {2040-3372},
abstract = {The mechanical mismatch between rigid clinical electrodes and soft biological tissues remains a primary bottleneck restricting the stability of long-term electrophysiological monitoring. Printable flexible thin-film electrodes offer a compelling solution by enabling additive, high-throughput patterning on flexible and stretchable substrates, thereby circumventing the reliance on vacuum environments and high-temperature processing typical of conventional microfabrication. This review synthesizes recent advances in functional inks, ranging from metals and carbon nanomaterials to conductive polymers and ionogels, together with high-resolution printing techniques. Addressing the critical challenge of interfacial failure in flexible devices, we explore engineering strategies to enhance adhesion at both electrode-substrate and electrode-tissue interfaces. Specifically, we analyze the pivotal roles of physical interlocking and chemical anchoring mechanisms in suppressing dynamic delamination and maintaining device integrity. Finally, the review highlights representative applications in wearable electronics, implantable systems, and emerging organoid interfaces, and outlines key translational challenges, including long-term stability and manufacturing reproducibility.},
}

@article {pmid42301851,
year = {2026},
author = {Li, M and Jiang, J and Dong, B and Zhao, R and Wang, B and Wang, K and Yu, H and Wang, C and Xu, M and Ming, D},
title = {Training and transfer effect of evoked brain responses by brain-computer interaction.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3704419},
pmid = {42301851},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalography (EEG)-based neuro feedback training (NFT) guides users to regulate their neural activity via sensory feedback and further modulates their cognitive state or function. It is a crucial approach to improving brain-computer interface (BCI) performance. However, previous studies rarely report cross-task transfer effects of existing NFT approaches beyond the training task, suggesting that they may fail to improve task-related common functions.

METHODS: Here, we propose a steady-state visual evoked potential (SSVEP)-based table hockey BCI game as an NFT approach to improve SSVEP-BCI performance. 40 healthy subjects were randomized into four groups: the 10-frequency NFT group, the 5-frequency NFT and transfer group, the placebo group, and the blank control group. All completed a 10-frequency online SSVEP task before and after five days of training, with EEG and subjective experiences recorded throughout.

RESULTS: The two NFT groups achieved significant improvement in online SSVEP classification accuracy. This was accompanied by increased SSVEP power, inter-trial phase coherence (ITPC), and expansion of activated cortical areas. Notably, enhancements generalized to untrained transfer tasks, likely due to the common impulse response between the training and the transfer tasks.

CONCLUSION: These findings demonstrate that the proposed NFT approach not only improves SSVEP-BCI performance on the trained conditions but also induces transferable neural changes to adjacent frequencies, suggesting training augments the neural population engaged in processing steady-state visual stimuli.

SIGNIFICANCE: This work advances understanding of how self-regulation during NFT improves task-related common functions.},
}

@article {pmid42301910,
year = {2026},
author = {Gu, W and Ju, J and Wang, L},
title = {Structural Designs and Implantation Strategies of Penetrating Flexible Neural Electrodes: From Planar Shanks to Volumetric Neural Interfaces.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e74206},
doi = {10.1002/smll.74206},
pmid = {42301910},
issn = {1613-6829},
support = {LMS25F040004//Natural Science Foundation of Zhejiang Province/ ; 2025A-417-G,2024A-144-G//Yongjiang Talent Program of Ningbo/ ; },
abstract = {This article reviews recent advances in the structural design and implantation strategies of penetrating flexible neural electrodes for intracortical recording. Traditional rigid electrodes suffer from limited long-term stability due to mechanical mismatch with brain tissue. While flexible electrodes improve biocompatibility, they face significant implantation challenges. The article systematically analyzes penetrating-capable structures, including tapered shank electrodes, thread-like electrodes, mesh and bioinspired electrodes, and hollow electrodes, and discusses corresponding implantation strategies such as biodegradable stiffening coatings, shuttle-assisted delivery, syringe injection, as well as stylet or fluid pressure based insertion. It further introduces three-dimensional, high-density electrode arrays enabled by rolling- and assembly-based fabrication techniques, which combine tissue compliance with high electrode density and functionality. Finally, we highlight persistent challenges and future opportunities in scalable fabrication, device reliability, and implantation mechanics, emphasizing the need for integrated approaches to enable next-generation flexible neural interfaces.},
}

@article {pmid42302830,
year = {2026},
author = {Fenoglio, E and Garro, F and Bucchieri, A and Semprini, M},
title = {Motor imagery supported by augmented reality activates motor brain areas similarly to physical movements.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7e27},
pmid = {42302830},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor imagery (MI) is a well-established cognitive process that may enhance motor skills and recovery during motor rehabilitation. Integrating action observation and augmented reality into MI tasks is a promising approach to enhance brain modulation and improve motor learning, yet it remains underexplored. This study examines the impact of augmented reality on brain modulation during tasks combining MI and action observation, and its implications for motor learning assessed through an implicit sequence learning paradigm.

APPROACH: 35 participants were separated into two groups: a motor execution (ME) group performing a physical reaching task, and a motor imagery (MI) group performing the same task within an augmented-reality-based kinesthetic MI paradigm. Event-related synchronization/desynchronization was analyzed in source-localized electroencephalography (EEG) data between and within groups.

MAIN RESULTS: We found similar brain modulation patterns between ME and MI groups specifically when MI was supported by augmented reality, particularly in alpha and beta bands during movement planning and execution phases. Moreover, we observed high inter-individual variability: a subgroup of MI participants did not produce the expected neural response, showing reduced modulation in motor-related regions compared to those who responded as expected. Furthermore, reaction times were compared during physical movements, through implicit repeated and random sequences of reaching movements. We observed non-significant trends towards faster responses in implicit sequences, which may suggest potential implicit motor learning.

SIGNIFICANCE: These exploratory results offer useful insights into augmented reality-supported MI, particularly for users who may require personalized approaches to benefit from MI paradigms, as in brain-computer interface applications.},
}

@article {pmid41849117,
year = {2026},
author = {Luo, Z and She, Q and Jin, T and Li, Y and Xi, X},
title = {Adaptive graph convolution domain adaptation network for cross-subject EEG emotion recognition.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41849117},
issn = {1741-0444},
support = {No. 62371172//National Natural Science Foundation of China/ ; No. 62371178//National Natural Science Foundation of China/ ; },
abstract = {Electroencephalogram (EEG)-based emotion recognition is essential for the advancement of affective brain-computer interface (aBCI) system. However, in cross-subject scenarios, the dynamic nature and subject-specific characteristics of EEG signals significantly hinder knowledge transfer, thereby leading to reduced model performance on previously unseen target domain. To overcome these limitations, we design an innovative domain adaptation model, the adaptive graph convolution domain adaptation network (AGCDAN), to capture the dynamic spatial information of EEG signals and reduce inter-domain distribution discrepancies by aligning both marginal and conditional distributions through domain adaptation. Specifically, adaptive graphs are firstly constructed based on differential entropy features extracted from EEG signals to extract dynamic frequency-spatial representations. A multi-branch neural network is then employed to extract customized feature representations tailored to each source domain and the target domain individually. Subsequently, discriminator-free adversarial learning is employed to align marginal distributions, and introduces subdomain metric learning guided by label information to achieve conditional distribution alignment. Finally, domain-specific classifiers, combined with a decision fusion strategy, produce the final emotion predictions. We evaluate AGCDAN on three datasets (SEED, SEED-IV, DEAP) under a multi-source domain adaptation setting for cross-subject emotion recognition. Achieving recognition accuracies of 89.68%, 68.61%, and 68.13%, respectively, demonstrating superior performance over current state-of-the-art (SOTA) domain adaptations techniques in cross-subject emotion recognition, showcasing its strong capability in modeling dynamic emotional states and reducing negative transfer effects.},
}

@article {pmid42285527,
year = {2026},
author = {Chen, P and Ma, M and Liu, X and Ju, Y and Zhang, Y and Liao, M and Liu, S and Ming, D},
title = {Neural abnormalities in cognitive subprocesses of emotional conflict control in bipolar II disorder: Evidence from ERPs and brain functional networks.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {122124},
doi = {10.1016/j.jad.2026.122124},
pmid = {42285527},
issn = {1573-2517},
abstract = {Patients with bipolar disorder (BD) exhibit deficits in emotional conflict control. These abnormalities may be related to alterations in distinct cognitive subprocesses involved in emotional conflict processing; however, the specific stages affected remain unclear. Given the temporal and stage-dependent nature of emotional conflict control, examining specific processing stages may clarify the mechanisms underlying these deficits in BD. Therefore, this study combined a face-word emotional Stroop task with EEG, integrating event-related potentials (ERPs) and brain functional network analyses to characterize the cognitive subprocesses involved in emotional conflict control in bipolar II disorder (BD-II). BD-II patients showed significant abnormalities in early cognitive stages, including emotional stimulus perception and conflict monitoring (p < 0.05). These abnormalities were mainly reflected by reduced N200 amplitudes, right temporal region (T8)-centered network changes, and alterations in both global topology and frontal network organization. Machine learning analysis further suggested that these abnormal electrophysiological features may contain information relevant to distinguishing BD-II patients from healthy controls (HC), yielding an accuracy of 83.3% on the held-out test set. In summary, this study suggests that emotional conflict control deficits in BD-II are mainly reflected in early-stage electrophysiological abnormalities, with ERP amplitude changes and T8-centered right temporal network alterations representing the core findings. These findings provide candidate EEG features for further investigation of emotional conflict control abnormalities in BD-II, but require validation in larger independent samples.},
}

@article {pmid42285811,
year = {2026},
author = {Cleary, HL and Bernard, MD and Davenport, DL and Bernard, AC},
title = {Diagnostic value of serum troponin in stable patients at risk for blunt cardiac injury.},
journal = {Injury},
volume = {},
number = {},
pages = {113448},
doi = {10.1016/j.injury.2026.113448},
pmid = {42285811},
issn = {1879-0267},
abstract = {INTRODUCTION: There are no gold standard criteria for diagnosing blunt cardiac injury (BCI). While the combination of electrocardiogram (ECG) and serum troponin is considered the most sensitive screening method for those being considered for discharge, little evidence exists regarding the diagnostic value and clinical utility of initial and serial serum troponin levels in stable patients who are being admitted with a possible BCI. Therefore, this study seeks to determine if troponin level is an independent predictor of adverse cardiac events in stable, admitted patients at risk for BCI.

METHODS: This was a five-year retrospective study using the trauma database at a University Level I Trauma Center. The study population included adult trauma patients presenting with a physician diagnosis of BCI or a sternal fracture who met prespecified stability criteria (SBP ≥ 90 mmHg, HR < 110bpm, shock index < 1, GCS ≥ 14). The data collected included all troponin values and ECG interpretations as well as echocardiogram interpretations, when performed. A patient was classified as having an adverse cardiac event if they were diagnosed with a new arrhythmia requiring treatment, had cardiac surgery, or suffered cardiac-related mortality. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratios were calculated to analyze the diagnostic performance of the different BCI screening modalities. Diagnostic tests were compared using the Exact Mcnemar's test and the exact binomial test.

RESULTS: 350 patients met inclusion criteria. There were 12 adverse cardiac events; each were new arrhythmias requiring treatment with one patient also requiring synchronized cardioversion. Only one adverse cardiac event occurred in a patient with a normal ECG and troponin (n = 160). No patients with a normal ECG and abnormal troponin had an adverse cardiac event (n = 83). Patients with an abnormal ECG were more likely to have an adverse cardiac event (p < 0.001).

DISCUSSION: In stable patients in this cohort, troponin level did not predict adverse cardiac events. Therefore, admitted patients at risk for BCI who meet stability criteria might be safely observed without measurement of serum troponin.},
}

@article {pmid42286462,
year = {2026},
author = {Dai, W and Jahangir, M and Li, T and Guo, WJ},
title = {Early-life stress and adolescent circadian dysrhythmia drives unique behavioral and microbial profiles in rats.},
journal = {BMC microbiology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12866-026-05287-y},
pmid = {42286462},
issn = {1471-2180},
support = {82171487//National Natural Science Foundation of China/ ; 2024C03006//"Pioneer" and "Leading Goose" R&D Program of Zhejiang/ ; TD2024003//Leading innovation and entrepreneurship team of Hangzhou/ ; },
abstract = {OBJECTIVES: Early life adversity and circadian disruptions are known to impact neurodevelopment and physiology. This study investigated the effects of maternal separation (MS), adolescent circadian dysrhythmia, and their combination (double hit) on anxiety-like behavior and gut microbiota composition in rats.

METHODS: Rats were divided into four groups: CL (control group: normal early-life conditions with a standard light/dark cycle during adolescence), MS + N (maternal separation (MS) with a standard light/dark cycle (N=normal)) during adolescence), N + ALD (normal early-life conditions (N) with an altered light/dark cycle (ALD) during adolescence), and MS + ALD (combined exposure: MS with an altered light/dark cycle (ALD) during adolescence). Anxiety-like behavior and locomotor activity were assessed using the Open Field Test. Gut microbial diversity and taxonomic composition were analysed to identify microbial shifts across groups.

RESULTS: Behavioral analysis indicated that the combined stress group (MSLD) spent significantly (p < 0.05) more time in the center of the arena compared to the CL, MS + N, and N + ALD groups, suggesting a compromise in risk assessment ability due to dual stress exposure. Microbiome profiling revealed that while a core microbiome was conserved, each stressor generated a unique taxonomic signature. The N + ALD group appeared as the most distinct outlier, characterized by the lowest number of unique features and a specific enrichment of the viral species of phylum Uroviricota. Conversely, the MS + ALD group was distinguished by an enrichment of Bacteroidota species, including Muribaculum intestinale and Phocaeicola vulgatus, while the MS + N group showed enrichment in Bacteroides acidifaciens. Mycobiome analysis showed that early-life stress was the primary driver of fungal restructuring, distinguishing maternal separation groups by the loss of Neocallimastix species and the competitive expansion of Piromyces finnis. While adolescent circadian disruption alone largely preserved the baseline mycobiome, the cumulative dual-hit stress (MS + ALD) generated a distinct dysbiotic profile evident by the unique proliferation of Anaeromyces robustus.

CONCLUSIONS: In conclusion, the developmental timing of stress exposure drives distinct dysbiotic shifts. Specifically, adolescent circadian disruption selectively targets the virome, whereas early-life stress causes shift in the microbiome which endures a long-term foundation for adolescent psychiatric vulnerability. Notably, the cumulative effect of early life and adolescence stressors results in a unique microbial and behavioral profile, highlighting that the specific developmental window of exposure is a decisive factor in gut-brain axis dysfunction.},
}

@article {pmid42287979,
year = {2026},
author = {Jin, Z and Li, D and Zhou, Q and Wang, Z},
title = {NEURAL-VOX: NEURal auditory language decoding for voice and text reconstruction.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {204},
number = {},
pages = {109221},
doi = {10.1016/j.neunet.2026.109221},
pmid = {42287979},
issn = {1879-2782},
abstract = {Neural decoding of perceived linguistic content from non-invasive brain recordings remains a profound scientific challenge with transformative implications for assistive technologies. Existing approaches often struggle to generate intermediate representations, such as mel spectrograms or phonemes, and seldom integrate multi-modal information to enhance text decoding. This study presents a framework for decoding non-invasive brain activity into text, phoneme sequences, and mel-spectrogram-based acoustic representations, termed NEURAL-VOX. Leveraging a three-stage training strategy, NEURAL-VOX not only improves the accuracy of brain-to-text decoding, but also enables text generation to benefit from joint optimization with speech synthesis. By incorporating multi-scale frequency-domain analysis, our model more effectively captures the hierarchical structure of language processing in neural activity. Experiments across multiple datasets demonstrate that NEURAL-VOX achieves substantial gains over existing methods. The learned phoneme representations encode rich linguistic information and further strengthen text decoding, while model interpretability analysis reveals strong alignment with neurobiological patterns.},
}

