EEG Emotion Recognition via Graph-based Spatio-Temporal Attention Neural Networks
Shadi Sartipi, Mastaneh Torkamani‐Azar, Müjdat Çetin
Abstract
Emotion recognition based on electroencephalography (EEG) signals has been receiving significant attention in the domains of affective computing and brain-computer interfaces (BCI). Although several deep learning methods have been proposed dealing with the emotion recognition task, developing methods that effectively extract and use discriminative features is still a challenge. In this work, we propose the novel spatio-temporal attention neural network (STANN) to extract discriminative spatial and temporal features of EEG signals by a parallel structure of multi-column convolutional neural network and attention-based bidirectional long-short term memory. Moreover, we explore the inter-channel relationships of EEG signals via graph signal processing (GSP) tools. Our experimental analysis demonstrates that the proposed network improves the state-of-the-art results in subject-wise, binary classification of valence and arousal levels as well as four-class classification in the valence-arousal emotion space when raw EEG signals or their graph representations, in an architecture coined as GFT-STANN, are used as model inputs.