<![CDATA[Motor imagery (MI) electroencephalography (EEG) classification remains difficult in cross-subject settings because signals vary substantially between individuals. This study compares a compact deep learning model (EEGNet) with a classical Common Spatial Pattern and Linear Discriminant Analysis pipeline (CSP+LDA) for hands-versus-feet MI classification. Data came from the EEG Motor Movement/Imagery Dataset (EEGMMIDB) on PhysioNet. A leave-one-subject-out (LOSO) evaluation was conducted across 109 subjects using runs 6, 10, and 14. Preprocessing applied a 7-30 Hz band-pass filter, a 1-2 s post-cue window, and subject-wise z-score normalization. Because normalization used unlabeled statistics from each held-out subject, the setting is LOSO with unsupervised test-time normalization rather than a fully inductive calibration-free protocol. EEGNet reached a mean balanced accuracy of 0.660 ± 0.136 (macro-F1 0.640 ± 0.147; Cohen’s kappa 0.312 ± 0.271), whereas CSP+LDA reached 0.635 ± 0.141 (macro-F1 0.605 ± 0.164; kappa 0.270 ± 0.281). A descriptive paired comparison showed that EEGNet scored higher for 60 of 109 subjects, CSP+LDA scored higher for 47 subjects, and 2 subjects were tied. The aggregated EEGNet confusion matrix showed higher recall for hands than for feet (0.735 vs 0.574), and balanced accuracy varied widely across subjects. These results provide a reproducible subject-independent comparison on EEGMMIDB and show that inter-subject variability remains a major barrier to robust MI decoding. The reproducible pipeline also offers a practical neuroinformatics case for teaching biosignal preprocessing, leakage-aware validation, and model comparison, while the subject-level variability shows what must be addressed before assistive educational deployment.]]>
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