@article {pmid42288545,
year = {2026},
author = {Nechchad, M and Elkari, B and Midaoui, IE and Cadi, SAE and M'Hifed, Z and Ourabah, L and Workneh, AD and Chaibi, Y and Irshad, SM and El-Barbary, ZMS and Yessef, M},
title = {EEG blink and gaze control using random forest classification for accessible assistive robotic navigation in real world conditions.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-56416-6},
pmid = {42288545},
issn = {2045-2322},
abstract = {This study presents an EEG-based Brain-Computer Interface for intuitive robotic navigation driven by ocular activity. A real multi-subject dataset collected from 15 participants was used to extract blink- and gaze related EEG features for five control commands. Eight supervised classifiers were evaluated under stratified 5-fold cross-validation: Random Forest, multilayer perceptron, support vector machine, k-nearest neighbors, RUSBoost, Naive Bayes, decision tree, and linear discriminant analysis. Among them, Random Forest achieved the best overall performance, reaching 98.74 ± 1.19% accuracy, 0.9874 Macro-F1, and 0.9993 macro-AUC, demonstrating excellent robustness and class separability across the five ocular classes. The decoded commands were transmitted wirelessly to an embedded Raspberry Pi platform, where they were converted into safe motor actions for wheelchair type robot navigation. Real world experiments in indoor environments confirmed stable motion control, reliable command execution, and successful obstacle avoidance without physical interaction from the user. These findings support the feasibility of a low-cost, portable, and non-invasive BCI solution based on realistic multi-subject EEG data for assistive mobility applications. Although the full control loop operated online, the end-to-end system latency was not quantitatively benchmarked in the present study and remains an important limitation to be addressed in future work. Future developments will focus on expanding the command set, improving robustness under more variable conditions, and validating the system in broader assistive mobility scenarios.},
}

@article {pmid42292038,
year = {2026},
author = {Wang, NN and Cao, F and Xu, D},
title = {Hydrogels in Neurological Disorders: Emerging Diagnostic and Therapeutic Applications.},
journal = {International journal of nanomedicine},
volume = {21},
number = {},
pages = {618081},
pmid = {42292038},
issn = {1178-2013},
mesh = {*Hydrogels/chemistry/therapeutic use ; Humans ; *Nervous System Diseases/diagnosis/therapy/drug therapy ; Animals ; Drug Delivery Systems/methods ; Brain-Computer Interfaces ; Electroencephalography/methods ; Blood-Brain Barrier ; },
abstract = {The clinical management of neurological disorders remains a major challenge worldwide, constrained by fundamental limitations in both diagnosis and therapy. Electroencephalography (EEG), the cornerstone of neurological assessment, is limited by low spatial resolution and inconsistent signal quality. Therapeutically, the blood-brain barrier (BBB) restricts drug delivery to the brain, resulting in subtherapeutic intracerebral concentrations. These convergent diagnostic and delivery bottlenecks underscore an urgent imperative for innovative materials and technologies. Hydrogels, characterized by biomimetic three-dimensional (3D) architectures, have emerged as a versatile material platform to bridge this gap. From a diagnostic perspective, hydrogels-based electrodes exhibit exceptional biocompatibility and low interfacial impedance, enabling high-fidelity EEG acquisition while minimizing insult to sensitive neural and skin tissues. From a therapeutic perspective, their 3D architecture provides versatile scaffolds for therapeutic agents, supporting high loading efficiency and programmable release profiles for neurological interventions. In this review, we first outline the physicochemical properties and fabrication techniques of hydrogels. We then discuss their applications, with particular emphasis on neural bio-electrodes, brain-computer interfaces (BCIs), drug delivery, and neuro-bioengineering. Finally, we examine the challenges impeding the clinical translation of hydrogels and outline prospective mitigation strategies. The integration of these functionalities is anticipated to advance closed-loop therapeutic systems for the precise management of complex neurological disorders.},
}

*Hydrogels/chemistry/therapeutic use
Humans
*Nervous System Diseases/diagnosis/therapy/drug therapy
Animals
Drug Delivery Systems/methods
Brain-Computer Interfaces
Electroencephalography/methods
Blood-Brain Barrier

@article {pmid42292814,
year = {2026},
author = {Wang, Z and Zhao, W and Chen, X and Zhao, S and Li, X and Yang, Q and Zong, F and Zhang, H},
title = {Activation of GLP-1R ameliorates alcohol withdrawal induced anxiety-like behavior by regulating neuronal mitochondrial quality control.},
journal = {Frontiers in pharmacology},
volume = {17},
number = {},
pages = {1820128},
pmid = {42292814},
issn = {1663-9812},
abstract = {INTRODUCTION: Alcohol use disorder (AUD) is a specific psychological state induced by repeated heavy drinking, and withdrawal symptoms such as anxiety are closely related to relapse after withdrawal. While neuronal damage caused by alcohol is considered a significant precipitating factor for withdrawal-induced anxiety, the underlying molecular mechanisms remain unclear.

METHODS: In this study, we established a mouse model of alcohol withdrawal through 3 months of chronic ethanol exposure (CEE) followed by withdrawal. Mice were treated with semaglutide (0.03 mg/kg) via intraperitoneal injection and subjected to behavioral, biochemical, and morphological analyses.

RESULTS: Our results demonstrate that the glucagon-like peptide-1 receptor (GLP-1R) agonist semaglutide alleviates anxiety-like behaviors in CEE withdrawal mice and reverses the downregulation of GLP-1R and its downstream effector CREB in the mitochondria of prefrontal cortex (PFC) neurons. Enhancing the GLP-1R/CREB pathway regulates mitochondrial quality control, including fission, fusion, and mitophagy, to maintain mitochondrial function and ameliorate synaptic impairment.

DISCUSSION: These findings suggest that activation of GLP-1R ameliorates alcohol withdrawal-induced anxiety-like behaviors by regulating neuronal mitochondrial function, providing a potential therapeutic target for AUD.},
}

@article {pmid42294101,
year = {2026},
author = {Sümer-Arpak, E and Saini, R and Chakladar, DD and Varun, SK and Simistira Liwicki, F},
title = {The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1838064},
pmid = {42294101},
issn = {1662-5161},
abstract = {Inner speech (IS), or imagined speech without overt articulation, is a promising target for brain-computer interfaces (BCIs) aimed at restoring communication in individuals with severe speech impairments, such as locked-in syndrome. Foundation models (FMs), typically trained using self-supervised learning (SSL) on large-scale datasets, offer new opportunities for learning transferable and robust representations from neural signals. This mini review provides an overview of FM-based approaches for IS decoding using non-invasive neuroimaging modalities, including functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and functional near-infrared spectroscopy, highlighting architectural trends, pretraining strategies, and model adaptation techniques. We discuss how recent models move beyond task-specific classification toward scalable representation learning and semantic-level decoding. Despite these advances, several challenges remain, including the weak, noisy, and non-stationary nature of neural signals, variability in data acquisition, and limitations in dataset scale, standardization, computational resources, interpretability, and evaluation metrics. Ethical and privacy considerations are also critical. Overall, FMs provide a promising paradigm for non-invasive IS decoding, addressing neurophysiological, methodological, and ethical challenges is essential for developing scalable and reliable BCI systems.},
}

@article {pmid42294231,
year = {2026},
author = {Uszko, JM and Abibu, SA and Eichhorn, SJ and Patil, AJ and Hall, SR},
title = {Cellulose Biofilms, New Biotemplates in the Synthesis of Cuprate Superconductors.},
journal = {ACS omega},
volume = {11},
number = {22},
pages = {32391-32399},
pmid = {42294231},
issn = {2470-1343},
abstract = {Bacterial cellulose (BC), obtained from fermented food byproducts (Symbiotic Culture of Bacteria and Yeast, and Nata de Coco), was successfully used as a template for the synthesis of a YBa2Cu3O6+δ (YBCO) superconductor. As previous studies have shown, a dry template is needed to ensure the maximum uptake of the precursor solution. BC used is obtained in a wet state; it must be dried before use as a template. A variety of template drying techniques were investigated to assess the efficacy. This included air, oven, freeze, and solvent exchange drying. Among these, freeze-drying proved to be the most effective method as it best preserved the porous internal structure of the template. The addition of ethylenediaminetetraacetic acid (EDTA), a polychelating acid, also had a beneficial effect on the synthesis, improving both phase purity and the contribution of the superconducting phase. Waste-derived BC was shown to be a suitable substrate for the sol-gel synthesis of cuprate superconductors, providing an alternative to the ionic-liquid/nanocellulose-based approach used previously.},
}

@article {pmid42295582,
year = {2026},
author = {Li, J and Li, Y and Shi, W and Kang, R and Wang, H},
title = {A Multiscale Decoding Approach of Subject-Independent Motor Imagery EEG Signal Combined with Data Alignment Strategy.},
journal = {Annals of biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42295582},
issn = {1573-9686},
support = {62373108//the National Nature Science Foundation of China/ ; },
abstract = {PURPOSE: Brain-computer interface (BCI) leverages artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signals, significantly enhancing quality of life. EEG-based motor imagery (MI) brain signals are widely used in various BCI applications, including smart healthcare, intelligent vehicle, smart homes, and robotics control. However, the substantial individual variability in EEG data distribution poses a challenge in applying constructed models to new subjects.

METHODS: To address this issue, this study proposes a novel deep learning framework, namely Multiscale Spatiotemporal Convolutional Neural Network (MSTCNN) based on Euclidean Space Data Alignment (ESDA), which can effectively decode MI tasks in subject-independent scenarios. First, to address the data distribution variability for different subjects, the proposed ESDA method is utilized to align all MI task data, reducing the discrepancies caused by physiological differences. Subsequently, an end-to-end multiscale model is constructed to extract multi-level MI task-related feature information from EEG signals. Additionally, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to highlight critical local features, further improving MI task-decoding performance.

RESULTS: Experiments conducted on the publicly available BCI Competition IV dataset 2a demonstrate that the proposed multiscale model achieves an average accuracy of 67.5% in subject-independent scenarios, with a relatively small number of parameters (4.4 × 10[3]). Moreover, the highest decoding accuracy for individual subjects reaches 81.4%, surpassing many other state-of-the-art (SOTA) methods. The ablation experiments further validate the model's performance and robustness.

CONCLUSION: This study may provide a solid theoretical foundation for advancing BCI system applications in Internet of Things units, clinical, and other areas.},
}

@article {pmid42295948,
year = {2026},
author = {Zhang, Z and Hamada, H and Rangpong, P and Connelly, A and Yagi, T},
title = {Auditory Perception as a Surrogate for Auditory Imagery in EEG-Based BCI Training: Neural Evidence and a Feasibility 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.2026.3703988},
pmid = {42295948},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI) systems are commonly classified as reactive or active. Reactive BCIs rely on responses to external stimuli, simplifying decoding but limiting direct user control. In contrast, active BCIs enable intuitive control through spontaneous mental activity but impose greater signal-processing challenges. Speech BCIs, a subset of active systems, hold promise for individuals with severe neurological impairments such as locked-in syndrome (LIS), potentially enabling communication through imagined speech. However, their adoption is hindered by the demanding pre-training required to collect imagery data, which often induces fatigue, and the difficulty many users face in generating consistent, high-quality imagery. To address these limitations, we propose a hybrid BCI framework that uses passive listening tasks as a surrogate for auditory imagery tasks during model training. As a first step, this proof-of-concept feasibility study examined neural responses to five Japanese vowels (/a/, /i/, /u/, /e/, /o/) under listening and imagery conditions in a limited experimental setting with healthy male participants, using event-related potentials (ERPs), topographical mapping, and event-related spectral perturbations (ERSPs), followed by statistical analysis of shared and distinct neural patterns. In the primary three-class and binary classification analyses, classification accuracies for imagery signals obtained from models trained solely on listening data were comparable to, and in some settings higher than, those trained directly on imagery data, with both outperforming chance level. These findings indicate that passive listening may provide effective training data for active auditory imagery BCIs within the tested setting, potentially reducing cognitive demands without compromising performance. Further validation in larger and more diverse cohorts is required before clinical application.},
}

@article {pmid42295950,
year = {2026},
author = {Fang, C and Xu, G and Chen, R and Li, H and Zhang, X and Xie, J and Tian, P and Yang, Z and Han, C and Zhang, S},
title = {Noise-Driven Feature Enhancement of High-Frequency SSVEP through Underdamped Second-order Stochastic Resonance Energy Transfer.},
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.2026.3703399},
pmid = {42295950},
issn = {1558-0210},
abstract = {Brain-computer interfaces (BCIs) system leveraging high-frequency steady-state visual evoked potentials (SSVEP) enhance user comfort, but their signals suffer from significant amplitude attenuation, which challenges precise detection. To address this, we proposed a novel weak-feature enhancement framework, FBUSSR-CCA, which integrates a monostable (single-well) underdamped second-order stochastic resonance (USSR) model into the filter-bank processing pipeline. This model strategically converts noise energy to nonlinearly amplify weak, high-frequency target components. Validation was performed on two public high-frequency SSVEP datasets (stimulus frequencies of 40-43Hz and 40-41.5Hz, respectively). The proposed FBUSSR-CCA was compared against six benchmark methods, including the best-performing FBCCA. Results indicated that the proposed framework attained superior performance across all three experimental conditions: the mean accuracy and ITR reached 83.61 ± 13.16 % / 17.92 ± 7.81 bits min[-1], 98.89 ± 2.17 % / 28.77 ± 2.30 bits min[-1], and 70.27 ± 19.02 % / 11.88 ± 8.62 bits min[-1], respectively. Further analysis confirmed the USSR module is a general-purpose tool (enhancing all six methods) and is parametrically robust to perturbations. These findings show that the monostable USSR framework is a reliable solution for enhancing high-frequency SSVEP, advancing the potential for more comfortable and efficient BCI systems.},
}

@article {pmid42296064,
year = {2026},
author = {Tai, YD and Villalobos, J and Wickramasinghe, N and Widdicombe, B and Unnithan, RR and Grayden, DB and John, SE},
title = {Toward a fully wireless endovascular neural interface: Evaluating power transfer efficacy.},
journal = {PloS one},
volume = {21},
number = {6},
pages = {e0351138},
doi = {10.1371/journal.pone.0351138},
pmid = {42296064},
issn = {1932-6203},
mesh = {Animals ; *Wireless Technology/instrumentation ; Sheep ; *Endovascular Procedures/instrumentation/methods ; Equipment Design ; Brain ; },
abstract = {BACKGROUND: Endovascular neural interfaces (ENIs) offer a minimally invasive approach for neural stimulation and recording without the need for open brain surgery. However, current generation devices have long transvascular wires from the implant site to the chest. Eliminating these wires will unlock clinical usability, including lowering infection risk from transvascular wires, reducing the risk of thrombosis from altered hemodynamics, and improving mechanical reliability. However, removing these transvascular wires would require efficient power transfer across the skull and tissue while meeting specific absorption rate (SAR) limits, which is a significant challenge in the field.

OBJECTIVE: This work designed and evaluated endovascular receiver (Rx) and transmitter (Tx) coils within endovascular geometric and biological constraints to maximize wireless power transfer.

METHODS: This study evaluated the optimal operating frequencies, quantified coupling, coil quality factors, power transfer efficiency, and SAR using computational modeling, benchtop, and in-vivo testing. The study also assessed the tolerance to coil misalignment and load mismatch. We evaluated each case with and without ferrites with measurements in air, sheep tissue, and in vivo in sheep.

RESULTS: The results showed that inductive power transfer delivered power to endovascular geometry devices at clinically relevant depths. The maximum power transfer efficiency (PTE) reached 11% at 15 mm and 2% at 30 mm, with up to 72 mW delivered at 30 mm under SAR safety limits. The rectangular planar coil pair performed best at ≤15 mm, whereas the ferrite-core flux-pipe Tx with a helical Rx outperformed beyond ~20 mm and was more tolerant to misalignment.

