@inproceedings{
sharma2023medic,
title={{MED}iC: Mitigating {EEG} Data Scarcity Via Class-Conditioned Diffusion Model},
author={Gulshan Sharma and Abhinav Dhall and Ramanathan Subramanian},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=0aeDKGhlTo}
}
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.
MEDiC: Mitigating EEG Data Scarcity Via Class-Conditioned Diffusion Model
Gulshan Sharma Abhinav Dhall Ramanathan Subramanian