Abstract
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 language | English |
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Title of host publication | 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Pages | 3150-3157 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-6654-5258-8 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE International Conference on Systems, Man, and Cybernetics: SMC 2022 - Prague, Czech Republic Duration: 9 Oct 2022 → 12 Oct 2022 |
Conference
Conference | 2022 IEEE International Conference on Systems, Man, and Cybernetics |
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Abbreviated title | SMC 2022 |
Country/Territory | Czech Republic |
City | Prague |
Period | 9/10/22 → 12/10/22 |
ASJC Scopus subject areas
- Artificial Intelligence
- Signal Processing
- Biomedical Engineering
Fields of Expertise
- Human- & Biotechnology
- Information, Communication & Computing