Abstract
Due to a large number of potential applications, a good deal of effort has been recently made towards creating machine learning models that can recognize evoked emotions from one’s physiological recordings. In particular, researchers are investigating the use of EEG as a low-cost, non-invasive method. However, the poor homogeneity of the EEG activity across participants hinders the implementation of any such system by a time-consuming calibration stage. In this study, we introduce a new participant-based feature normalization method, so-called stratified normalization, for training deep neural networks in task of cross-subject emotion classification from EEG signals. The new method is able to subtract inter-participant variability while maintaining the emotion information in the data. We carried out our analysis on the SEED dataset, which contains 62-channel EEG recordings collected from 15 participants while watching film clips. Results demonstrate that networks trained with stratified normalization outperformed standard training with batch normalization significantly. In addition, the highest model performance was achieved when extracting EEG features with the multitaper method, reaching a classification accuracy of 91.6% for two emotion categories (positive and negative) and 79.6% for three (also neutral). This analysis provides us with great insight into the potential benefits that stratified normalization can have when developing any cross-subject model based on EEG.
Competing Interest Statement
The authors have declared no competing interest.
