EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network

Emotion recognition based on electroencephalography (EEG) is of great important in the field of Human–Computer Interaction (HCI), which has received extensive attention in recent years. Most traditional methods focus on extracting features in time domain and frequency domain. The spatial information from adjacent channels and symmetric channels is often ignored. To better learn spatial representation, in this paper, we propose an end-to-end Regional-Asymmetric Convolutional Neural Network (RACNN) for emotion recognition, which consists of temporal, regional and asymmetric feature extractors. Specifically, continuous 1D convolution layers are employed in temporal feature extractor to learn time–frequency representations. Then, regional feature extractor consists of two 2D convolution layers to capture regional information among physically adjacent channels. Meanwhile, we propose an Asymmetric Differential Layer (ADL) in asymmetric feature extractor by taking the asymmetry property of emotion responses into account, which can capture the discriminative information between left and right hemispheres of the brain. To evaluate our model, we conduct extensive experiments on two publicly available datasets, i.e., DEAP and DREAMER. The proposed model can obtain recognition accuracies over 95% for valence and arousal classification tasks on both datasets, significantly outperforming the state-of-the-art methods.