Dataset-Independent EEG Channel Selection for Emotion Recognition

Electroencephalography (EEG) stands as a noninvasive and cost-effective method for recording neural activity, holding potential for applications such as identifying neural processes underlying human emotions. This paper delves into the transferability and generalizability of EEG channel selection in emotion recognition, adopting a dataset-independent approach. By leveraging Power Spectral Density (PSD), we identify high-contributing EEG channels in the SEED V dataset and validate our approach on the independent SEED IV dataset using a Convolutional Neural Network (CNN) model. The channel selection method helped in eliminating insignificant EEG channels, which can improve the applicability of developing more efficient EEG devices for daily use to monitor emotions, as well as in individuals suffering from various neurodegenerative diseases. Through extensive experiments varying the number of channels and features, our model achieves classification accuracies of 77.02%, 75.42%, 71.31%, and 64.31% with 62, 30, 20, and 10 EEG channels, accompanied by 310, 90, 60, and 30 Differential Entropy (DE) features respectively. Further, the proposed approach is tested by introducing Gaussian noise to the training set and evaluating its sensitivity to signal noise. Finally, results are compared with state-of-the-art models highlighting the potential of our dataset-independent channel selection method.