CONCLUSION: This study demonstrated the feasibility of wirelessly powering multichannel ENIs using coils that can be placed inside a blood vessel and powered inductively. Making an endovascular neural interface fully wireless has the potential to transform the technology by improving both safety and reliability.},
}

Animals
*Wireless Technology/instrumentation
Sheep
*Endovascular Procedures/instrumentation/methods
Equipment Design
Brain

@article {pmid42296109,
year = {2026},
author = {Janapati, R and Maram, B and Saidhbi, S and Desai, U and Nayak, P},
title = {SET Net A Hybrid Deep Learning Framework For EEG Based Attention Relaxation Classification In Brain Computer Interface Applications.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {231},
pages = {},
doi = {10.3791/70700},
pmid = {42296109},
issn = {1940-087X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Deep Learning ; *Attention/physiology ; },
abstract = {Brain Computer Interface (BCI) technology is used as a tool to provide a direct communication channel between the human brain and the external environment. This has applications in assistive and interactive systems. The non-invasive acquisition of neural activity using Electroencephalography (EEG) is standard with BCI systems. However, it is difficult to effectively classify cognitive states, including attention and relaxation, because the EEG signal is non-stationary and noisy. To overcome these shortcomings, this study proposes a new hybrid deep learning architecture namely, squeeze-and-excitation (SE) transformer network (SET-Net). In this method, EEG signals are preprocessed and divided into temporal windows to analyze the significant spectrogram representations. The proposed model achieves a classification accuracy of 93.7%, with F1-score of 0.93 and ROC-AUC of 0.98. This demonstrates the effectiveness of the hybrid deep learning model with enhanced discrimination of EEG-based attention-relaxation state. This offers a scalable system for real-time BCI applications as assistive technology in human-computer interaction.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Deep Learning
*Attention/physiology

@article {pmid42296181,
year = {2026},
author = {Wang, L and Chen, H and Gong, X and Shi, Y and Lu, Z and Zhang, L and Chen, X and Chen, D and Zhou, H and Cheng, L},
title = {Research on Pathological Voice Recognition Based on XGBoost.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {231},
pages = {},
doi = {10.3791/68784},
pmid = {42296181},
issn = {1940-087X},
mesh = {Humans ; Boosting Machine Learning Algorithms ; *Voice Disorders/diagnosis ; *Pattern Recognition, Automated/methods ; },
abstract = {With the continuous growth of human social communication, the number of people suffering from voice disorders is also increasing. Due to the objective and non-invasive advantages of acoustic detection methods for pathological voice, the use of speech signal analysis for pathological voice recognition has become a research hotspot. This article first selected 101 continuous vowels /a/ from the German SVD database as the research object. Secondly, using wavelet packet technology for time-frequency analysis, four nonlinear dynamic parameters, namely approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, are extracted from the sub signals as the feature parameter set for the pathological voice classifier. Finally, the machine learning algorithm XGBoost is selected as the pattern recognition method to establish a pathological voice classifier, and the classification performance is verified using five fold cross validation and ROC curve. Experimental results have shown that the accuracy of XGBoost's pathological voice classifier is 0.857, the F1 score is 0.875, and the AUC value is 0.944, all of which are higher than the classifier constructed by SVM, indicating that XGBoost has better performance in pathological voice recognition.},
}

Humans
Boosting Machine Learning Algorithms
*Voice Disorders/diagnosis
*Pattern Recognition, Automated/methods

@article {pmid42035123,
year = {2026},
author = {Xi, L and Liu, Q and Li, H and Li, W and He, D and Yao, L and Yang, X},
title = {Comparative efficacy of motor imagery augmented with central non-invasive brain stimulation versus peripheral electrical stimulation for upper extremity rehabilitation post-stroke: a systematic review and network meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {},
pmid = {42035123},
issn = {1743-0003},
support = {zx2019-04-02//Rehabilitation Clinical Medical Centre of Yunnan Province/ ; 2019IC034//Jiajie Expert Workstation of Yunnan Province/ ; 202203AC100007-6//Study on a New Model of Comprehensive Intervention in Rehabilitation and Psychology of "Brain and Heart together"/ ; 202305AF150032//Science and Technology Talent and Platform Program (Academician and Expert Workstation)/ ; 2022YFC2009700//Research and Development of Integrated Chinese and Western Medicine Rehabilitation Technology and Multi-modal Monitoring System for movement Disorders/ ; 2018YFC2002301//National Key Research and Development Program of China/ ; 2024XKTDTS18//the Neurorehabilitation Team of Kunming Medical University/ ; FWCY-ZNT2024011//Systematic Development and Industrial Applications of Balneotherapy in Stroke Rehabilitation: A Translational Research Framework/ ; 202402AA310058//Application and Innovative Research of Balneotherapy in Chronic Disease Management/ ; 2024J0383//Scientific Research Fund project of Education Department of Yunnan Province/ ; 2023BS01//Doctoral research project of the Second Affiliated Hospital of Kunming Medical University/ ; 2025KFZD006//Investigating the Impact of iTBS on the HPA Axis in Stroke Patients via Stimulation of Different Brain Regions Using Resting-State EEG/ ; 82560452//National Natural Science Foundation of China/ ; 202501AY070001-185//Investigating the Modulation of Negative Emotions by the Deep Cerebellar Nuclei-Hippocampal Neural Circuit Using Transcranial Magnetic Stimulation/ ; },
abstract = {BACKGROUND: Upper limb dysfunction is a common and debilitating consequence of stroke, severely affecting patients’ activities of daily living and quality of life. Motor imagery (MI) has emerged as a promising rehabilitation technique, and its combination with various forms of non-invasive stimulation, both central (e.g., repetitive transcranial magnetic stimulation, rTMS; transcranial direct current stimulation, tDCS) and peripheral (e.g., functional electrical stimulation, FES), has been increasingly investigated. While previous meta-analyses have confirmed the general benefit of combined interventions, the relative efficacy of different MI-based combination strategies remains unclear. This systematic review and network meta-analysis aimed to directly and indirectly compare the effectiveness of MI augmented with different non-invasive central or peripheral stimulation modalities for upper extremity recovery post-stroke.

METHODS: We registered the study on PROSPERO (CRD420251131264) and followed the PRISMA guidelines. Randomized controlled trials (RCTs) were searched in PubMed, Cochrane Library, EMBASE, Scopus, CNKI, and Wanfang databases from inception until August 4, 2025. The included RCTs involved adult stroke patients with upper limb dysfunction receiving MI combined with any non-invasive stimulation. The primary outcome was the change in upper limb motor function measured by the Fugl-Meyer Assessment (FMA or FMA-UE). A frequentist network meta-analysis was performed using random-effects models. Risk of bias was assessed using the Cochrane RoB 2 tool. Subgroup, sensitivity, and meta-regression analyses were conducted to explore heterogeneity.

RESULTS: Seventeen RCTs involving 846 participants were included in the systematic review, with 13 studies forming the network for meta-analysis, comparing 9 intervention strategies. Network meta-analysis for the FMA outcome showed that MI combined with low-frequency rTMS (MI-LF-rTMS) showed a statistically significant difference compared to conventional rehabilitation alone (Standardized Mean Difference, SMD = 1.755, 95% CI 0.631 to 2.879, p  = 0.002). No other intervention, including MI-tDCS, MI-FES, or any single therapy, showed a statistically significant difference compared to conventional rehabilitation. MI-LF-rTMS also showed a statistically significant difference in upper limb functional activity (Action Research Arm Test). Subgroup analyses indicated that the statistically significant difference for MI-LF-rTMS was also observed across intervention durations ≤ 4 weeks, disease stages ≤ 3 months post-stroke, and in protocols not using brain-computer interface technology. Meta-regression identified that the use of a brain-computer interface, publication year, and patient mean age were significant sources of heterogeneity.

CONCLUSION: Among the intervention strategies evaluated in this network meta-analysis, motor imagery combined with low-frequency repetitive transcranial magnetic stimulation (MI-LF-rTMS) showed a statistically significant difference compared to conventional rehabilitation. This regimen integrates central neuromodulation with cognitive training and may be a clinically feasible option, particularly for patients in the early phase after stroke. Future research should focus on parameter optimization, mechanistic exploration, and validation in larger, more diverse populations.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-026-02002-w.},
}

@article {pmid42284173,
year = {2026},
author = {Meunier, A and Zak, MR and Munz, L and Garkot, S and Eder, M and Xu, J and Grosse-Wentrup, M},
title = {A Conversational Brain-Artificial Intelligence 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.2026.3703091},
pmid = {42284173},
issn = {1558-0210},
abstract = {We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on electroencephalography (EEG), termed EEGChat. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to be able to generate language. Our work thus demonstrates the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.},
}

@article {pmid42277056,
year = {2026},
author = {Wang, Y and Lin, Z and Wu, D and Yang, Y and Zhang, J},
title = {Associations of DTI-ALPS index and choroid plexus volume with clinical severity across the Parkinson's disease spectrum.},
journal = {NPJ Parkinson's disease},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41531-026-01432-6},
pmid = {42277056},
issn = {2373-8057},
support = {82571418//National Natural Science Foundation of China/ ; 32530027//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {Impaired clearance of pathogenic proteins may contribute to Parkinson's disease (PD) progression, but the clinical relevance of brain clearance-associated processes in humans remains incompletely understood. Using cross-sectional data from 1,861 participants (704 PD, 997 prodromal, 160 healthy controls) in Parkinson Progression Marker Initiative, we investigated whether the magnetic resonance imaging (MRI)-based indirect markers linked to brain clearance-related processes, diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index and normalized choroid plexus volume (NCPV), are associated with disease stage and motor/non-motor manifestations across the PD spectrum. The ALPS index declined with age, was lower in males, and showed a stepwise reduction from controls to prodromal individuals and PD patients. Clinically, lower ALPS index correlated with greater motor severity in prodromal and PD groups, with stronger associations at advanced stages. Lower ALPS index also correlated with rapid eye movement sleep behavior disorder, cognitive impairment, and depressive symptoms. NCPV showed a complementary trend, demonstrating positive associations with clinical severity measures and a significant negative correlation with the ALPS index. Together, these findings suggest that MRI markers linked to brain clearance processes are associated with clinical progression across the PD continuum and may provide imaging biomarkers relevant to disease staging and pathophysiology.},
}

@article {pmid42277539,
year = {2026},
author = {Xu, R and Huang, X and Chen, Z and Mohamed, HA and He, X and Lu, F and Ou, Y and Li, G and Zhang, K},
title = {Application Mechanisms and Clinical Prospects of Brain-Computer Interface Technology in Radiation-Induced Brain Injury.},
journal = {Cellular and molecular neurobiology},
volume = {},
number = {},
pages = {},
doi = {10.1007/s10571-026-01758-y},
pmid = {42277539},
issn = {1573-6830},
support = {2024A1515011451//Natural Science Foundation of Guangdong Province/ ; 2023-CCA-TCM-033//Li Xin Traditional Chinese Medicine Research and Innovation Fund/ ; BYPDF2411213//Wohua Research Fund/ ; 82271395//the National Natural Science Foundation of China/ ; 0011/2025/RIA1//the Science and Technology Development Fund of Macau/ ; 2023A1515030073//the Guangdong Basic and Applied Basic Research Foundation/ ; 2025A04J4740//the Guangzhou Science and Technology Plan Project/ ; KY0120220133 and DFJHBF202111//the Special Project of Dengfeng Program of Guangdong Provincial People's Hospital/ ; KY012026190//Excellence Initiative Project of the National Natural Science Foundation of China/ ; },
abstract = {Radiation-induced brain injury (RIBI) is a common complication of radiotherapy that leads to neurological symptoms, significantly impairing patients' daily functioning and long-term rehabilitation. Consequently, the development of effective therapeutic strategies has received considerable attention. As an emerging approach in the management of neurological disorders, brain-computer interface (BCI) technology shows substantial potential for both the assessment and treatment of RIBI. This review synthesizes evidence retrieved from electronic databases and examines RIBI from the perspectives of its mechanisms and clinical manifestations. Current findings suggest that BCI technology holds promise for several applications in RIBI, including early diagnosis, mitigation of neuroinflammation, alleviation of associated symptoms, and prediction and management of complications. The implementation of BCI is likely to play a significant role in early assessment and treatment processes for RIBI. Furthermore, with ongoing technological advancements, the development of next-generation BCI is expected to enable more targeted treatment that address additional pathological mechanisms of RIBI, thereby progressively improving the quality of life for affected patients.},
}

@article {pmid42280831,
year = {2026},
author = {Zini, S and Bidone, F and Napoletano, P},
title = {A Robust Multi-Branch CNN-LSTM Architecture for Cross-Subject Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {11},
pages = {},
doi = {10.3390/s26113310},
pmid = {42280831},
issn = {1424-8220},
support = {PNC0000003//AdvaNced Technologies for Human-centrEd Medicine (ANTHEM)/ ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) aim to convert electroencephalographic (EEG) activity into reliable device commands across users and recording setups. However, low signal-to-noise ratio and strong inter-subject variability still limit true "plug-and-play" deployment without lengthy calibration. To address these challenges, we propose a multi-branch convolutional long short-term memory (CNN-LSTM) architecture that jointly performs multi-scale temporal feature extraction and within-trial sequence modeling. The model employs four parallel 1D convolutional branches with distinct kernel sizes, each followed by an LSTM module and late fusion, combined with group normalization and supervision over sequences of sub-windows within each trial. We evaluate the approach on the EEG Motor Movement/Imagery (EEGMMI) dataset from PhysioNet under strictly subject-independent conditions, and on the ISLab-MI Dataset, a 32-channel wearable-EEG collection designed to assess cross-setup robustness. On EEGMMI, the network achieves up to 82.63% accuracy for binary left/right MI and 74.10% for a four-class task using 4 s trials under 5-fold cross-validation, outperforming an EEGNet-style baseline by 1-10% depending on class count and window length. Under a leave-one-subject-out protocol, the model attains 74.9% mean accuracy for a three-class MI task. Zero-shot transfer to ISLab-MI yields 64.60% and 63.02% accuracy in three- and four-class settings, respectively, while brief subject-specific fine-tuning using only 20% of each session improves performance to 81.38% and 73.48%. These findings show that combining multi-scale convolutional feature extraction with explicit sequence modeling and robust normalization yields accurate, data-efficient, and portable MI decoders suitable for practical BCI applications.},
}

@article {pmid42280857,
year = {2026},
author = {Shen, X and Zhong, H and Gu, Y and Han, R},
title = {DO-PI-EATCNet: Efficient-Attention- and Dream-Optimization-Based Channel Selection for EEG Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {11},
pages = {},
doi = {10.3390/s26113336},
pmid = {42280857},
issn = {1424-8220},
support = {Grant No. BK20251914//Jiangsu Province Science and Technology Department/ ; Grant No. JC2023072//the Nantong Natural Science Foundation/ ; },
abstract = {Existing deep-learning-based motor imagery (MI) electroencephalogram (EEG) decoding methods face challenges in generalizing across sessions and providing channel-level physiological interpretability. These limitations hinder the practical application of MI-EEG systems. Accordingly, DO-PI-EATCNet (Dream-Optimization-Enhanced, Physics-Inspired, Efficient-Attention Temporal Channel Network) is proposed to improve generalization and interpretability in MI-EEG classification. Unlike models that simply combine multiple components, DO-PI-EATCNet assigns distinct roles to feature representation, temporal channel modeling, temporal regularization, and channel compactness. Latent-Projected Attention (LPA) enhances spatiotemporal discriminability by aligning attention in a low-dimensional latent space, and Temporal Channel Cascaded Collaborative Attention (TCCA) refines dependencies between time and channels. Fractional-Order Difference Temporal Consistency Loss (FD-TCL) is introduced as a neurodynamics-inspired temporal regularizer to reduce high-frequency fluctuations in prediction sequences and improve within-subject cross-session prediction stability. The Multi-Population Dream Optimization Algorithm (MPDOA) is used for channel selection to obtain a compact EEG channel subset and reduce computational load, although it introduces a slight accuracy decrease compared with the uncompressed full model. Under a within-subject cross-session protocol on the BCI Competition IV-2a four-class MI dataset, the final compact model achieves an average accuracy of 84.4% and Cohen's κ of 0.790, outperforming the reimplemented baselines. Compared with the uncompressed LPA-TCCA-FD-TCL variant, MPDOA slightly decreases accuracy from 84.9% to 84.4%, but reduces EEG channels from 22 to about 15 and decreases MACs by 27%. Scalp topographies and selected-channel visualizations provide qualitative support for channel-level anatomical plausibility, as the selected electrodes are mainly located over expected sensorimotor-related regions, while t-SNE offers a descriptive visualization of the learned feature distributions.},
}

@article {pmid42280920,
year = {2026},
author = {Zhang, Y and Gong, X and Yuan, X},
title = {MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {11},
pages = {},
doi = {10.3390/s26113402},
pmid = {42280920},
issn = {1424-8220},
support = {62171152//National Natural Science Foundation of China/ ; },
abstract = {Hybrid brain-computer interfaces (BCIs) have attracted growing research attention because they combine the millisecond-level temporal resolution of electroencephalography (EEG) with the spatially informative hemodynamic responses of functional near-infrared spectroscopy (fNIRS). However, most existing deep fusion methods rely on static late-fusion strategies, which tend to underexploit latent cross-modal dependencies and are vulnerable to modality-specific signal degradation. To address these limitations, we propose MGFNet, a multi-granularity fusion network for hybrid BCI decoding. MGFNet contains three components: (1) intra-modal encoders that learn modality-specific spatiotemporal representations from EEG, oxygenated hemoglobin (HbO), and deoxygenated hemoglobin (HbR) signals; (2) cross-modal interaction encoders that temporally align paired modalities and use dilated convolutions to capture long-range EEG-fNIRS dependencies; and (3) a Coupling-Guided Sparse Component Routing (CGSCR) module that estimates sample-specific cross-modal coupling and performs adaptive discrete routing. We further introduce a deep supervision strategy to stabilize optimization and improve branch-level discriminability. Under a within-subject held-out evaluation protocol on a public benchmark dataset, MGFNet achieved classification accuracies of 99.40% on the n-back task and 99.03% on the word generation (WG) task, outperforming representative comparison methods evaluated under a matched protocol. Ablation studies further confirmed the contributions of the intra-modal encoders, the cross-modal interaction encoders, and the CGSCR module. Under controlled EEG corruption with additive white Gaussian noise at -10 dB, MGFNet outperformed a static-fusion variant by 9.23 percentage points on the n-back task and 6.31 percentage points on the WG task. These results support the effectiveness of MGFNet in the present offline within-subject setting and indicate improved robustness under controlled single-modality degradation.},
}

