EEG-based Emotion Recognition Using Temporal Convolutional Network and Vision Transformer

Electroencephalogram (EEG)-based emotion recognition is a crucial component of affective computing. While the Transformer has proven successful in computer vision, its application in the EEG remains a challenge. This paper introduces a novel model, the Temporal Convolutional Vision Transformer (TC-ViT), seamlessly integrating the architectures of the Temporal Convolutional Network (TCN) and Vision Transformer (ViT), which employs the TCN module to extract preliminary spectral-spatial features from EEG and the ViT module to enhance the extracted features discriminative capability. Emotion classification is executed using a multilayer perceptron. The proposed TC-ViT model demonstrates exceptional performance on the SEED (99.31%) and SEED-IV (93.72%) public datasets, outperforming state-of-the-art methods in EEG-based emotion recognition.