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InfoResearchPeer-reviewed
Enhancing EEG Signal-Based Emotion Recognition With Synthetic Data: Diffusion Model Approach
- Published
- Record updated
Summary
Researchers propose generating synthetic EEG samples with a conditional denoising diffusion probabilistic model (DDPM) that adds noise, to address scarce EEG data for emotion recognition. On the DEAP dataset, synthetic data improved classification accuracy by up to 5.6%, with similar positive results on the SADT dataset. The approach outperformed traditional generative adversarial network and DDPM-based methods.