Holistic Semi-Supervised Approaches for EEG Representation Learning
This work addresses the challenge of labeling EEG data for emotion recognition, but it is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of EEG representation learning with limited labeled data by adapting holistic semi-supervised approaches, achieving strong results with as few as 1 labeled sample per class on SEED and SEED-IV datasets.
Recently, supervised methods, which often require substantial amounts of class labels, have achieved promising results for EEG representation learning. However, labeling EEG data is a challenging task. More recently, holistic semi-supervised learning approaches, which only require few output labels, have shown promising results in the field of computer vision. These methods, however, have not yet been adapted for EEG learning. In this paper, we adapt three state-of-the-art holistic semi-supervised approaches, namely MixMatch, FixMatch, and AdaMatch, as well as five classical semi-supervised methods for EEG learning. We perform rigorous experiments with all 8 methods on two public EEG-based emotion recognition datasets, namely SEED and SEED-IV. The experiments with different amounts of limited labeled samples show that the holistic approaches achieve strong results even when only 1 labeled sample is used per class. Further experiments show that in most cases, AdaMatch is the most effective method, followed by MixMatch and FixMatch.