@article {pmid42282632,
year = {2026},
author = {Yoon, S and Avansino, DT and Madugula, S and Levin, AD and Fan, C and Abramovich Krasa, B and Singh, A and Vo, C and Hahn, NV and Card, NS and Fogg, Z and Wairagkar, M and Nason-Tomaszewski, SR and Jacques, BG and Bechefsky, PH and Iacobacci, C and Deo, DR and Hochberg, LR and Brandman, DM and Stavisky, SD and Au Yong, N and Pandarinath, C and Henderson, JM and Willett, FR},
title = {Neural decoding of speech using deep neural ensembles.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.06.02.729705},
pmid = {42282632},
issn = {2692-8205},
abstract = {Speech brain-computer interfaces (BCIs) can restore rapid communication to people with paralysis, but decoding errors still limit performance. In recent brain-to-text decoding competitions, deep ensemble methods, which combine predictions from multiple independently trained decoders, have delivered striking accuracy improvements and account for the largest gains over baseline approaches. However, these methods have not previously been tested in real-time, require substantial computational resources, and their performance under various clinically relevant constraints remains poorly understood. Here, we present the first closed-loop test of deep ensembles in a participant with bilateral intracortical microelectrode arrays, demonstrating a reduction in word error rate from 33.7% to 26.0% on a large-vocabulary task. Using additional data from three participants, we then assess how these gains depend on baseline error rate, training dataset size, and ensemble size, including the resource-accuracy tradeoffs most relevant for real-world deployment. Finally, we introduce a computationally efficient pseudoensembling approach based on test-time augmentation that improves decoding accuracy while requiring only a single base decoder, greatly reducing the computational burden of ensembling. Together, these results show that the benefits of deep ensembling can be realized in real time and under practical resource constraints, bringing speech BCIs closer to broader clinical adoption.},
}

@article {pmid42283210,
year = {2026},
author = {Ding, W and Li, L and Liu, H and Liu, Y and Wang, J and Wang, Z and Tao, L},
title = {BOOI-Defined Obstruction Stratifies Early Outcomes After ThuLEP in Men With Detrusor Underactivity: A Retrospective Complete-Case Cohort Study.},
journal = {Lower urinary tract symptoms},
volume = {18},
number = {4},
pages = {e70073},
doi = {10.1111/luts.70073},
pmid = {42283210},
issn = {1757-5672},
support = {2022cg29//Wuhu Municipal Science and Technology Bureau/ ; },
abstract = {BACKGROUND: The benefit of transurethral outlet surgery in men with benign prostatic enlargement/benign prostatic obstruction (BPE/BPO) and detrusor underactivity (DU) remains uncertain, particularly when bladder outlet obstruction (BOO) is not demonstrated by pressure-flow testing.

OBJECTIVE: To evaluate early outcomes after transurethral thulium laser enucleation of the prostate (ThuLEP) in men with DU and BPE/BPO-related voiding dysfunction, and to examine whether BOOI-defined obstruction status stratifies early postoperative improvement.

METHODS: We retrospectively reviewed 189 ThuLEP records from a single centre and constructed a 100-patient complete-case cohort with interpretable preoperative pressure-flow studies and 3-month follow-up. DU was defined as bladder contractility index (BCI) < 100. BOO status was classified using bladder outlet obstruction index (BOOI) as definite BOO, equivocal BOO or no BOO. Functional, symptom, catheter-removal, anatomical and safety outcomes were compared descriptively across BOO strata.

RESULTS: Ninety-one patients (91.0%) had DU: 56 had definite BOO, 25 had equivocal BOO and 10 had no BOO. In the DU cohort, mean Qmax increased from 3.88 ± 1.76 mL/s at baseline to 9.98 ± 3.68 mL/s at 1 month and 11.03 ± 4.02 mL/s at 3 months (both p < 0.001). Mean PVR decreased from 267.8 ± 167.4 to 142.6 ± 115.3 mL and 124.7 ± 103.4 mL, respectively (both p < 0.001). IPSS and QoL also improved. Because enucleated specimen weight was unavailable, postoperative prostate volume was analyzed as an anatomical surrogate; mean 3-month postoperative prostate volume was 18.8 ± 4.3 mL, corresponding to an absolute volume reduction of 38.0 ± 14.2 mL and a percentage reduction of 65.4% ± 6.3% in the DU cohort. Catheter-removal success was 83.9% in definite BOO, 68.0% in equivocal BOO and 40.0% in no BOO patients (descriptive p = 0.009).

CONCLUSIONS: Preoperative BOOI-based stratification may help counsel men with DU regarding expected early functional improvement after ThuLEP. BOOI-defined no-BOO patients in this cohort were clinically selected after counseling, and the findings should be interpreted as exploratory because of the retrospective design, complete-case selection, short follow-up, and the small no-BOO subgroup.},
}

@article {pmid42182433,
year = {2026},
author = {Cattabriga, M and Alamri, AH and Hobbs, TG and Emonds, AMX and Sobinov, AR and Gaunt, RA and Greenspon, CM and Valle, G},
title = {Spatiotemporal encoding of touch signals in the human somatosensory and motor cortices.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {42182433},
issn = {2692-8205},
abstract = {The sense of touch is fundamental for dexterous manipulation, object interaction, and body awareness. It is primarily processed in the somatosensory cortex (SC), yet our understanding of how tactile information is encoded at the level of neural populations and single neurons in humans remains limited. It is unclear how natural tactile signals are represented in SC and how they may be influenced by visual inputs, as well as how closely sensory and motor cortices interact during passive touch. Here, we investigated the neural basis of touch in the human SC using chronically implanted microelectrode arrays in three participants. By delivering controlled mechanical stimuli, we characterized neural responses to natural touch and mapped detailed somatotopic receptive fields (the patch of skin that elicits neural responses when stimulated) in humans, including multidigit representations. Surprisingly, we also found strong, clearly somatotopic activation in the motor cortex (MC) during passive touch, even in the absence of movement, highlighting a tight and functionally relevant sensorimotor coupling. We further examined how vision shapes tactile processing by comparing neural activity during actual touch with and without vision, and during observation of touch on another person's hand. While touch to the participants' hands elicited robust, event-locked, and somatotopically organized responses in the SC, observation of tactile actions alone did not produce significant activation, suggesting limited vicarious encoding at this level. These findings provide a detailed characterization of human touch processing at the level of neuronal populations and give insights for the design of microstimulation strategies of the SC for the restoration of touch.},
}

@article {pmid42246522,
year = {2026},
author = {Rodionova, KN and Vigovskaya, EA and Novosad, YA and Shabunin, AS and Vissarionov, SV},
title = {[Materials and technologies in neural interfaces: optimization ways for chronic implantation].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {126},
number = {5},
pages = {21-28},
doi = {10.17116/jnevro202612605121},
pmid = {42246522},
issn = {1997-7298},
mesh = {Humans ; *Brain-Computer Interfaces ; *Brain/physiology ; },
abstract = {A neural interface is a set of tools that enable information exchange between the brain and an external device. Such systems are widely used in biomedicine, including the recovery of nervous system functions. This review summarizes the operating principles of neurointerfaces, reviews the materials used in their design, and presents examples of this technology's use in medicine, including chronic implantation.},
}

Humans
*Brain-Computer Interfaces
*Brain/physiology

@article {pmid42266219,
year = {2026},
author = {Gu, T and Li, J and Chen, T and Pan, Y and Yu, Q and Sha, J},
title = {Effect of biofeedback electrical stimulation combined with HoLEP on surgical outcomes in patients with benign prostatic hyperplasia complicated with detrusor underactivity: a retrospective cohort study.},
journal = {Frontiers in surgery},
volume = {13},
number = {},
pages = {1847062},
pmid = {42266219},
issn = {2296-875X},
abstract = {OBJECTIVE: To investigate the clinical efficacy and safety of biofeedback electrical stimulation combined with holmium laser enucleation of the prostate (HoLEP) in the treatment of patients with benign prostatic hyperplasia (BPH) complicated by detrusor underactivity (DUA).

METHODS: A retrospective analysis was conducted on 100 patients with BPH and DUA who had surgical indications and were treated in the Department of Urology of our hospital from January 2023 to June 2025. Patients were divided into an intervention group (n = 51) and a control group (n = 49) according to the treatment modality they received. Patients in the intervention group underwent HoLEP followed by biofeedback electrical stimulation therapy (three times per week for a total of 10 sessions), whereas those in the control group received HoLEP alone. The International Prostate Symptom Score (IPSS), Quality of Life score (QOL), maximum urinary flow rate (Qmax), bladder contractility index (BCI), bladder outlet obstruction index (BOOI), maximum detrusor pressure (Pdetmax), post-void residual volume (PVR), voiding efficiency (VE), and postoperative complications were compared between the two groups before surgery and at 3 months postoperatively.

RESULTS: Baseline characteristics were comparable between the two groups (P > 0.05). At 3 months postoperatively, the intervention group showed significantly higher Qmax (14.38 ± 1.47 mL/s vs. 10.01 ± 0.85 mL/s, P < 0.001) and BCI (111.68 ± 10.15 vs. 93.96 ± 8.42, P < 0.001), significantly lower IPSS (10.8 ± 1.9 vs. 18.6 ± 2.1, P < 0.001) and QOL scores (2.1 ± 0.8 vs. 3.0 ± 0.6, P < 0.001), significantly lower PVR (21.8 ± 5.8 mL vs. 40.2 ± 7.5 mL, P < 0.001), and significantly higher VE (77.8 ± 6.2% vs. 61.9 ± 5.8%, P < 0.001) compared with the control group. The proportion of patients achieving Qmax ≥15 mL/s at 3 months postoperatively was 39.2% in the intervention group vs. 20.8% in the control group (P = 0.022). At 90 days postoperatively, the incidence rates of urinary tract infection (13.7% vs. 28.6%, P = 0.047), urinary incontinence (9.8% vs. 24.5%, P = 0.039), and indwelling catheter reinsertion (2.0% vs. 12.2%, P = 0.037) were significantly lower in the intervention group than in the control group. No significant differences were observed in the incidence of postoperative bleeding or urethral stricture between the two groups (P > 0.05).

CONCLUSION: Biofeedback electrical stimulation combined with HoLEP significantly improves voiding function, clinical symptoms, and quality of life in patients with BPH and DUA, enhances bladder contractility, and reduces the risk of postoperative complications, offering clear clinical benefits and a favorable safety profile, warranting broader clinical adoption.},
}

@article {pmid42266278,
year = {2026},
author = {Achanccaray, D and Clodic, A and Roy, RN},
title = {Error-related potentials detection to enhance human-robot collaboration: a mini review.},
journal = {Frontiers in neuroergonomics},
volume = {7},
number = {},
pages = {1769098},
pmid = {42266278},
issn = {2673-6195},
abstract = {Error-related potentials (ErrPs) have been studied to evaluate wrong decisions or actions in several contexts. An ErrP is an electrical potential on the scalp generated by the perception of errors and occurs unwittingly. In human-robot collaboration (HRC), ErrP detection can be used to trigger a feedback or an action to adapt the system to the user. This contributes to the improvement of HRC, taking into account user performance. However, to our knowledge, the detection of ErrPs in HRC has not been widely explored, resulting in only a few studies. This systematic review will present work on ErrP-based interfaces related to adaptation, control, and neuroergonomics for HRC. Thirteen articles were included after the exclusion criteria of the review stages. The average accuracy of ErrP detection was between 54 and 87.2%. In most cases, the authors simulated the occurrence of unexpected behavior of the robot. The robot mistakes occurred randomly between 20 and 35% of the total trials. Some works focused on the robot learning process and adaptation between humans and robots. The mental model and the robot behavior policy were updated based on the decoded ErrPs during collaborative interactions. Control-related works have included ErrPs detection/features as input inside the control loop or algorithm. Other studies assessed the influence of mental workload variability in the adaptation process, given that a high mental workload affects the cognitive processes needed to perceive errors. Thus, ErrPs present advantages for enhancing HRC, and this review opens the way to further developments in the robotic domain.},
}

@article {pmid42269199,
year = {2026},
author = {Wei, J and Ye, H and Shao, B and Ma, L and Ding, Q and Zhou, Z and Cao, X and Zhong, J and He, H},
title = {A spatiotemporal dependency-aware lightweight CNN-ViT network for 3D MRF with a balanced acceleration strategy.},
journal = {Medical image analysis},
volume = {113},
number = {},
pages = {104147},
doi = {10.1016/j.media.2026.104147},
pmid = {42269199},
issn = {1361-8423},
abstract = {The push for rapid MRI acquisition aims to enhance clinical efficiency and diagnostic consistency by shortening scan times. 3D Magnetic Resonance Fingerprinting (MRF) has emerged as a promising technique for fast, multi-parametric quantitative imaging. However, its accuracy and relatively long acquisition time remain a limiting factor for clinical adoption. Accelerating MRF while preserving quantitative accuracy constitutes a crucial research objective. Deep learning approaches have recently been applied to accelerate MRF parameter quantification, but existing methods still exhibit notable limitations in both acceleration scheme design and the ability to model the complex contextual information embedded in MRF data. To address these limitations, we propose a lightweight spatiotemporal attention enhanced network (LiST-UNet) that integrates convolutional neural networks with lightweight Vision Transformer components to model long-range spatiotemporal dependencies in 3D MRF. A precursor-successor network is included to model interrelationships among tissue parameters, improving T2 quantification accuracy, while a balanced k-space and temporal-frame acceleration strategy significantly reduces errors compared with single-dimension undersampling schemes. Experimental results demonstrate that the proposed method enables whole-brain MRF imaging in approximately 1.25 min, achieving an eightfold acceleration over conventional 10-minute acquisitions with superior quantification accuracy and image quality compared to previously proposed deep learning methods. This work combines architectural improvements in MRF reconstruction with an acceleration strategy, supporting the future clinical translation of 3D MRF.},
}

@article {pmid42270932,
year = {2026},
author = {Shen, JJ and Yang, YJ and Tang, YY and Yan, S and Ying, MD and Chen, WH and Xu, LL},
title = {m[6]A-modified Mid1 promotes sevoflurane-induced cognitive impairment in neonatal mice by ubiquitin-mediated degradation of Syngap1.},
journal = {Experimental & molecular medicine},
volume = {},
number = {},
pages = {},
pmid = {42270932},
issn = {2092-6413},
abstract = {Investigating the cognitive effects of sevoflurane exposure during early development is essential due to its potential long-term neurodevelopmental impacts. This investigation systematically explored the molecular basis of sevoflurane-induced cognitive impairment, with emphasis on m[6]A RNA modifications and ubiquitin-dependent proteostasis involving Mid1 and Syngap1. Using integrated approaches, including methylated RNA immunoprecipitation sequencing (MeRIP-seq), transcriptomic profiling, neurobehavioural testing and molecular analyses, 2091 m[6]A methylation sites were identified that were differentially regulated. Mechanistically, Mid1 was found to orchestrate Syngap1 degradation via the ubiquitin-proteasome pathway, establishing a direct link between protein stability control and cognitive outcomes. Behavioural phenotyping demonstrated that Mid1 suppression ameliorated learning and memory deficits in sevoflurane-exposed mice, which was corroborated by improved neuronal viability and attenuated apoptotic signalling in biochemical assays. Epigenetic regulation studies further revealed that the m[6]A eraser ALKBH5 and the reader YTHDF2 collaboratively modulate Mid1 mRNA stability, thereby contributing to neuropathological progression. Pathway analysis uncovered Mid1-Syngap1 axis-mediated dysregulation of MAPK signalling cascades, proposing this network as a potential therapeutic target. Collectively, the present findings delineated a novel m[6]A-ubiquitin regulatory circuit centred on Mid1 that drives sevoflurane-associated cognitive dysfunction, offering mechanistic insights for the development of neuroprotective interventions against anaesthesia-related neurotoxicity in paediatric and other at-risk populations.},
}

@article {pmid42270979,
year = {2026},
author = {Wang, J and Yang, C and Chang, S and Jiao, D and Lin, J and Yang, X and Cai, W and Ma, D and Ding, ZJ and Huang, J and Huang, J and Fan, M and Hu, M and Wang, Y and Xu, H and Su, N and Guo, J},
title = {Cryo-EM structures of Drosophila OR67d-Orco complexes reveal insect pheromone sensing mechanism.},
journal = {Cell research},
volume = {},
number = {},
pages = {},
pmid = {42270979},
issn = {1748-7838},
support = {32371204//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32421001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371300//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024T170801//China Postdoctoral Science Foundation/ ; },
abstract = {Pheromones mediate intraspecific communication to regulate the physiology and behavior of animals, particularly insects. The detection of pheromones is initiated by the binding of pheromone molecules, e.g., 11-cis-vaccenyl acetate (cVA) in Drosophila, to specific receptor proteins in chemosensory neurons, but the underlying molecular mechanisms remain unclear. Here, we report structures of Drosophila pheromone receptor OR67d-Orco complexes in apo closed, pheromone-bound open, and synthetic agonist VUAA1-bound open conformations. OR67d and Orco assemble into a hetero-tetrameric channel with a 1:3 stoichiometry. In OR67d, the inverted L-shaped cVA or its analog binds into a deep and bent hydrophobic pocket, inducing both local and global conformational changes that lead to an asymmetrical opening of the channel gate. By comparison, VUAA1 binds to Orco instead of OR67d to cause a similar asymmetrical opening. Together, our studies reveal the structural basis for pheromone activation of hetero-tetrameric pheromone receptors.},
}

@article {pmid42271499,
year = {2026},
author = {Shi, Z and Yuan, Z and Gao, L and Hu, Y and Ni, G and Liao, W and Xie, Y and He, J and Xiao, D and Chen, X and Wang, Z},
title = {Mixed reality assisted target localization for transcranial magnetic stimulation navigation: a feasibility study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-02045-z},
pmid = {42271499},
issn = {1743-0003},
support = {3502Z20254ZD1008//Xiamen Municipal Guiding Project for Medical and Health Services/ ; },
abstract = {BACKGROUND: Transcranial magnetic stimulation (TMS), as a non-invasive neurostimulation technique, modulates neural activity by applying electromagnetic fields to specific areas of the brain. It is clinically used for several approved indications, including major depressive disorder, obsessive‑compulsive disorder, and migraine with aura, and is under active investigation for other neurological and psychiatric conditions. Accurate stimulation targeting is crucial for the effectiveness of TMS. Existing targeting methods, such as generic brain localization caps and the international 10-20 electroencephalogram (EEG) system, generally provide only rough localization, leading to significant targeting errors. In recent years, significant progress has been made in the application of mixed reality (MR) technology in medicine, particularly in surgical navigation, offering new ideas and possibilities for developing a simple, low-cost, and efficient TMS navigation system.

