Differences first in asymmetric brain: A bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition

Neuroscience research studies have shown that the left and right hemispheres of the human brain response differently to the same or different emotions. Exploiting this difference in the human brain response is important to emotion recognition. In this study, we propose a bi-hemisphere discrepancy convolutional neural network model (BiDCNN) for electroencephalograph (EEG) emotion recognition, which can effectively learn the different response patterns between the left and right hemispheres, and is designed as a three-input and single-output network structure with three convolutional neural network layers. Specifically, to capture and amplify different electrical responses of the left and right brain to emotional stimuli, three different EEG feature matrices are constructed based on the International 10-20 System. Subsequently, by using three convolutional neural network layers, the spatial and temporal features are extracted to mine the inter-channel correlation among the physically adjacent EEG electrodes. We evaluate our proposed BiDCNN model on the classical dataset called DEAP to verify its effectiveness. Our results of the subject-dependent experiment show that BiDCNN achieves state-of-the-art performance with a mean accuracy of 94.38% in valence and 94.72% in arousal, where the data for training and testing come from one subject. Furthermore, our subject-independent experimental results, in which different subjects are used to train and test the model, show that BiDCNN also obtains superior results on the valence and arousal recognition tasks, respectively achieving an accuracy of 68.14% and 63.94%. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Neuroscience research has found that the left and right hemispheres of the human brain respond differently to emotions, which is crucial for emotion recognition. The proposed BiDCNN model effectively learns these differences and achieves state-of-the-art performance in emotion recognition tasks. The model demonstrates high accuracy rates in both subject-dependent and subject-independent experiments.