# EEG-Based Fatigue Detection

EEG fatigue detection has treated multi-band modeling and temporal weighting as separate concerns, without defining how the spectral structure inside a window should shape that window's contribution. LBSNet encodes each window's relative cross-band activity as a normalized descriptor, softly matches it to learnable spectral prototypes, and combines the assignments with prototype importance to weight power aggregation. In subject-independent experiments on SEED-VIG and SADT it beats seven baselines on accuracy and F1 while using the fewest parameters and multiply-accumulate operations among the compared models.

EEG fatigue detection has treated multi-band modeling and temporal weighting as separate concerns, without defining how the spectral structure inside a window should shape that window's contribution. LBSNet encodes each window's relative cross-band activity as a normalized descriptor, softly matches it to learnable spectral prototypes, and combines the assignments with prototype importance to weight power aggregation. In subject-independent experiments on SEED-VIG and SADT it beats seven baselines on accuracy and F1 while using the fewest parameters and multiply-accumulate operations among the compared models.