Brain Signal Generation and Data Augmentation with a Single-Step Diffusion Probabilistic Model

Szabolcs Torma, Dr. Luca Szegletes

Keywords: diffusion probabilistic model, generative model, electroencephalograpghy, eeg, erp, motor imagery, synthesis, augmentation

TL;DR: We show on multiple brain signal datasets that distilled diffusion probability models can synthesize EEG signals with high accuracy and diversity, which can be used for data augmentation.

Abstract: Brain-computer interfaces based on deep learning rely on large amounts of high-quality data. Finding publicly available brain signal datasets that meet all requirements is a challenge. However, brain signals synthesized with generative models may provide a solution to this problem. Our work builds on diffusion probabilistic models (DPMs) and aims to generate brain signals that have the properties needed to develop further classification models based on deep learning. We show that our DPM can generate high-quality event-related potentials (ERPs) and motor imagery (MI) signals. Furthermore, with the progressive distillation of the model, subject-specific data can be produced in a one-step reverse process. We augment publicly available datasets and demonstrate the impact of the generated signals on a deep learning classification model. DPMs are versatile models, and this work shows that brain signal processing is one of many other tasks in which these models can be useful.

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