Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition

It was observed that accuracy of the Subject-Dependent emotion recognition model was much higher than that of the Subject-Independent model in the field of electroencephalogram (EEG) based affective computing. This phenomenon is mainly caused by the individual difference of EEG, which is the key issue to be solved for the application of emotion recognition. In this work, 14 subjects from the SEED were selected for individual difference analysis. Through individual aggregation features evaluation, sample space visualization, and correlation analysis, we proposed four quantification indicators to analyze individual difference phenomenon. Finally, we presented the Personal-Zscore (PZ) feature processing method, and it was found that the data set processed with PZ method could represent emotion better than the original data set, and the conventional model with the PZ method was more robust. The accuracies of emotion recognition models trained with PZ processing have been improved to some extent, which showed that the PZ method could effectively eliminate the individual aggregation of feature space and improve the emotional representation ability of data sets. Hence, our findings may provide a new insight into the foundation for universal implementation of EEG-based application, and the Personal-Zscore feature processing method is of great significance for the development of effective emotion recognition system.