OBJECTIVE: This study proposes, for the first time, a portable MR navigation system for non-invasive neural modulation target localization. The aim is to evaluate its localization accuracy and operational efficiency in TMS through preclinical validation. This system seeks to provide a simple and high-precision localization solution for other non-invasive technologies, with the goal of improving localization accuracy and simplifying the operational workflow in clinical applications.

METHODS: The system is based on Microsoft HoloLens 2 and features three specifically designed interaction tools. Five different types of simulation head models were selected, and ten target points were set on each head model. CT scanning was used to obtain imaging data for each head model. Three researchers used the system to perform target localization and repeated the verification process by adjusting the head model posture (from standing to lying) to assess localization accuracy and efficiency.

RESULTS: The validation conducted by the three researchers showed the following results: In the standing position of the simulated head model, the measurement errors were 2.4 (IQR: 1.4-2.7) mm, 2.3 (IQR: 1.7-2.7) mm, and 2.6 (IQR: 1.9-3.0) mm, respectively. In the lying position of the simulated head model, the measurement errors were 1.9 (IQR: 1.6-2.4) mm, 2.0 (IQR: 1.4-3.0) mm, and 2.5 (IQR: 1.9-2.9) mm, respectively. There was a significant difference between researchers (p < 0.05), but no significant difference within the same researcher (p > 0.05).

CONCLUSION: The TMS-Guide, based on mixed reality technology, is a portable and simple navigation solution that provides higher localization accuracy than traditional manual targeting. It shows promising potential for broader applications in non-invasive neural modulation and brain-computer interface fields.},
}

@article {pmid42271590,
year = {2026},
author = {Imura, T},
title = {[Communication Support for Neurological Disorders].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {78},
number = {6},
pages = {701-705},
doi = {10.11477/mf.188160960780060701},
pmid = {42271590},
issn = {1881-6096},
abstract = {Communication support for patients with neurological disorders extends beyond high-technology communication aids. It also includes non-aided and low-technology methods and requires flexible selection and the combined use of these methods depending on the situation. Gaining experience with various communication strategies in a stepwise manner from an early stage enables the smoother introduction of advanced communication devices when necessary. Effective support must be tailored to the disease stage, as communication abilities and needs change over time. In this context, collaboration among multiple professionals is essential. Such interprofessional collaboration enables appropriate assessment, timely intervention, and continuity of care across disease stages. A team-based, continuous support system benefits patients, and caregivers and professionals involved in their care. By sharing knowledge, skills, and responsibilities within a support team, the burden on individual supporters can be reduced, and the quality and consistency of communication support can be enhanced. Looking to the future, further development of emerging technologies such as eye-gaze input systems, personalized speech synthesis, and brain-machine interfaces is highly anticipated. However, careful consideration of their characteristics, limitations, and potential risks is necessary to ensure their safe and effective use in clinical practice.},
}

@article {pmid42272373,
year = {2025},
author = {Aabideen, M and Ashokan, A and Nivargi, SM and Ramzan, M and Boddu, D and Aboobacker, F and Odat, A and Taher, M and Mohamed, R and Cingapagu, R and Al Shamsi, HO and Aabideen, Z},
title = {Single Centre Experience of Treating Children with Cancer in the United Arab Emirates.},
journal = {The Gulf journal of oncology},
volume = {1},
number = {49},
pages = {80-84},
pmid = {42272373},
issn = {2521-3881},
abstract = {INTRODUCTION: Cancer is a leading cause of death in children. Advancements in medical sciences have significantly improved childhood cancer outcome. However, the pattern of malignancy and outcomes of childhood cancer in the UAE have not been published in the literature in the recent period. Therefore, we aim to investigate this in a leading cancer institute in Abu Dhabi, United Arab Emirates (UAE).

METHODOLOGY: This is a retrospective study. We collected data including diagnosis; age at diagnosis; treatments used e.g., chemotherapy, radiation, surgery, immunotherapy and bone marrow transplant (BMT) and outcomes. Overall survival (OS) and Event-Free Survival (EFS) were estimated using the Kaplan Meier method.

RESULTS: There are 82 children with cancer. The male-tofemale ratio is 1.2. Most patients (45%) were diagnosed between one to five years of age. The most common malignancies are B cell Acute Lymphoblastic Leukaemia (32, 39%), brain tumours (12, 15%); Neuroblastoma (9, 10%), Hodgkin Lymphoma (8, 9%) and Wilms Tumour (5, 6%); Acute Myeloid Leukaemia (3, 4%); Non-Hodgkin Lymphoma (3, 4%); Ewing Sarcoma (3, 4%); T ALL (2, 2%); Osteosarcoma (2, 2%); Angiosarcoma (1, 1%); Synovial Sarcoma (1, 1%). 28 (34%) of patients had completed all treatment at Burjeel Cancer Institute (BCI); 15 (18%) had completed their treatment at another centre and attending follow-up at BCI; and 5 (6%) commenced their treatment at BCI and were transferred to another centre. 34 (41%) but currently still undergoing treatment at BCI. The abandonment rate is 0%. Overall survival is 94% and event-free survival is 90%.

DISCUSSION: The rapid progression of UAE cancer care over the past four decades has contributed massively to our favourable survival outcomes. Radiation therapy, bone marrow stem cell transplantation, strict medication regulation and monitoring by the UAE Department of Health have been established in recent years to further enhance treatment for cancer patients in the UAE.

CONCLUSION: The results of our study are comparable to the international standard. More studies involving multiple centres in the UAE are needed to ascertain the exact pattern of paediatric malignancy and outcomes in the UAE.},
}

@article {pmid42274110,
year = {2026},
author = {Chen, HL and Zou, S and Zheng, LL},
title = {Brain-Computer Interface Applications in Craniofacial Nerve Functional Reconstruction: A Narrative Review.},
journal = {The Journal of craniofacial surgery},
volume = {},
number = {},
pages = {},
doi = {10.1097/SCS.0000000000013038},
pmid = {42274110},
issn = {1536-3732},
abstract = {BACKGROUND: Brain-computer interface (BCI) technology is increasingly relevant to craniofacial nerve functional reconstruction because it can decode cortical motor intent and convert it into physical or digital output when peripheral motor pathways are impaired. Facial nerve palsy, dysphagia, and oromandibular motor dysfunction remain difficult to treat when conventional nerve repair, muscle transfer, or electrical stimulation cannot restore coordinated and natural movement.

METHODS: This narrative review synthesized peer-reviewed literature on BCI-related craniofacial functional reconstruction. A targeted search of PubMed, Embase, Web of Science, and Google Scholar was performed, covering English-language articles published from 2014 to April 12, 2026. Eligible core articles addressed BCI-based facial motor restoration, swallowing or oromandibular BCI paradigms, speech or orofacial neuroprosthetics, neural interface integration in craniofacial surgery, flexible facial bioelectronic sensing, or functional electrical stimulation systems with direct relevance to craniofacial nerve recovery. Background literature was cited separately to contextualize disease burden, conventional reconstruction, dysphagia, outcome assessment, calibration, and neuroethical issues.

RESULTS: Twenty-two core articles were included in the final thematic synthesis and organized into 3 domains: facial expression motor reconstruction, oromandibular and swallowing rehabilitation, and neural interface integration in craniofacial surgery. EEG-based facial-expression decoding has shown promising accuracy under controlled laboratory conditions, speech neuroprosthetics provide potentially transferable frameworks for orofacial motor decoding that remain unproven in facial palsy or dysphagia rehabilitation, swallowing motor-imagery studies support physiological feasibility for dysphagia-oriented BCI, and flexible facial biosensors may support future closed-loop systems.

CONCLUSIONS: BCI technology should be regarded as a potential complement to conventional craniofacial reconstruction rather than a replacement for established surgical techniques. Current evidence supports technical feasibility, but clinical translation will require naturalistic decoding, durable interfaces, faster patient-specific calibration, meaningful outcome measures, and early attention to ethical issues.},
}

@article {pmid42274641,
year = {2026},
author = {Shahbaz, H and Sherman, AB and Shaikh, FA and Elsawwah, JK and Charles, EJ and Curran, T and Nemeth, ZH},
title = {Risk factors and outcomes of blunt cardiac injury in adult motor vehicle collision patients.},
journal = {Traffic injury prevention},
volume = {},
number = {},
pages = {1-5},
doi = {10.1080/15389588.2026.2674238},
pmid = {42274641},
issn = {1538-957X},
abstract = {OBJECTIVES: Motor vehicle protective equipment, such as seatbelts and airbags, has improved occupant safety. However, while seatbelts reduce facial and abdominal injuries, they may not significantly prevent head, neck, or thoracic trauma. Limited data exist on blunt cardiac injury (BCI). This study evaluated patterns of BCI, associated thoracic injuries, and hospital outcomes in adult trauma patients following motor vehicle collisions (MVCs).

METHODS: We analyzed the 2023 American College of Surgeons Trauma Quality Improvement Program (ACS-TQIP) database for adult MVC occupants. Abbreviated Injury Scale codes 4208xx.x, 4404xx.x, 4410xx.x, 4412xx.x, 4413xx.x, and 4416xx.x identified patients with BCI. Those without BCI formed the reference cohort. A 1:1 propensity score match (PSM) on Injury Severity Score (ISS) was performed using RStudio to balance collision severity.

RESULTS: In the overall cohort, the incidence of BCI was 1.2% (1,914/161,446). After PSM, 1,914 patients remained in each cohort with a mean ISS of 22.7. Both seatbelt plus airbag use and airbag use alone were independently associated with increased odds of BCI. BCI was strongly associated with thoracic injuries, including sternum fracture (odds ratio [OR] 3.492; 95% CI 2.95-4.14), hemothorax (OR 2.928; 95% CI 2.29-3.75), thoracic aortic injury (OR 1.773; 95% CI 1.29-2.44), and pulmonary contusion (OR 1.382; 95% CI 1.18-1.62). In multivariable analysis with BCI as the outcome, mortality (OR 2.325; 95% CI 1.93-2.79) and cardiac arrest (OR 1.827; 95% CI 1.29-2.59) were independently associated with BCI.

CONCLUSION: Protective equipment use correlates with BCI and thoracic trauma. In MVC patients using seatbelts and airbags, concomitant chest injuries should heighten suspicion for BCI and prompt further evaluation.},
}

@article {pmid42275342,
year = {2026},
author = {Abdollahpour, N and Artan, NS},
title = {EC-Transformer: Connectivity-Informed Embeddings and Adaptive Gating for fNIRS.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3702505},
pmid = {42275342},
issn = {2168-2208},
abstract = {Functional Near-Infrared Spectroscopy (fNIRS) provides a non-invasive modality for monitoring brain activity, yet jointly modeling temporal dynamics and inter-regional interactions remains challenging for accurate brain-computer interface (BCI) decoding. This study proposes an Effective Connectivity Transformer (EC-Transformer), which integrates connectivity-informed representations into transformer-based modeling of fNIRS signals. The architecture combines a time- wise embedding that captures temporal dynamics using positional encoding and bidirectional LSTMs with a connectivity-based embedding that encodes low-frequency directed dependency patterns. An adaptive gating mechanism dynamically fuses these representations during classification. The model was evaluated using leave-one-subject-out validation on two public fNIRS datasets involving mental arithmetic and motor execution tasks, achieving accuracies of $76.83 \pm 2.4$ and $76.03 \pm 2.00$, respectively. The proposed framework demonstrates competitive performance relative to existing transformer-based approaches while maintaining substantially lower model complexity (approximately 0.7 M parameters compared to 1.7M-3.5 M in prior models). Ablation and control analyses further suggest that EC-based embeddings provide connectivity-informed representations that complement temporal modeling while maintaining competitive decoding performance. Interpretability analyses revealed task-related connectivity patterns broadly consistent with distributed cognitive and motor-related networks. Overall, the findings suggest that incorporating connectivity-informed representations can provide physiologically structured complementary information for transformer-based fNIRS decoding while maintaining competitive performance and computational efficiency.},
}

@article {pmid42275895,
year = {2026},
author = {Chen, W and Daly, I and Chen, Y and Li, J and Wu, X and Zhao, R and Wang, X and Cichocki, A and Jin, J},
title = {EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {204},
number = {},
pages = {109197},
doi = {10.1016/j.neunet.2026.109197},
pmid = {42275895},
issn = {1879-2782},
abstract = {Motor imagery is a non-invasive process that operates independently of external stimuli, and can be used to establish a direct connection between the brain and external devices solely through the imagination of a specific movement. Nonetheless, the complexity and variability of neural patterns pose substantial challenges, as accurately decoding motor imagery from electroencephalography signals remains a significant obstacle. This paper introduces an enhanced dynamic spatiotemporal -frequency attention convolutional neural network (EDSF-Net) for the precise decoding of motor imagery. EDSF-Net employs a refined spatiotemporal attention mechanism, grounded in enhanced dynamic convolution (EDConv), to emphasize localized spatial features alongside high and low-frequency temporal characteristics. Subsequently, EDConv is utilized for global spatial feature extraction. Following this, group convolutions formed by EDConv are implemented to fuse the extracted features effectively. Ultimately, a synchronized channel-frequency attention mechanism is employed to capture critical channel and frequency domain information, facilitating the model's focus on features most pertinent to the task throughout the learning process. We conducted a comprehensive evaluation of the performance of EDSF-Net on two public datasets, BCI Competition IV 2a and OpenBMI. In the hold-out session experiments, EDSF-Net achieved decoding accuracies of 84.26% and 75.14%, respectively. In the leave-one-subject-out experiments, EDSF-Net attained decoding accuracies of 66.78% and 82.24%, respectively. These results show that EDSF-Net has robust generalization capabilities, affirming its efficacy in addressing complex pattern recognition tasks, with significant potential for diverse applications.},
}

