An EEG Data Processing Approach for Emotion Recognition

As the most direct way to measure the true emotional states of humans, EEG-based emotion recognition has been widely used in affective computing applications. In this paper, we aim to propose a novel emotion recognition approach that relies on a reduced number of EEG electrode channels and at the same time overcomes the negative impact of individual differences to achieve a high recognition accuracy. According to the statistical significance results of EEG power spectral density (PSD) features obtained from the SJTU Emotion EEG Dataset (SEED), six candidate sets of EEG electrode channels are determined. An experiment-level batch normalization (BN) is proposed and applied on the features from the candidate sets, and the normalized features are then used for emotion recognition across individuals. Eleven well-accepted classifiers are used for emotion recognition. The experimental results show that the recognition accuracy when using a small portion of the available electrodes is almost the same or even better than that when using all the channels. Based on the reduced number of electrode channels, the application of experiment-level BN can help further improve the recognition accuracy, specifically from 73.33% to 89.63% when using the LR classifier. These results demonstrate that better and easier emotion recognition performance can be achieved based on the batch normalized features from fewer channels, indicating promising applications of our proposed method in real-time emotion recognition applications in intelligent systems.