Contrastive Representation Learning for Electroencephalogram Classification
Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 136:238-253, 2020.
Interpreting and labeling human electroencephalogram (EEG) is a challenging task requiring years of medical training. We present a framework for learning representations from EEG signals via contrastive learning. By recombining channels from multi-channel recordings, we increase the number of samples quadratically per recording. We train a channel-wise feature extractor by extending the SimCLR framework to time-series data. We introduce a set of augmentations for EEG and study their efficacy on different classification tasks. We demonstrate that the learned features improve EEG classification and significantly reduce the amount of labeled data needed on three separate tasks: (1) Emotion Recognition (SEED), (2) Normal/Abnormal EEG classification (TUH), and (3) Sleep-stage scoring (SleepEDF). Our models show improved performance over previously reported supervised models on SEED and SleepEDF and self-supervised models on all three tasks.