Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition
This work addresses the challenge of limited labeled data for EEG emotion recognition, which is incremental as it applies existing semi-supervised techniques to a specific domain.
The paper tackles the problem of expensive and time-consuming labeling for EEG-based emotion recognition by proposing a semi-supervised pipeline that jointly exploits unlabeled and labeled data to learn EEG representations, achieving a new state-of-the-art performance with small subsets (3%, 5%, and 10%) of labels on the SEED dataset.
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 recognition, we propose a semi-supervised pipeline to jointly exploit both unlabeled and labeled data for learning EEG representations. Our semi-supervised framework consists of both unsupervised and supervised components. The unsupervised part maximizes the consistency between original and reconstructed input data using an autoencoder, while simultaneously the supervised part minimizes the cross-entropy between the input and output labels. We evaluate our framework using both a stacked autoencoder and an attention-based recurrent autoencoder. We test our framework on the large-scale SEED EEG dataset and compare our results with several other popular semi-supervised methods. Our semi-supervised framework with a deep attention-based recurrent autoencoder consistently outperforms the benchmark methods, even when small sub-sets (3\%, 5\% and 10\%) of the output labels are available during training, achieving a new state-of-the-art semi-supervised performance.