@article {pmid42275898,
year = {2026},
author = {Cheng, C and Zhang, J and Cheng, Y and Jia, Z and He, W},
title = {MS-STGAN: A dual-branch multi-scale spatio-temporal generative adversarial framework for incomplete EEG-based emotion recognition.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {204},
number = {},
pages = {109203},
doi = {10.1016/j.neunet.2026.109203},
pmid = {42275898},
issn = {1879-2782},
abstract = {Electroencephalography (EEG) enables high-resolution emotion recognition but often suffers from incomplete data in real-world scenarios due to sensor failures or preprocessing errors. To this end, we propose a Multi-Scale Spatio-Temporal Generative Adversarial Network (MS-STGAN). Specifically, we first apply random masking to the EEG channels data to simulate missing data conditions in practical environments. Then, we design a spatio-temporal dual-branch generator to reconstruct complete representations: the spatial branch employs graph convolutional networks (GCNs) to capture robust inter-regional dependencies, while the temporal branch leverages the BiMamba state space model to encode the dynamic evolution of emotions. To further enhance feature learning, multi-scale 2D convolution and deconvolution layers are incorporated before and after both branches, enabling the extraction of diverse spatio-temporal features. Additionally, we introduce a generative adversarial framework, where the generator restores informative features from incomplete inputs and the discriminator enforces the authenticity of reconstructed data. Finally, a fusion module integrates the outputs of both branches for downstream classification. Extensive experiments on the DEAP and SEED-IV datasets validate the effectiveness of each component and demonstrate that MS-STGAN achieves superior performance and strong generalization ability.},
}

@article {pmid42276068,
year = {2026},
author = {Kistler, W and Fakhreddine, R and Rodriguez, GR and Hayward, M and Buch, ER and Bestmann, S and Cohen, LG},
title = {Early skill learning is shaped by the offline emergence of expert synergies.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2026.05.032},
pmid = {42276068},
issn = {1879-0445},
abstract = {Everyday skilled actions depend on the formation of coordinated motor synergies that integrate multiple digits into stable, low-dimensional control units. Although initial practice of a new skill leads to rapid performance improvements, it is unclear whether the underlying movement kinematics reorganize on a similar timescale or in a way that directly relates to these gains. It also remains uncertain whether such reorganization occurs mainly during active practice or instead during brief rest breaks. Here, we tracked the temporal evolution of multi-digit synergy formation during early learning of a naturalistic keypress skill. Initial practice rapidly sculpted the motor repertoire toward higher-order, temporally compressed, and overlapping multi-digit synergies. These synergies emerged after only minutes of practice and continued to be expressed throughout the full training session. Notably, they were primarily shaped across brief rest breaks and robustly predicted individual skill proficiency. Across learning, distinct synergy subtypes emerged that differed in their heuristic prevalence. Rarely expressed synergies reflected transient novice patterns, synergies expressed at intermediate levels could index exploratory and trial-initiation strategies, and highly expressed synergies emerged later to dominate performance, reflecting the consolidation and expansion of skilled motor control. Together, these findings indicate that skilled performance is supported by the early formation of a compact repertoire of expert multi-digit synergies that emerge preferentially across rest periods and predict subsequent skill gains. They further raise the hypothesis that explicitly training such expert synergies alongside task goals could enhance learning in domains such as the arts, sports, and neurorehabilitation.},
}

@article {pmid42262704,
year = {2026},
author = {Luo, N and Yang, Z and Song, M and Di, S and Chu, C and Shi, W and Yue, W and Zhang, Y and Yan, H and Zhang, X and Zhang, D and Sui, J and Calhoun, V and Jiang, T},
title = {Measuring the Impacts of Urbanicity and Different Exposome Factors on Human Brain through Exposure Network Mapping.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42262704},
issn = {1995-8218},
abstract = {While urbanicity increases the risk of mental health issues, its effects on brain networks are heterogeneous and underexplored in relation to different exposome factors. Using a coordinate network mapping strategy termed exposure network mapping (ENM) across eight datasets, this study first consolidated heterogeneous findings of urbanicity to a significant, replicable network involving the middle frontal gyrus, orbital gyrus, and anterior cingulate gyrus. Afterwards, among the other factors examined (air pollution, noise, income, stress, green space), only stress converged into a distinct common network, highlighting the orbital gyrus, caudate, anterior/middle cingulate gyrus, hippocampus, and middle frontal gyrus. This ENM-stress map exhibited the highest correlation with both the ENM-urbanicity map (r = 0.77) and a transdiagnostic map (r = 0.72). In addition, sleep-related coordinates also formed a consistent network, involving the middle cingulate gyrus, orbital gyrus, caudate, and putamen, which correlated strongly with urbanicity (r = 0.75), stress (r = 0.80), and the transdiagnostic pattern (r = 0.55). Collectively, this study highlights the potential risks of urbanicity and stress, as well as the protective role of sleep on brain networks, which may offer new insights for preventing mental health issues in urban environments.},
}

@article {pmid42262958,
year = {2026},
author = {Mrachacz-Kersting, N and Pasluosta, C and Meyer, B and Nascimento, OFD and Stieglitz, T and Farina, D},
title = {Cortical Activity Associated with Phantom Leg Movements.},
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.2026.3701504},
pmid = {42262958},
issn = {1558-0210},
abstract = {We tested the feasibility for amputees to control artificial limbs using non-invasive electroencephalography (EEG). Thirteen participants engaged in attempts of isometric ankle plantar-flexions using their phantom or intact limb at slow or ballistic speeds. EEG data were analyzed for movement-related cortical potentials (MRCPs), the slow negative potentials related to the planning and execution of movements. We focused on temporal profiles and single-trial classification at electrode location Cz where MRCPs are most prominent. Distinctly different MRCP morphologies were observed for both movement speeds and phantom versus intact limbs. Crucially, time since amputation correlated significantly with classification errors for distinguishing tasks performed with the intact limb from those of the phantom limb (R = 0.36, p =0.004) and movement speed during trials of only the phantom limb (R = -0.33, p = 0.01). Here we show the persistent capacity of amputees to plan and attempt to execute limb motions at varying speeds using their phantom limb. This has implications for understanding neural adaptations over extended post-amputation periods and for the practical implementation of the MRCP in the design of brain-computer interfaces to control prosthetic devices using single-electrode EEG recordings.},
}

@article {pmid42263493,
year = {2026},
author = {Offenberg, EC and Berezutskaya, J and Müller, L and Freudenburg, ZV and Ramsey, NF and Vansteensel, MJ},
title = {Optimal positioning and size of high-density electrocorticography grids for speech brain-computer interfaces.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {190},
number = {},
pages = {2111940},
doi = {10.1016/j.clinph.2026.2111940},
pmid = {42263493},
issn = {1872-8952},
abstract = {OBJECTIVE: Speech-based brain-computer interfaces (BCIs) can offer an intuitive communication method for those who have lost the ability to speak due to paralysis. Significant progress has been made in classifying individual words from high numbers of electrocorticographic (ECoG) electrodes on the sensorimotor cortex (SMC). As implantations of larger grids with more ECoG electrodes are associated with higher surgical risk, we investigated whether confined electrode configurations can match the classification accuracy of larger grids.

METHODS: We analyzed data from eight able-bodied participants with high-density ECoG grids (64 to 128 electrodes) performing a 12-word repetition task in Dutch.

RESULTS: Word pronunciation elicited high frequency band activity in two SMC foci: one ventral, one dorsal. Smaller, rectangular configurations with surface areas of 325 mm[2] to 561 mm[2] (32 electrodes) could achieve similar word classification accuracies as larger grids: 76 ± 16% versus 75 ± 17% across participants, respectively (practical chance level 16.7%). The best configurations were oriented vertically and centered on the central sulcus.

CONCLUSION: These findings indicate that a 32-electrode ECoG grid placed optimally can be sufficient for achieving high word classification accuracy on a closed set of words.

SIGNIFICANCE: These findings support the targeted placement of small ECoG grids, reducing surgical demands on end users and justifying energy- and complexity-efficient designs of fully implantable BCI devices for individuals with severe paralysis.},
}

@article {pmid42264810,
year = {2026},
author = {Haroon, N and Jabbar, H and Jeong, TT and Khan, US and Rashid, N and Naseer, N},
title = {Investigating cognitive fatigue recovery through mechanical massage and binaural beats: An AI-driven fNIRS study.},
journal = {Journal of bodywork and movement therapies},
volume = {47},
number = {},
pages = {305-331},
doi = {10.1016/j.jbmt.2026.04.004},
pmid = {42264810},
issn = {1532-9283},
abstract = {Cognitive fatigue is a state of reduced mental performance resulting from prolonged periods of cognitive activity. It is characterized by a sense of tiredness that reduces decision-making abilities. To date, there remains a significant gap in classifying cognitive fatigue under the influence of mechanical massage via massage chair and binaural beats brain massage aided by functional Near-Infrared Spectroscopy. Our aim is to explore the impact of mechanical and binaural brain massage on cognitive fatigue recovery whilst carrying out an extensive comparative analysis of the efficacy of the existing Deep Learning (DL) models alongside conventional Machine Learning (ML) models. The experimental paradigm is consisted of two treatments: Treatment A (Control (General Rest) Group) and B (Experimental Group). Real-time data acquisition of 10 test subjects before and after both treatments is being done. Following a meticulous features extraction protocol, a comprehensive set of 8 DL and 8 ML models is utilized, and their performance is evaluated through a comparative analysis. The categorical results unequivocally demonstrate that Temporal Convolutional Network achieves superior performance by outperforming other DL models, boasting a remarkable accuracy of 97% and 96.52% for Treatment A and B, respectively. Likewise, Support Vector Machine with Radial Basis Function overtakes other ML models by yielding 91.00% and 87.50% accuracy for Treatment A and B, respectively. Upon evaluation of models' performance in Brain-Computer Interface application, it's been concluded that mechanical massage along with binaural beats significantly helps to relieve mental fatigue, enhance working memory, and mental vigilance.},
}

@article {pmid42265105,
year = {2026},
author = {Gracia, DI and Iáñez, E and Ortiz, M and Azorín, JM},
title = {An Electrospinography Database of Gait-Related Tasks and Motor Imagery Exercises.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07592-7},
pmid = {42265105},
issn = {2052-4463},
abstract = {This study presents a dataset of electrospinography (ESG) signals recorded from the human spinal cord during gait-related activities and motor imagery tasks. The dataset was acquired as part of a broader initiative to develop a spinal-machine interface (SMI) for closed-loop control of lower limb exoskeletons. ESG signals were collected using high-density surface electromyography (HD-sEMG) electrodes from fourteen able-bodied participants performing baseline trials (2), movement execution tasks (12) and motor imagery tasks conducted both in static conditions (5) and during movement (5). The dataset encompasses multiple electrode configurations targeting the brachial and lumbar plexuses, as well as surrounding musculature, across three experimental protocols. A total of 10 sessions were recorded for Experiment 1 (one 64-electrode matrix), 10 sessions for Experiment 2 (two 32-electrode matrices) and 5 sessions for Experiment 3 (two-32 electrode matrices). Preprocessing techniques were applied to mitigate cardiac and motion artifacts. The data provides a valuable and pionneering resource for advancing neurorehabilitation research, allowing the refining of exoskeleton control strategies and improving artifact removal methods.},
}

@article {pmid42265352,
year = {2026},
author = {Busch, EL and Fincke, EC and Lajoie, G and Krishnaswamy, S and Turk-Browne, NB},
title = {Human learning of noninvasive brain-computer interfaces via manifold geometry.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42265352},
issn = {1546-1726},
support = {1839308//National Science Foundation (NSF)/ ; 2139841//National Science Foundation (NSF)/ ; 2047856//National Science Foundation (NSF)/ ; R01MH069456//U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)/ ; R01GM130847//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01GM135929//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; FG-2021-15883//Alfred P. Sloan Foundation/ ; },
abstract = {Brain-computer interfaces (BCIs) promise to restore and enhance human capabilities. Yet, their adoption has been limited by slow and inconsistent learning across users. We show that BCI learning is accelerated by leveraging the naturally occurring geometry, or intrinsic manifold, of brain activity, extracted using data diffusion. Participants were trained with real-time functional magnetic resonance imaging to control an avatar in a video game by self-modulating activity in brain regions supporting spatial navigation. We perturbed the mapping between brain activity and avatar movement to test how neural manifolds constrain human BCI learning. When new mappings relied on directions of significant variance on the intrinsic manifold, participants successfully gained control by realigning brain activity along these directions. When new mappings did not follow the intrinsic manifold, participants could not learn to control the avatar. These findings show how manifold geometry in higher-order brain regions guides human learning of complex cognitive tasks, identifying a principle for improving future neurotechnologies.},
}

@article {pmid42265834,
year = {2026},
author = {Ling, Y and Sun, P and Guo, T and Luo, B},
title = {"Digital eye tracking and plasma biomarkers: Distinguishing functional cognitive impairment from Alzheimer's disease biology".},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {22},
number = {6},
pages = {e71574},
doi = {10.1002/alz.71574},
pmid = {42265834},
issn = {1552-5279},
support = {2022C03064//the key Research and Development Program of Zhejiang/ ; 2025ZFJH01//the Fundamental Research for the Central Universities/ ; 2022KY067//Medical and Health Science and Technology Project of Zhejiang Province/ ; 82422027//National Natural Science Foundation of China/ ; U24A20340//National Natural Science Foundation of China/ ; },
}

@article {pmid42259992,
year = {2026},
author = {Fan, Y and Gao, L and Lin, Z and Zhang, T and Bai, X and Chen, L},
title = {The effect of brain-computer interface training on cognitive function in stroke patients: a systematic review and meta-analysis.},
journal = {Journal of neurology},
volume = {273},
number = {7},
pages = {},
pmid = {42259992},
issn = {1432-1459},
support = {82471345//National Natural Science Foundation of China/ ; 2024-LCYJ-MS-17//Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University/ ; NDYGN2025005//Aid project ofJiangsu Ningai Medical Development &Medical Aid Foundation/ ; },
abstract = {OBJECTIVE: This study aims to assess the therapeutic impact of BCI-based interventions on global and domain-specific cognitive functions (attention, memory, and executive function), and activities of daily living in stroke survivors. Furthermore, we seek to identify the potential moderating effects of feedback modes and BCI paradigms on the overall rehabilitative efficacy.

METHODS: A systematic search of PubMed, Embase, Web of Science, the Cochrane Library, and CNKI databases was conducted to identify eligible randomized-controlled trials (RCTs). Meta-analyses were performed by pooling standardized mean differences (SMDs) to synthesize effect sizes. To explore sources of heterogeneity and the effects of potential moderators, subgroup analyses were conducted according to outcome measures, stroke phase, BCI paradigm, and feedback type.

RESULTS: Twelve studies were included. The meta-analysis demonstrated that BCI training significantly improved global cognitive function (SMD = 0.62, P < 0.00001), attention, and executive function, alongside enhanced activities of daily living performance. However, no significant improvement was observed in memory function. Subgroup analyses revealed that superior and more robust effects were associated with subacute patients, active BCI paradigms, and multimodal feedback (visual + auditory + proprioceptive).

