EEG2Vec: Learning affective EEG representations via variational autoencoders

David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari, Lewis Chuang, Ozan Özdenizci, Albrecht Schmidt

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Number of pages8
ISBN (Electronic)978-1-6654-5258-8
Publication statusPublished - 2022
Event2022 IEEE International Conference on Systems, Man, and Cybernetics: SMC 2022 - Prague, Czech Republic
Duration: 9 Oct 202212 Oct 2022


Conference2022 IEEE International Conference on Systems, Man, and Cybernetics
Abbreviated titleSMC 2022
Country/TerritoryCzech Republic

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Biomedical Engineering

Fields of Expertise

  • Human- & Biotechnology
  • Information, Communication & Computing


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