Quantifying Emotional Patterns for EEG-based Emotion Recognition: An Interpretable Study on EEG Individual Differences

Electroencephalogram (EEG) individual differences are a critical factor influencing EEG-based emotion recognition, yet they have not been thoroughly investigated, hindering the development of affective Brain-Computer Interfaces (aBCI). Facing the lack of EEG information decoding research, we conducted an interpretable study on EEG individual differences using five datasets (SEED, SEED-IV, SEED-V, RCLS, and MPED). We analyzed the impact of different EEG information (individual, session, emotion, and trial) through sample space visualization, aggregation phenomena quantification, and energy pattern analysis. By examining emotional difference feature distribution patterns, we identified the Cross-Session Consistency of Individual Emotional Patterns (CCIEP) and the Individual Emotional Pattern Difference (IEPD). These characteristics are the main factors impacting emotion recognition stability. To quantify emotional patterns, we proposed the Correction T-test (CT) weight extraction method. Leveraging individual emotional pattern and trial information, we developed the Weight-based Channel-model Matrix Framework (WCMF) to address limitations of traditional modeling approaches caused by IEPD. Finally, WCMF was validated on cross-dataset tasks through two practical scenario experiments. The results demonstrated that WCMF achieves more stable and superior performance compared to traditional methods. This study provides a deeper understanding of EEG individual differences and offers a robust framework to advance aBCI systems.