Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition

EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often expensive and time-consuming. To tackle this problem and reduce the need for output labels in the context of EEG-based emotion recogni- tion, we propose a semi-supervised pipeline to jointly exploit both unlabeled and labeled data for learning EEG representations.