Affective Brain-Computer Interfaces: The Significance of P300 Components in Emotion Detection and Classification

This paper provides an in-depth analysis around the application of P300 EEG signals in EEG emotion recognition. The P300 components in the SEED dataset are extracted by peak detection method and the 4D-CRNN model is improved, which confirms the effectiveness of the P300 components in emotion recognition. The improved model has high accuracy and good generalization ability in the emotion classification task. The model not only adapts to the characteristics of P300 signals, but also shows good performance in the emotion classification task. This study reinforces the importance of the P300 component in emotion recognition, contributes new value to emotion learning datasets in the field of emotional brain-computer interfaces and provides new perspectives and clues for subsequent research in related fields.