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Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial

Juan Lorenzo Hagad^1,2, Tsukasa Kimura², Ken-Ichi Fukui²

¹Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This study introduces a novel deep learning model to improve emotion detection from electroencephalography (EEG) signals. The model addresses limited data and subject variability, enhancing accuracy in emotion recognition.

Keywords:

domain adversarial network domain generalization electroencephalography emotion modeling subject independence variational autoencoder

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Area of Science:

Neuroscience
Machine Learning
Affective Computing

Background:

Emotion detection from electroencephalography (EEG) faces challenges due to limited labeled data and significant inter-subject variability.
Existing models struggle to generalize across diverse user populations, hindering practical applications.

Purpose of the Study:

To develop a context-generalized deep neural network model for robust emotion detection from EEG data.
To simultaneously address data scarcity and subject variability in EEG-based emotion recognition.

Main Methods:

Utilized variational autoencoders (VAEs) for training on both labeled and unlabeled data, maximizing data utilization.
Implemented variational regularization to promote Gaussian-distributed, subject-independent feature embeddings.
Applied subject-adversarial regularization to bi-lateral features for enhanced subject-independence in emotion classification.

Main Results:

The proposed model demonstrated superior subject-independent generalization performance compared to state-of-the-art methods on SEED and DEAP datasets.
Qualitative analysis indicated that the subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture discovers normally distributed features, improving cross-subject performance.

Conclusions:

The BiVDANN architecture effectively tackles data constraints and subject variability in EEG emotion detection.
The model offers improved generalization capabilities for real-world applications of affective computing using EEG.