Creating Synthetic EEG Signals Using a Denoising Diffusion Probabilistic Model
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2023-02-23
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en
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Abstract
Electroencephalography (EEG) is often used in neuroscientific research to measure brain
signals non-invasively. However, due to the limitations of EEG a lot of potentially interesting
data is lost. Some researchers have attempted to solve this problem with data
augmentation. The state-of-the-art of synthetic EEG signal generation uses variational
autoencoders (VAEs) or generative adversarial networks (GANs). In the past few years
a new kind of generative model has been introduced, namely denoising diffusion probabilistic
models (diffusion models). In this work we aim explore the possible capacity of
diffusion models to generate synthetic EEG signals. We train a diffusion model adapted
for one-dimensional data on the EEGdenoiseNet dataset. To compare it to the state-ofthe-
art we also train TimeVAE on the same dataset. We evaluate both models using the
minimal Euclidean distance, mean Wasserstein distance, and FID-score. Additionally, we
visually examine the generated samples and compare them with a PSD-plot. For two
evaluation metrics our diffusion model performs similarly as the state-of-the-art, and for
one evaluation metric it significantly outperforms the state-of-the-art. Therefore, we conclude
that our diffusion model is capable of generating synthetic EEG signals and more
diverse samples than the state-of-the-art while maintaining similar quality.
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Faculteit der Sociale Wetenschappen