EEG-Based Emotion Recognition with Consideration of Individual Difference.

Electroencephalograms (EEGs) are often used for emotion recognition through a trained EEG-to-emotion models. The training samples are EEG signals recorded while participants receive external induction labeled as various emotions. Individual differences such as emotion degree and time response exist under the same external emotional inductions. These differences can lead to a decrease in the accuracy of emotion classification models in practical applications. The brain-based emotion recognition model proposed in this paper is able to sufficiently consider these individual differences. The proposed model comprises an emotion classification module and an individual difference module (IDM). The emotion classification module captures the spatial and temporal features of the EEG data, while the IDM introduces personalized adjustments to specific emotional features by accounting for participant-specific variations as a form of interference. This approach aims to enhance the classification performance of EEG-based emotion recognition for diverse participants. The results of our comparative experiments indicate that the proposed method obtains a maximum accuracy of 96.43% for binary classification on DEAP data. Furthermore, it performs better in scenarios with significant individual differences, where it reaches a maximum accuracy of 98.92%.