Improved EEG-Based Emotion Classification via Stockwell Entropy and CSP Integration

Traditional entropy-based learning methods primarily extract the relevant entropy measures directly from EEG signals using sliding time windows. This study applies differential entropy to a time-frequency domain that is decomposed by Stockwell transform, proposing a novel EEG emotion recognition method combining Stockwell entropy and a common spatial pattern (CSP). The results demonstrate that Stockwell entropy effectively captures the entropy features of high-frequency signals, and CSP-transformed Stockwell entropy features show superior discriminative capability for different emotional states. The experimental results indicate that the proposed method achieves excellent classification performance in the Gamma band (30-46 Hz) for emotion recognition. The combined approach yields high classification accuracy for binary tasks ("positive vs. neutral", "negative vs. neutral", and "positive vs. negative") and maintains satisfactory performance in the three-class task ("positive vs. negative vs. neutral").