International IEEE/EMBS Conference on Neural Engineering : [proceedings]

Objective:Reliable detection of high-frequency oscillations (HFOs) in intracranial EEG (iEEG) is critical for localizing the seizure onset zone (SOZ). However, artifact contamination severely impairs detection specificity and contributes to false positives. Methods:We developed a reliability guided HFO analysis pipeline that targets artifact elimination through a multi-level strategy, including event level denoising, channel signal quality assessment, and segment level temporal reliability analysis. The classifier was trained on 22 subjects using manual annotations of reliable versus unreliable data and validated using leave-one-subject-out cross-validation (LOSO-CV). Results:The final pipeline was further applied to an additional cohort of 8 independent subjects to evaluate generalizability and clinical utility. SOZ localization accuracy based on HFOs retained after event-level denoising improved from 66% (baseline) to 82%, and further to 93% after applying channel and segment reliability filters. ROC analysis confirmed that reliability-based filtering enhanced separation between SOZ and non-SOZ channels, improving clinical specificity. Conclusion:Incorporating spatial and temporal contextual information, specifically by evaluating signal quality across neighboring channels and consistency across adjacent time windows, significantly enhances the ability to distinguish true HFOs from artifacts. This approach improves both the robustness and clinical accuracy of HFO-based SOZ localization.

Stroke often causes damage to the motor descending pathways in the lesioned hemisphere, resulting in compensatory recruitment of contralesional motor resources. This proof-of-concept study examines how motor task demand affects brain-muscle communication in individuals with chronic stroke, which represents motor system remodeling after the injury. Coherence between EEG and deltoid EMG was estimated during an isometric shoulder abduction task, and hemisphere dominance was evaluated using a Laterality Index (LI) in five stroke survivors and five healthy controls during isometric shoulder abduction at 20% and 40% of maximal voluntary contraction. Compared to controls, stroke participants exhibited a significantly reduced beta-band corticomuscular connectivity (CMC) at the lesioned hemisphere, with enhanced contralesional CMC. This was accompanied by an increase in motor demand, which amplified contralesional cortical involvement and revealed a pronounced shift in the Laterality Index (LI) toward contralesional dominance. This shift is indicative of maladaptive neural reorganization and diminished reliance on the ipsilesional hemisphere. The potential of beta-band CMC and LI as sensitive neurophysiological biomarkers for evaluating motor impairment and guiding individualized neurorehabilitation strategies that account for cortical plasticity and load-dependent recruitment patterns in stroke recovery is underscored by these task-dependent alterations in coherence and hemispheric dominance.

High-frequency oscillations (HFOs) are promising biomarkers for localizing the seizure onset zone (SOZ) in drug-resistant epilepsy. Automatic HFO detectors often depend on specific parameter choices, limiting effectiveness across diverse datasets. This study optimizes a published amplitude-threshold-based detector using a data-driven approach on intracranial EEG recordings from 20 patients at four epilepsy centers. We use the Tree-structured Parzen Estimator (TPE) to efficiently search the parameter space, guided by a new objective function, the SOZ Detection Quality (SDQ) score, which balances SOZ localization precision with the number of detected events in the SOZ. In the training cohort, the optimized detector increased the proportion of detections in SOZ channels from 49% to 61% and improved AUROC from 0.78 to 0.89 while largely preserving the SOZ detection counts. Similar improvements were observed in the test cohort, where detections in SOZ rose from 45% to 62%, and AUROC increased from 0.92 to 0.95. In both cohorts, detections outside the SOZ were significantly reduced, enhancing spatial specificity without compromising sensitivity. Theoretically, our TPE-based optimization achieved over 2,958×speedup over a grid search. These results show an effective data-driven parameter tuning approach that improves HFO biomarker reliability and supports their use in clinical planning for epilepsy surgery.

Deep learning has the potential for advancing EEG analysis and interpretation, however widespread implementation has been constrained by the need for extensive labeled datasets and limited transparency. In contrast, generative pretrained transformer (GPT) architectures have enabled efficient learning of small datasets through fine-tuning and also offer verifiable chain-of-thought reasoning in agentic frameworks. Here, we present EEG-GPT, a framework for EEG classification fine-tuned on publicly available large-language models. We benchmark EEG-GPT on the Temple University Hospital (TUH) EEG corpus in a few-shot regime. Utilizing only 2 % of the training data, EEG-GPT classifies normal versus abnormal EEG recordings with AUROC of 0.86, comparable to state-of-the-art deep learning methods. In addition, EEG-GPT demonstrates the capability to be used as an artificial intelligence (AI) agent, orchestrating usage of specialized EEG tools while providing step-by-step verifiability of its reasoning steps. These results underscore GPTs potential to advance EEG analysis and interpretation, highlighting their efficient learning in small data regimes and their potential to automate EEG analysis in a verifiable manner.

Proficiency in microanastomosis is a fundamental competency across multiple microsurgical disciplines. These procedures demand exceptional precision and refined technical skills, making effective, standardized assessment methods essential. Traditionally, the evaluation of microsurgical techniques has relied heavily on the subjective judgment of expert raters. They are inherently constrained by limitations such as inter-rater variability, lack of standardized evaluation criteria, susceptibility to cognitive bias, and the time-intensive nature of manual review. These shortcomings underscore the urgent need for an objective, reliable, and automated system capable of assessing microsurgical performance with consistency and scalability. To bridge this gap, we propose a novel AI framework for the automated assessment of microanastomosis instrument handling skills. The system integrates four core components: (1) an instrument detection module based on the You Only Look Once (YOLO) architecture; (2) an instrument tracking module developed from Deep Simple Online and Realtime Tracking (DeepSORT); (3) an instrument tip localization module employing shape descriptors; and (4) a supervised classification module trained on expert-labeled data to evaluate instrument handling proficiency. Experimental results demonstrate the effectiveness of the framework, achieving an instrument detection precision of 97