Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization

• Published in: Frontiers in neuroscience Vol. 15; p. 626277
• Main Authors: Fdez, Javier, Guttenberg, Nicholas, Witkowski, Olaf, Pasquali, Antoine
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 03.02.2021; Frontiers Media S.A
• Subjects: Accuracy; Classification; cross-subject; deep learning; EEG; Experiments; feature normalization; Labeling; Learning algorithms; Machine learning; Memory; Methods; Neural networks; Neuroscience; Neurosciences; Researchers; SEED dataset; stratified normalization; Variables; deep learning; stratified normalization; cross-subject; affective computing; EEG; SEED dataset; emotion recognition; feature normalization
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2021.626277

Due to a large number of potential applications, a good deal of effort has been recently made toward 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 such a system by a time-consuming calibration stage. In this study, we introduce a new participant-based feature normalization method, named stratified normalization , for training deep neural networks in the 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 watching film clips. Results demonstrate that networks trained with stratified normalization significantly outperformed standard training with batch normalization. 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.