CONCLUSION: BCI training is an effective intervention for post-stroke cognitive recovery. Early initiation of therapy and the integration of multimodal feedback appear to be critical factors for maximizing therapeutic outcomes.},
}

@article {pmid42260169,
year = {2026},
author = {Su, Z and Gan, KB and Sim, KS},
title = {Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.},
journal = {Annals of biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42260169},
issn = {1573-9686},
support = {FRGS/1/2024/TK07/UKM/02/14//Ministry of Higher Education, Malaysia/ ; },
abstract = {Brain-computer interfaces (BCIs) have emerged as a promising technology with significant potential across various domains in recent years, including healthcare, industry, and entertainment. Among the many BCI paradigms, motor imagery (MI) based on electroencephalography (EEG) is one of the most commonly used and has been widely applied in medical settings. However, due to the inherently low signal-to-noise ratio and non-stationary nature of EEG signals, current decoding accuracy remains suboptimal-particularly in the classification of movements involving the same limb, where finer motion distinctions and higher decoding precision are urgently needed. This review summarizes the research on upper-limb MI-EEG classification and applications over the past 5 years and analyzes the relevant data extracted from the literature. The objective is to provide a comprehensive overview of the current state of research on decoding hand motor imagery from MI-EEG signals and to examine the challenges encountered in practical applications. We systematically investigate state-of-the-art methods, compare their performance and underlying assumptions, and discuss emerging trends and open challenges. Furthermore, we explore how these decoding methods can be translated into real-world applications, highlighting their potential as well as their limitations. The aim of this work is to provide valuable insights and guidance for researchers and developers in the field of EEG-based BCIs.},
}

@article {pmid42250530,
year = {2026},
author = {C, S and C, S and S, IJ},
title = {Efficient FPGA accelerator for low-power high-speed BCI motor imagery classification using novel deep learning.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {203},
number = {},
pages = {109105},
doi = {10.1016/j.neunet.2026.109105},
pmid = {42250530},
issn = {1879-2782},
abstract = {A brain-computer interface (BCI) is an advanced technology that enables direct communication between the human brain and external systems, eliminating the need for intermediaries. Electroencephalography (EEG) is a commonly used signal for developing BCIs. However, EEG signals have challenges such as a poor signal-to-noise ratio, high dimensionality, nonlinearity, and instability. This necessitates the development of an automated system using deep learning (DL) models for motor imagery (MI) classification. Many researchers have worked on MI classification and developed various algorithms; however, several issues remain unsolved, including achieving high accuracy for EEG data across all groups and unseen data, effective feature extraction, and deploying DL models on edge devices with low power consumption and high-speed MI classification. To address these challenges, a novel DL model, Few-Shot Learning (FSL)-Dual Attention-based SqueezeNet, is designed and tested. FSL enables learning MI classification with a small amount of data and improves accuracy on unseen data. SqueezeNet, combined with a Dual Attention Mechanism (DAM), effectively extracts important temporal and frequency features from EEG signals with low computational cost. The proposed network is evaluated on the BCI Competition IV 2a dataset under intra-session, cross-subject, and inter-session settings. It achieves accuracies of 0.9704, 0.8702, and 0.9568, respectively, outperforming well-known DL models such as EfficientNet (0.9426 in intra-session). Further comparisons with existing methods demonstrate competitive and consistent performance across different evaluation protocols. For generalizability analysis, the proposed network is tested on other public datasets. The proposed network achieves an accuracy of over 98% across all datasets, proving its effectiveness for MI classification using EEG signals. Next, a hardware accelerator is designed to deploy the proposed network on edge devices. The hardware is optimized for fast MI detection and low power consumption by employing a dual-core DPU and a dual-buffer scheme. The proposed accelerator's performance and hardware utilization are analyzed. The hardware design consumes only 12.14 W, which is 4.8 and 6.3 times lower than CPU and GPU power consumption, respectively. It performs MI classification in just 5.01 ms, significantly faster than CPU and GPU inference times. The proposed DL network and hardware accelerator demonstrate that the framework is well-suited for real-time deployment in MI classification tasks.},
}

@article {pmid42250823,
year = {2026},
author = {Márton, A and Benke, J and Markus, M and Hoheisel, W and Bartsch, J and Thommes, M},
title = {Promising advancements to the blue dye ingress test - Quantification of blister integrity by leakage rate.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {},
number = {},
pages = {115140},
doi = {10.1016/j.ejpb.2026.115140},
pmid = {42250823},
issn = {1873-3441},
abstract = {The most common packaging type for solid dosage forms is the blister package. The critical quality attribute of blisters is the integrity, which is required to be tested. Hereby it is crucial to develop methodologies representing an improvement compared to the current standard, the blue dye ingress test, regarding sensitivity limits and quantification. In this study, two analytical methods (optical emission spectroscopy and a helium mass spectrometry, which rely on a similar principle), were characterized. For the latter a sample preparation procedure was also developed for filling the blister packages with helium tracer gas. Leaky blister packages were prepared via laser drilling, and the leakage rate was measured. Quantification within the experimental space was found to be feasible using optical emission spectroscopy, and partially feasible using helium mass spectrometry. Furthermore, the repeatability was examined and the measurement results were verified with physical and empirical models describing the molecular flow. In conclusion, the two characterized methods represent promising competition to the established standard test due to quantification. Additionally, the procedures can serve as a sensitive reference method for development as well as production.},
}

@article {pmid42253789,
year = {2026},
author = {Heskebeck, F and Bernhardsson, B and Bergeling, C},
title = {Rotation-based metric on the Riemannian manifold of SPD matrices with applications to source data selection for brain-computer interface transfer learning.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1824613},
pmid = {42253789},
issn = {1662-5161},
abstract = {This paper introduces the pole ratio metric and presents a sphere-based view of symmetric positive-definite matrix rotations on the Riemannian manifold of symmetric positive-definite matrices equipped with the affine-invariant Riemannian metric. The pole ratio quantifies whether data from different users lie on this Riemannian manifold in a way that enables effective transfer learning. The sphere-based view provides insight into the rotational step of transfer learning using the Riemannian Procrustes analysis method and highlights the limitations of rotation. For effective transfer learning, selecting appropriate source data is essential for good performance. The pole ratio is shown to be an effective metric for selecting source data. The main contribution of the paper is the insight into the limitations of rotations on a Riemannian manifold; the usefulness of the pole ratio as a source selection metric is a natural extension of this insight. This paper focuses on Brain-Computer Interfaces (BCIs), but the sphere-based view of rotations of symmetric positive-definite matrix data and the pole ratio are applicable to any field that models two-class data using symmetric positive-definite matrices.},
}

@article {pmid42253794,
year = {2026},
author = {Melby, SR and Asok Kumar, JN and Bigus, ER and Kellis, S},
title = {Clinical evaluation of communication brain computer interfaces in amyotrophic lateral sclerosis: a landscape analysis.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1771146},
pmid = {42253794},
issn = {1662-5161},
abstract = {INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that leads to severe motor impairment, including loss of communication ability, and ultimately death. Communication brain computer interfaces (cBCIs) have the potential to restore communication without reliance on motor function, thereby improving quality of life, independence, and palliative care. However, standardized methods to evaluate cBCI efficacy necessary for clinical implementation are not yet established.

METHODS: We conducted a systematic literature review, semi structured interviews with key opinion leaders (KOLs), and a clinical assessment review panel to (1) identify clinical outcome assessments (COAs) relevant to cBCIs in ALS, (2) obtain expert feedback, and (3) synthesize the current clinical and scientific landscape.

RESULTS: A total of 21 COAs were identified as potentially relevant and may serve as a foundation for cBCI specific measures. However, no existing COA was found to comprehensively capture the clinical benefit or functional impact of cBCIs in ALS.

DISCUSSION: Current COAs are insufficient to evaluate cBCIs in ALS, highlighting a critical gap. Development of cBCI specific outcome measures is needed to support clinical validation, regulatory evaluation, and adoption.},
}

@article {pmid42253796,
year = {2026},
author = {Kojima, S and Kanoh, S},
title = {The ASME-speller: 30-class auditory brain-computer interface speller using stream segregation and the QWERTY layout.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1807535},
pmid = {42253796},
issn = {1662-5161},
abstract = {INTRODUCTION: This study presents the ASME-speller, a novel 30-class auditory brain-computer interface (BCI) speller system that combines auditory stream segregation with the familiar QWERTY keyboard layout to facilitate intuitive and visionfree communication.

METHODS: In the ASME-speller, three distinct auditory streams are presented simultaneously, each corresponding to a row on the QWERTY keyboard. The low-, middle-, and high-frequency streams represent the bottom, middle, and top rows, respectively. Within each stream, alphabet letters and selected symbols are repeatedly presented as spoken voice stimuli. Users are instructed to focus exclusively on the stream corresponding to the row containing the target letter and to selectively attend to that letter within the stream. By leveraging the QWERTY layout and auditory stream segregation, the proposed approach enables users to restrict their attentional focus to a subset of letters by directing selective attention to auditory streams, while the mapping between QWERTY rows and stream pitch facilitates intuitive letter selection. We conducted online experiments with ten healthy participants to evaluate system performance.

RESULTS: The ASME-speller achieved an average classification accuracy of 0.76 and an average information transfer rate (ITR) of 2.16 bits/min. Excluding one participant whose EEG data contained excessive artifacts, these values improved to 0.84 and 2.40 bits/min, respectively. Post-hoc analyses further examined the effects of preprocessing parameters, classification pipelines, and early stopping strategies. Among four pipelines tested, a linear discriminant analysis (LDA) combined with dynamic stopping demonstrated the most robust performance across participants (accuracy of 0.80 and ITR of 4.76 bits/min). For the best participant, a deep learning model (EEGNet4,2) with dynamic stopping achieved accuracy of 1.0 with ITR of 14.44 bits/min.

DISCUSSION: Compared to previous auditory BCI spellers, the ASME-speller demonstrates performance comparable to existing systems, while offering advantages in terms of simplicity, requiring only standard headphones and no visual support. These findings demonstrate the feasibility of the ASME-speller and pave the way toward practical auditory BCI applications for communication.},
}

@article {pmid42256276,
year = {2026},
author = {Yu, M and Guo, T and Han, S and Xue, N and Yang, W and Huang, J and Chen, H and He, C and Ding, J and Xia, L},
title = {Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.},
journal = {iScience},
volume = {29},
number = {6},
pages = {115785},
pmid = {42256276},
issn = {2589-0042},
abstract = {Obtaining sufficient electroencephalography (EEG) signals for training deep neural networks (DNNs) in brain-computer interfaces (BCIs) is challenging due to individual differences in neural activity, which require large per-participant data to map signals to actions, while factors like movement artifacts often limit data collection. Existing advances mainly leverage generative models with various regularizers to produce sufficient EEG signals. However, selecting appropriate regularizers remains challenging. Inspired by metacognition, the human cognitive process that monitors and regulates learning and decision-making, we propose a metacognitive regulation module including three regularizers that explicitly capture EEG temporal dynamics and functional resolution, thereby improving both the diversity and similarity of generated data. Through extensive theoretical and empirical validation on two datasets, we demonstrate that our module: (1) significantly improves generative models for generating highly complex, realistic EEG activity; (2) improves generalization across different generative models; and (3) endows DNN models with enhanced human-like decision-making and adaptation capabilities.},
}

@article {pmid42256671,
year = {2026},
author = {Enériz, D and Antolín, D and Medrano, N and Calvo, B},
title = {Reproducible testing for embedded BCIs: a demultiplexing PCB and acquisition system for EEG signal emulation.},
journal = {HardwareX},
volume = {26},
number = {},
pages = {e00800},
pmid = {42256671},
issn = {2468-0672},
abstract = {Validating machine learning models for Brain-Computer Interfaces (BCIs) on resource-constrained edge devices is challenging, as traditional methods rely on costly EEG equipment or simulations that fail to capture real-world electronic characteristics. To bridge this gap, we introduce the DEEGMUX, a low-cost, open-source hardware system for high-fidelity, hardware-in-the-loop (HIL) testing of EEG classification algorithms. The system comprises an EEG Demultiplexer Board that converts a multiplexed EEG signal into 8 parallel channels, and an EEG Acquisition and Processing Board featuring an ADS1299 24-bit ADC interfaced with an Arduino Nano 33 BLE. This setup enables the use of real EEG datasets, such as the PhysioNet Motor Imagery dataset, to generate precisely timed electronic signals. Characterization demonstrated high signal fidelity, with a Mean Squared Error of 1.7·10[-10] V[2] and a Signal-to-Noise Ratio of 16 dB relative to the original digital data. Furthermore, an EEGNet motor imagery classifier evaluated on hardware-acquired signals showed a negligible accuracy difference of (-0.3 ± 5)% compared to evaluation on the original data, confirming that the emulation chain preserves classification-relevant features. The DEEGMUX provides a scalable, reproducible, and affordable platform for rigorously testing edge-deployed CNN models against realistic electronic inputs, accelerating the transition from simulation to robust real-world BCI deployment.},
}

@article {pmid42256857,
year = {2026},
author = {Evetović, N and Rosipal, R and Polyanskaya, A and Rošťáková, Z and Dvornák, K and Vankó, M and Korečko, Š and Trejo, LJ},
title = {EEG-based monitoring of mental fatigue during virtual-reality motor imagery tasks.},
journal = {Frontiers in behavioral neuroscience},
volume = {20},
number = {},
pages = {1810723},
pmid = {42256857},
issn = {1662-5153},
abstract = {Prolonged motor-imagery training in immersive virtual-reality environments can induce mental fatigue, reducing engagement and potentially limiting the effectiveness of neurorehabilitation. This study investigated neural markers of mental fatigue by recording electroencephalography (EEG) from healthy participants during extended motor-imagery and control sessions in a head-mounted display setup. Multidimensional analysis was applied to extract spectral, spatial, and temporal features while using a novel deflation step for removing task-related motor components to isolate fatigue-specific activity. Evidence of mental fatigue was consistently seen in parieto-occipital alpha-band modulation, with increases in alpha power corresponding to subjective reports and EEG-based measures of mental fatigue. The derived models were robust to common EEG artifacts and demonstrated consistent fatigue estimation across tasks and sessions. These findings suggest that individualized neural markers can enable real-time monitoring of fatigue (with an accuracy of 83.49 ± 6.34%), allowing adaptive adjustments of task difficulty or pacing in brain-computer interface systems. This work advances understanding of the neurophysiological dynamics of mental fatigue during immersive motor-imagery tasks and provides a foundation for designing more effective, personalized neurorehabilitation protocols.},
}

@article {pmid42258675,
year = {2026},
author = {Wang, Z and He, X and Wang, H and Wu, D},
title = {CKD: Contrastive Knowledge Distillation for Cross-Dataset EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3701548},
pmid = {42258675},
issn = {1558-2531},
abstract = {OBJECTIVE: Cross-dataset transfer in electroencephalography (EEG)-based brain-computer interfaces (BCIs) remains challenging due to substantial distribution shifts across datasets, including differences in subjects, acquisition devices, and recording protocols. This study aims to improve cross-dataset EEG decoding by enhancing knowledge transfer beyond conventional output-level distillation.

METHODS: We propose a contrastive knowledge distillation (CKD) framework for cross-dataset EEG classification. CKD follows a two-stage transfer strategy, consisting of cross-dataset teacher pretraining and cross-subject online adaptation, and jointly exploits logit-level distillation and feature-level contrastive alignment. In this way, the student model is encouraged to inherit both the predictive behavior and representation structure of the teacher.

RESULTS: Experiments on five motor imagery EEG datasets showed that CKD consistently outperformed twelve conventional training, representative knowledge distillation, and domain adaptation baselines under both single-source and multi-source transfer settings. Additional visualizations and quantitative analyses further confirmed that CKD improves teacher-student alignment in terms of feature geometry and distribution consistency, and can be further enhanced by explicit domain adaptation.

CONCLUSION: The proposed CKD provides an effective solution for cross-dataset EEG decoding by jointly improving predictive knowledge transfer and latent feature alignment under severe dataset shifts.

SIGNIFICANCE: This work improves the robustness and generalizability of EEG decoding across heterogeneous datasets, which is important for practical BCI deployment under real-world acquisition conditions.},
}

@article {pmid42259065,
year = {2026},
author = {Motegi, M and Chikamatsu, K},
title = {Management of conductive and mixed hearing loss intolerant to air-conduction hearing aids: A stepwise algorithm and narrative review from a Japanese perspective.},
journal = {Auris, nasus, larynx},
volume = {53},
number = {4},
pages = {567-578},
doi = {10.1016/j.anl.2026.05.001},
pmid = {42259065},
issn = {1879-1476},
abstract = {OBJECTIVE: Conductive and mixed hearing loss in the complicated ear, including chronically inflamed ears with recurrent otorrhea, postoperative cavities, tympanic membrane lateralization, canal stenosis/atresia, and congenital malformations, remains a frequent, consequential problem in cases where conventional air-conduction hearing aids (ACHAs) are unusable or provide insufficient functional benefits. The expansion of the therapeutic landscape from non-implantable (e.g., cartilage conduction hearing aids [CCHA] and adhesive bone-conduction systems) to implantable (e.g., bone-conduction implants [BCIs] and active middle ear implants [Vibrant Soundbridge[®︎,] VSB]) options has not only increased opportunities for personalized rehabilitation but also created a practical "paradox of choice." Japan provides a distinctive clinical context because major implantable auditory devices are reimbursed under defined indications, whereas access to non-implantable options frequently depends on out-of-pocket purchases and/or subsidy programs.

METHOD: This Japan-based narrative review synthesized peer-reviewed evidence and integrated the domestic indication framework to propose a pragmatic, stepwise device-selection algorithm for complicated ears with conductive or mixed hearing loss. Step 1 comprises a Gatekeeper Trial using a non-surgical option (e.g., CCHA/adhesive systems or headband/soft band stimulation) to confirm real-world benefits, identify coupling-related limitations, and provide counseling. Step 2 categorizes the cochlear reserve into zones A, B, or C based on bone-conduction thresholds to align the device output capacity with the inner-ear reserve; this step also incorporates Japan-aligned indications and a high-frequency "B-C border" flag (e.g., >65 dB HL at high frequencies) that can shift the balance between BCI and VSB. Step 3 applies clinically decisive modifiers: ear status and infection-control strategy, imaging-based surgical feasibility, high-frequency listening demands, and patient priorities, such as cosmesis, skin tolerance, maintenance burden, and MRI considerations.

RESULTS: Asymmetric hearing loss is managed as a dedicated differentiator. When appropriate, BCI-mediated transcranial stimulation can add useful contralateral cochlear access to improve speech perception in relevant spatial noise configurations, whereas counseling emphasizes situation-dependent benefits and limited binaural restoration.

