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Cross-Subject EEG-Based Emotion Recognition through Neural Networks with Stratified Normalization

Javier Fdez, Nicholas Guttenberg, Olaf Witkowski, Antoine Pasquali
doi: https://doi.org/10.1101/2020.09.18.304501
Javier Fdez
1Cross Labs, Cross Compass Ltd., Tokyo,, Japan
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  • For correspondence: javier.f3rnand3z@gmail.com
Nicholas Guttenberg
1Cross Labs, Cross Compass Ltd., Tokyo,, Japan
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Olaf Witkowski
1Cross Labs, Cross Compass Ltd., Tokyo,, Japan
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Antoine Pasquali
1Cross Labs, Cross Compass Ltd., Tokyo,, Japan
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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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 20, 2020.
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Cross-Subject EEG-Based Emotion Recognition through Neural Networks with Stratified Normalization
Javier Fdez, Nicholas Guttenberg, Olaf Witkowski, Antoine Pasquali
bioRxiv 2020.09.18.304501; doi: https://doi.org/10.1101/2020.09.18.304501
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Cross-Subject EEG-Based Emotion Recognition through Neural Networks with Stratified Normalization
Javier Fdez, Nicholas Guttenberg, Olaf Witkowski, Antoine Pasquali
bioRxiv 2020.09.18.304501; doi: https://doi.org/10.1101/2020.09.18.304501

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