MEDiC: Mitigating EEG Data Scarcity Via Class-Conditioned Diffusion Model

Gulshan Sharma, Abhinav Dhall, Ramanathan Subramanian

Keywords: Synthetic EEG data, Class-Conditioned Diffusion Model, Alzheimer’s disease, Frontotemporal Dementia, EEG Augmentation

Abstract: Learning with a small-scale Electroencephalography (EEG) dataset is a non-trivial task. On the other hand, collecting a large-scale EEG dataset is equally challenging due to subject availability and procedure sophistication constraints. Data augmentation offers a potential solution to address the shortage of data; however, traditional augmentation techniques are inefficient for EEG data. In this paper, we propose MEDiC, a class-conditioned Denoising Diffusion Probabilistic Model (DDPM) based approach to generate synthetic EEG embeddings. We perform experiments on a publicly accessible dataset. Empirical findings indicate that MEDiC efficiently generates synthetic EEG embeddings, which can serve as effective proxies to original EEG data.

Submission Number: 64