Deep Learning pipeline for motor-imagery classification.
Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals
A compact 8‑channel EEG method for depression screening is evaluated strictly at the subject level, revealing that true accuracy is much lower than segment‑level reports.
End-to-end EEG motor imagery classification on 109 PhysioNet subjects. Classical ML + deep learning baselines under honest cross-subject validation. Best: 0.802 balanced accuracy with ShallowConvNet.
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