  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.
