EEG Emotion Recognition via Ensemble Learning Representations

Electroencephalography (EEG) based emotion recognition is gaining substantial interest because of its strong association with the area of brain-computer interface. Even though several works exist in the literature, it is still challenging to find discriminative features that can generalize well to different EEG datasets. In this work, we focus on developing a deep learning model that makes use of the spatial and temporal representations of the EEG signal to generate EEG embeddings for emotion recognition. The proposed model uses a self-attention mechanism along with a feature fusion approach to improve the discrimination power of the learned EEG embeddings. Comprehensive experiments are conducted on the DEAP dataset, which demonstrates the superiority of the proposed work, where the attained accuracies for the arousal and valence classification are 91.17% and 90.73%, respectively.