CONCLUSION: Finally, we introduce the concept of Device Readiness Surgery, reframing otologic surgery as a staged effort to achieve a safe, dry, and stable ear that enables ACHA use whenever realistic; when ACHA remains ineffective, the ear is optimized for the selected device. This review provides a clinically oriented roadmap to improve the consistency of counseling and device selection in complicated ears and highlights the priorities for prospective validation and comparative-effectiveness research.},
}

@article {pmid42259101,
year = {2026},
author = {Ghafoori, S and Cetera, A and Rabiee, A and Farhadi, MH and Singh, R and Furmanek, M and Shahriari, Y and Abiri, R},
title = {Cross-frequency bispectral EEG analysis of reach-to-grasp planning and execution.},
journal = {Computers in biology and medicine},
volume = {213},
number = {},
pages = {111791},
doi = {10.1016/j.compbiomed.2026.111791},
pmid = {42259101},
issn = {1879-0534},
abstract = {Neural motor control of reach-to-grasp emerges from complex, nonlinear interactions across multiple brain cortices. However, most electroencephalography (EEG)-based motor analysis has largely relied on linear and second-order spectral measures. Here, we investigate whether higher-order cross-frequency dynamics encode meaningful distinctions between motor planning and execution during natural reach-to-grasp movements. Using a cue-based experimental paradigm, EEG was recorded during precision and power plan-to-grasp tasks, enabling stage-resolved analysis of grasp planning and execution-related neural activity. Cross-frequency bispectral analysis was applied to compute complex bicoherence matrices across canonical frequency band pairs, from which magnitude- and phase-based features were extracted. Classification, permutation-based feature selection, and within-subject statistical testing revealed that execution is associated with stronger nonlinear coupling than planning, with dominant contributions from β- and γ-driven interactions. In contrast, decoding of precision versus power grasps showed similar performance across stages, indicating that grasp-type representations emerge during planning and persist into execution. Exploratory single-feature analyses further identified focal, stage-dependent modulation of nonlinear coupling in central motor regions. Informative bispectral features reflected coordinated activity across prefrontal, central, and occipital areas, while feature redundancy enabled dimensionality reduction without loss of performance. Compared with the conventional analytical methods as baselines, bispectral features provided consistent advantages for grasp-type discrimination and multiclass classification, highlighting the value of nonlinear cross-frequency analysis. In summary, our results extend bispectral analysis to clinically relevant grasping tasks and highlight nonlinear cross-frequency coupling as an informative marker of motor stages offering a foundation for future BCI and neuroprosthetic research.},
}

@article {pmid42259478,
year = {2026},
author = {Chen, Y and Li, Z and Wang, Y and Li, Y and Zheng, J and Yang, H and Liu, M and Cukur, T and Fan, Q and Li, Z and Lu, J and Tian, Q},
title = {MicroKAN: Mapping Human Brain Microstructure Using Diffusion MRI and Adaptive Nonlinear Modeling.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122032},
doi = {10.1016/j.neuroimage.2026.122032},
pmid = {42259478},
issn = {1095-9572},
abstract = {Diffusion magnetic resonance imaging (dMRI) provides powerful insights into brain microstructure, but conventional microstructural modeling methods require long acquisition times for covering sufficient diffusion directions and are computationally intensive. While deep learning has shown promise in reducing the direction requirement and accelerating the modeling, traditional architectures such as CNNs often struggle to capture the highly nonlinear relationships between multi-shell diffusion signals and microstructural properties. We present MicroKAN, a novel framework built upon Kolmogorov-Arnold Networks with adaptive spline-based activations, specifically designed to represent complex biophysical models with enhanced flexibility and efficiency. MicroKAN supports both supervised and self-supervised paradigms: the supervised variant learns mappings from data to reference metrics, while the self-supervised variant estimates model parameters directly by reconstructing signals through the forward diffusion process, eliminating the need for ground-truth labels. Evaluated on diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) across multiple datasets, MicroKAN substantially accelerates acquisition and improves the fidelity of microstructural parameter estimation. Beyond supervised training, its self-supervised formulation shows strong robustness to distribution shifts, enabling reliable performance even without annotations. Furthermore, transfer learning with minimal labeled data preserves high accuracy, underscoring the framework's adaptability to diverse scenarios. These advances establish MicroKAN as a versatile and efficient tool for dMRI analysis, offering new opportunities to accelerate neuroscience research and expand the clinical utility of microstructural imaging. Our source code is available at https://github.com/JustlfC03/MicroKAN.},
}

@article {pmid42246084,
year = {2026},
author = {Fischer, T and Middell, E and Moradi, S and von Lühmann, A},
title = {fNIRS Single-trial decoding improves systematically with higher optode density, model-based noise regression, and image reconstruction.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae782f},
pmid = {42246084},
issn = {1741-2552},
abstract = {Advances in high-density diffuse optical tomography (HD-DOT) promise to overcome long-standing performance limitations in classification of sparse functional Near-Infrared Spectroscopy (fNIRS) signals, but their combined impact on single-trial brain decoding and generalization remains largely unquantified. Here, we systematically evaluate how probe density, physiology removal via short-separation (SS) regression within a general linear model (GLM), and image-space feature representations aligned with brain parcellation schemes shape single-trial decoding accuracy. To enable a structured investigation and validation via realistic ground truth data, we introduce a flexible, easy-to-use framework that allows users to augment their own channel space resting-state fNIRS data with configurable synthetic hemodynamic response functions (HRFs) on target areas of the brain, using a state-of-the art diffuse optical forward model. Using three high-density fNIRS datasets -a whole-head resting-state recording augmented with synthetic HRFs and two motor ball-squeezing datasets -we derive sparse-to-HD optode subsets, integrate GLM-based SS regression into cross-validation, and compare channel-space and parcel-space features derived from HD-DOT image reconstructions. High-density configurations consistently and significantly improve classification accuracy and robustness to focal activations. SS correction yields systematic gains of 4 percentage points in within-subject decoding and more than 10 percentage points in cross-dataset transfer. Parcel-space features outperform channel-space features at matched dimensionality, enabling robust leave-one-subject-out decoding (mean accuracy 79%) and cross-dataset generalization across different probe layouts (72% with SS correction). All methodology is implemented and available in the open-source Cedalion framework. Together, these results demonstrate that HD-DOT, GLM-based SS regression, and parcel-space representations jointly enable significantly more accurate, robust, and probe-independent fNIRS classification pipelines.},
}

@article {pmid42246470,
year = {2026},
author = {Grill, WM and Chestek, CA and Wang, Y and Aberra, AS and Destexhe, A and Chan, RHM and Lu, BL and Hanein, Y and Robinson, JT and Whitmire, C and Song, AW and Farina, D and Ferris, DP and Green, R and Goding, J and Asplund, M},
title = {A Roadmap to Navigate the Future of Neural Engineering.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae78c2},
pmid = {42246470},
issn = {1741-2552},
abstract = {A group of leaders in neural engineering collaborated to develop a roadmap to navigate the future of neural engineering. We covered a range of themes, including brain machine interfaces, neural modelling, artificial intelligence and machine learning, neural interfaces, neural imaging, augmented rehabilitation, and neuromaterials. For each topic we reviewed the current status, identified current and future challenges, and speculated on the emerging and necessary advances in science and technology to meet these challenges. Neural engineering will continue to yield the approaches and insights that advance the diagnosis and treatment of nervous system disorders, as well as provide new understanding of neural function. .},
}

@article {pmid42243238,
year = {2026},
author = {Geng, X and Xiong, Z and Yu, P and Dai, T and Wu, S and Wang, H},
title = {ZMW-RSVP: a time-frequency prior-guided normalization-free RSVP-BCI decoding model.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-56317-8},
pmid = {42243238},
issn = {2045-2322},
support = {No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; },
abstract = {Rapid Serial Visual Presentation (RSVP)-based brain-computer interfaces (BCIs) are valuable for target detection and rehabilitation because they require no motor involvement and can elicit reliable event-related potentials (ERP). However, single-trial EEG decoding remains challenging due to low signal-to-noise ratio and substantial inter-subject variability. To address the limitations of conventional approaches that rely on handcrafted features and to further explore internal feature transformation in single-trial ERP decoding, this paper proposes an RSVP-BCI decoding model, termed ZMW-RSVP, by extending a time-frequency Transformer framework with oscillatory gated attention and a normalization-free dynamic activation network. The proposed model incorporates neuroscience-inspired priors through a multi-band oscillatory gating mechanism, with an emphasis on theta-related activity associated with RSVP/P300 processing, and further guides attention allocation via a time-window bias. In addition, the model replaces internal LayerNorm modules with Dynamic Tanh, which provides an element-wise internal transformation without explicit mean-variance normalization and is evaluated as a task-related alternative for ERP-related discriminative feature modeling. Experiments on the Tsinghua RSVP dataset and the NeuBCI Target Retrieval RSVP-EEG dataset demonstrate that ZMW-RSVP achieves improved classification performance under the evaluated cross-subject settings, validating the effectiveness of the proposed approach for single-trial RSVP decoding.},
}

@article {pmid42243338,
year = {2026},
author = {Ga, YJ and Yeh, JY},
title = {Eradication of Mycoplasma contamination in HeLa cells using neomycin resistance gene introduction and aminoglycoside G418 (Geneticin) treatment.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-55513-w},
pmid = {42243338},
issn = {2045-2322},
support = {RS-2025-02307583//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry/ ; },
abstract = {Mycoplasma contamination remains a persistent problem in continuous cell culture, compromising cellular physiology, altering gene expression, and potentially leading to erroneous experimental conclusions. Mycoplasmas can profoundly affect cultured cells, underscoring the need for efficient eradication strategies. Here, we developed a simple and effective method to eliminate Mycoplasma from HeLa cell lines. A neomycin resistance gene was introduced into Mycoplasma-contaminated cells, conferring resistance to aminoglycoside-induced cytotoxicity. Subsequently, a high concentration of the neomycin analogue G418 (Geneticin), combined with single-cell cloning, was applied to achieve complete removal of the Mycoplasma contamination. Mycoplasma presence and clearance were confirmed by PCR targeting the 16 S rRNA gene and immunofluorescence using a Mycoplasma-specific monoclonal antibody. This genetic-antibiotic combination proved technically simple and highly effective for long-term Mycoplasma eradication in continuous cell lines. To investigate the functional impact of Mycoplasma contamination, we compared protein expression following transient transfection with GFP and FAM-labeled reporters in Mycoplasma-eradicated versus Mycoplasma-contaminated cells. Fluorescence analysis revealed a marked increase in transfection efficiency and reporter expression in Mycoplasma-eradicated cells. Our findings provide an effective strategy for eliminating Mycoplasma contamination in cell line cultures, ensuring the reliability of cell-based research and the accuracy of experimental data.},
}

@article {pmid42245765,
year = {2026},
author = {Shivakumar, D and Gupta, CN and Hazra, B},
title = {Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.},
journal = {Frontiers in neuroinformatics},
volume = {20},
number = {},
pages = {1823408},
pmid = {42245765},
issn = {1662-5196},
abstract = {Understanding the temporal organization of brain activity requires methods that capture scale-free dynamics while accounting for the high-dimensional, spatially correlated nature of the electroencephalogram (EEG) data. We propose a novel framework that integrates nonlinear manifold learning (Isometric mapping) with detrended fluctuation analysis (DFA) to quantify long-range temporal correlations (LRTC) in the alpha-band of EEG signals. We applied this framework to two music related EEG datasets, as music is known to evoke different emotions and synchronize brain activity. The first dataset was obtained during live Indian classical music (ICM) listening that included two ragas, Yaman and Puriya Dhanashree. EEG was recorded from 13 healthy volunteers (24 channels, sampled at 500 Hz). The second dataset is the Music BCI dataset (006-2015), which includes Jazz and Synth-pop musical clips, with EEG collected from 11 subjects (64 channels, downsampled to 200 Hz). The EEG data from both datasets were preprocessed, band-limited to 8-13 Hz, and segmented into non-overlapping 2-s windows. Alpha-band power was extracted from each channel to form the feature matrix used for embedding. For the ICM dataset, Isometric mapping (Isomap) produced a low-dimensional representation (d = 3), which we analyzed using two approaches: (i) a norm-based approach and (ii) a mean-based approach. For comparison, an equivalent PCA-based pipeline (d = 5) was implemented. The Isomap mean-based DFA yielded consistent scaling exponents (α) in the range of 0.66-0.70, with higher goodness-of-fit (R [2]) and narrower bootstrap confidence intervals than the norm-based approach. PCA produced similar trends but required more dimensions. Paired t-tests showed that the Isomap mean-based approach detected music-related changes more sensitively than PCA (Yaman p = 0.02; Puriya Dhanashree p = 0.008). Comparable results were also observed for the second Music BCI dataset, where Isomap achieved a compact representation with d = 5, compared to d = 8 for PCA. In this dataset as well, the mean-based DFA yielded α values in the range of 0.62-0.65 and higher goodness-of-fit. Overall, the results suggest that combining nonlinear manifold embeddings with mean-based DFA provides a compact and robust framework for characterizing scale-free temporal structure in EEG data.},
}

@article {pmid41963527,
year = {2026},
author = {Gayathri, T and Manjula, G and Kenchannavar, HH and Sudha, D and Jankatti, SK and Kaur, R and Venkateswarlu, B},
title = {QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {41963527},
issn = {2045-2322},
abstract = {Electroencephalography (EEG) is a non-invasive, high-temporal-resolution method for diagnosing and monitoring neurological disorders. Deep learning has recently substantially enhanced the state of the art for automated EEG analysis. However, many of the currently applied paradigms are still challenged by limited generalisation across datasets, vulnerability to noise or preprocessing changes, and the absence of interpretable decision rules. Additionally, many deep learning models operate like black boxes, which limits their use in clinical settings where interpretability and trust are key. Although the potential of quantum-inspired learning has recently been demonstrated through improved feature separability in high-dimensional signal spaces, its scope of applicability does not yet extend to deep temporal modelling and explainable artificial intelligence applications. We address these limitations by introducing QuantumNeuroXAI, a quantum-inspired deep learning framework implemented on classical hardware that leverages structured feature encoding inspired by quantum neural networks to provide inherent explainability for EEG-based diagnosis of neurological disorders. This framework hybridises quantum-inspired feature encoding with a deep-learning architecture that blends temporal convolutional and attention-based recurrent modelling to capture local and long-range patterns of dependencies in EEG signals. The framework incorporates a multi-level explainability module relevant at the signal, model, and quantum-representation levels, allowing predictions to be interpreted in a clinically meaningful and transparent fashion. We conduct extensive experiments on three publicly available EEG datasets (TUH EEG, CHB-MIT, and BCI Competition IV-2a) to evaluate the proposed framework. These quantitative results show that QuantumNeuroXAI achieves statistically significant and large effect sizes, with macro-F1 improvements of up to 5.2% over classical machine learning, deep learning, and hybrid baseline models. Additional robustness and scalability analyses further validate stable performance against dataset shift and across various preprocessing configurations. In summary, QuantumNeuroXAI is an interpretable and reproducible solution for EEG-based neurological analysis, demonstrating promise for clinical decision support and future scalability to multimodal brain signal applications. It is important to note that the proposed framework does not rely on quantum hardware and is fully implemented using classical computational resources. The implementation of the proposed framework is publicly available at: https://github.com/venkateshwarlu-bondu/QuantumNeuroXAI.},
}

@article {pmid41968126,
year = {2026},
author = {Kuroda, N and Sato, Y and Harada, S and Teraoka, R and Teramoto, W},
title = {Bayesian causal inference reveals declined proprioception, increased integration bias underlie older adults' stronger visual bias in hand position perception.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {41968126},
issn = {2045-2322},
support = {23K18980//Japan Society for the Promotion of Science/ ; 20H05801 and 23H00076//Japan Society for the Promotion of Science/ ; },
abstract = {UNLABELLED: Self-localization is fundamental to bodily self-consciousness across the lifespan. Humans estimate body-part position by integrating afferent signals such as vision and proprioception. Rubber and mirror hand illusions highlight the dominant role of vision in hand position perception. Although older adults rely more heavily on visual information, the computational mechanisms underlying age-related increases in visual bias remain unclear. Here, we examined age-related changes in visuo-proprioceptive integration using a Bayesian causal inference (BCI) model. Two experiments introduced spatial discrepancies between visual and proprioceptive hand positions to manipulate the likelihood of integration. Participants reached toward a target after the visual hand disappeared, allowing the BCI model to estimate sensory reliabilities and the prior probability of a common cause ([Formula: see text]). Decision-making strategies were also compared within the BCI framework. Older adults exhibited reduced proprioceptive reliability and a higher [Formula: see text], indicating a stronger tendency to infer a shared source for visual and proprioceptive signals. No age-related differences were observed in decision-making strategy. These findings suggest that age-related visual bias reflects changes not only in sensory reliability but also in causal inference during multisensory integration.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-026-45797-3.},
}