Synthetic EEG Generation using Diffusion Models for Motor Imagery Tasks

TL;DR

This paper introduces a diffusion model-based method for generating synthetic EEG signals related to motor imagery tasks, aiming to enhance data availability and improve classification accuracy in brain-computer interfaces.

Contribution

It presents a novel application of diffusion probabilistic models to generate realistic synthetic EEG data for motor imagery, addressing data scarcity in BCI research.

Findings

Synthetic EEG achieved over 95% classification accuracy

Generated signals showed low mean squared error with real data

Synthetic data improved classifier performance in BCI tasks

Abstract

Electroencephalography (EEG) is a widely used, non-invasive method for capturing brain activity, and is particularly relevant for applications in Brain-Computer Interfaces (BCI). However, collecting high-quality EEG data remains a major challenge due to sensor costs, acquisition time, and inter-subject variability. To address these limitations, this study proposes a methodology for generating synthetic EEG signals associated with motor imagery brain tasks using Diffusion Probabilistic Models (DDPM). The approach involves preprocessing real EEG data, training a diffusion model to reconstruct EEG channels from noise, and evaluating the quality of the generated signals through both signal-level and task-level metrics. For validation, we employed classifiers such as K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), and U-Net to compare the performance of synthetic data against real data in classification tasks. The generated data achieved classification accuracies above 95%, with low mean squared error and high correlation with real signals. Our results demonstrate that synthetic EEG signals produced by diffusion models can effectively complement datasets, improving classification performance in EEG-based BCIs and addressing data scarcity.