Classification of Human Emotions using EEG-based Causal Connectivity Patterns

Electroencephalography (EEG) signals, recorded from different channels, are used to study human brain activity in the context of emotion recognition and seizure detection. Most of the existing emotion recognition methods have focused on EEG characteristics at an electrode level and not on connectivity patterns. Causal connectivity refers to the understanding of the causal relationship between the channels. In this work, we have developed an emotion recognition model using EEG-based causal connectivity patterns. Granger causality is used to find the causal relationship of the EEG signals from different channels. The quantification of causal configurations between the channels is carried out using Transfer Entropy. The obtained Transfer Entropy values are used as features for the classification of emotions. The performance of the proposed method is validated using a publicly available SEED-IV dataset. The proposed technique achieves an average subject-specific classification accuracy of 90% (using 18 channel signals). The proposed method achieves an improvement of 1% over state-of-the-art techniques based on correlation using 62 channel signals and an improvement of 17% compared to methods that use only 18 channel signals.