Multimodal ML

EEG Sleep Deprivation Classification

EEG representations & sleepiness prediction

Learns EEG representations for sleep-deprivation classification and transfers them to session-level sleepiness prediction.

8,300resting-state EEG epochs

0.692held-out Pearson correlation

5-foldsubject-wise cross-validation

What I built

Built an end-to-end EEG deep learning framework on 8,300 resting-state EEG epochs to classify normal sleep vs. sleep deprivation and predict session-level SSS/KSS sleepiness scores.
Designed leakage-safe 70/15/15 subject-wise splits with 5,738 train, 1,246 validation, and 1,316 test samples, ensuring no subject overlap across train, validation, and test sets.
Demonstrated transfer of learned EEG representations to downstream sleepiness prediction, achieving 0.692 Pearson and 0.738 Spearman correlation on held-out subjects using feature-based KSS modeling with leakage-safe subject-wise splits.
Benchmarked 4 EEG representation learners—Residual EEG CNN, 2-Branch EEG CNN, DeiT-Tiny, and ViT-Small—and reused the best encoder for downstream sleepiness modeling.
Implemented 5-fold subject-wise cross-validation to evaluate generalization across held-out subjects and prevent identity leakage in EEG experiments.
Built downstream prediction pipelines using pretrained EEG embeddings with Ridge/MLP/GRU/LSTM models, direct ordinal prediction, and multi-task learning for joint classification and sleepiness estimation.
Improved training robustness with AdamW, ReduceLROnPlateau, early stopping, dropout, weight decay, label smoothing, class-weighted loss, weighted sampling, time jitter, and random time-crop augmentation.

Source code, setup instructions, and project documentation are available in the repository.