EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders
This work addresses the need for efficient affective EEG representations in edge computing, though it is incremental as it builds on existing variational autoencoder methods.
The paper tackled the problem of representing affective EEG data in a sparse format for memory-constrained applications, achieving 68.49% classification accuracy for three emotion categories and generating synthetic EEG sequences that resemble real data.
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states as well as generate synthetic EEG data that are participant- and/or emotion-specific. We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data. Experimental results on affective EEG recording datasets demonstrate that our model is suitable for unsupervised EEG modeling, classification of three distinct emotion categories (positive, neutral, negative) based on the latent representation achieves a robust performance of 68.49%, and generated synthetic EEG sequences resemble real EEG data inputs to particularly reconstruct low-frequency signal components. Our work advances areas where affective EEG representations can be useful in e.g., generating artificial (labeled) training data or alleviating manual feature extraction, and provide efficiency for memory constrained edge computing applications.