2025/12/27 by Samanta, Argha Kamal, Mewada, Deepak, Sarma, Monalisa +1
Medicine · Neuroscience · #EEG and Brain-Computer Interfaces #Electroencephalography #Epilepsy research and treatment #FOS: Computer and information sciences #Feature (linguistics) #Feature selection #Functional Brain Connectivity Studies #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Neurocognitive #Neuroimaging #Pattern recognition (psychology) #Recall #Set (abstract data type)
paper · open access · doi:10.48550/arxiv.2512.22656
published in arXiv (Cornell University) (Cornell University)
Clinical electroencephalography is routinely used to evaluate patients with diverse and often overlapping neurological conditions, yet interpretation remains manual, time-intensive, and variable across experts. While automated EEG analysis has been widely studied, most existing methods target isolated diagnostic problems, particularly seizure detection, and provide limited support for multi-disorder clinical screening. This study examines automated EEG-based classification across eleven clinically relevant neurological disorder categories, encompassing acute time-critical conditions, chronic neurocognitive and developmental disorders, and disorders with indirect or weak electrophysiological signatures. EEG recordings are processed using a standard longitudinal bipolar montage and represented through a multi-domain feature set capturing temporal statistics, spectral structure, signal complexity, and inter-channel relationships. Disorder-aware machine learning models are trained under severe class imbalance, with decision thresholds explicitly calibrated to prioritize diagnostic sensitivity. Evaluation on a large, heterogeneous clinical EEG dataset demonstrates that sensitivity-oriented modeling achieves recall exceeding 80% for the majority of disorder categories, with several low-prevalence conditions showing absolute recall gains of 15-30% after threshold calibration compared to default operating points. Feature importance analysis reveals physiologically plausible patterns consistent with established clinical EEG markers. These results establish realistic performance baselines for multi-disorder EEG classification and provide quantitative evidence that sensitivity-prioritized automated analysis can support scalable EEG screening and triage in real-world clinical settings.