A Novel Solution for EEG-based Emotion Recognition

Abstract

Electroencephalogram (EEG) is widely utilized in emotion recognition because of its exceptional stability and high detection accuracy. However, large amounts of labeled EEG data are difficult to come by. Self-supervised representation learning with multi-transformation tasks is presented as an innovative solution for emotion recognition. The solution consists of two tasks: self-supervised representation learning and emotion recognition. Self-supervised learning is applied to learn high-level EEG representation from unlabeled data. Representation learning contains six different transformations to learn the high-level EEG representations comprehensively: noising, scaling, negating, horizontally flipping, permuting, and time-warping. Then the self-supervised network can recognize different EEG representations, after that the weights of convolutional layers are frozen and transferred to the emotion recognition network, and the ability to distinguish EEG is transferred too. This is the first work that self-supervised learning that has been used for emotion recognition using EEG signals to the best of our knowledge. The accuracy we achieved is 98.64% that higher than all known fully supervised methods, and self-supervised learning saves a tremendous amount of time for labeling data. This result is state-of-the-art until now. Our experiments prove that the application of self-supervised learning in EEG-based emotion recognition is feasible and effective.