BRAIN SIGNAL GENERATION AND DATA AUGMENTA-TION WITH A SINGLE-STEP DIFFUSION PROBABILIS-TIC MODEL

Abstract

Brain-computer interfaces based on deep learning rely on large amounts of highquality 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.



) is undoubtedly one of the most popular brain mapping technologies, which is widely used in research and clinical diagnosis (de Aguiar Neto & Rosa (2019), van Mierlo et al. (2020), Wang et al. (2020)). EEG records the neural activity of the brain in a non-invasive manner. (Biasiucci et al. (2019)) EEG is less complex and cheaper than other brain imaging technologies. EEG has one of the best temporal resolutions. However, the spatial resolution of the technology is quite poor due to its heavy dependence on the number of electrodes used for signal recording and non-invasiveness. (Craik et al. (2019b)) Brain-computer interfaces (BCIs) connect the brain and external processing devices, making it possible to perform tasks using only brain signals. BCIs can help in everyday life for people with limited movement and communication abilities (Pandarinath et al. (2017)). BCIs are also applied in many other fields from healthcare (Galán et al. (2008), Vilela & Hochberg (2020)) to entertainment (Finke et al. (2009)). BCIs are often based on EEG due to the ability of the technology to measure signals with only a couple of milliseconds difference and its relatively low cost and more comfort. The measurements are then processed by a decoder unit in the BCI that turns the recorded temporal and frequency patterns into actions. (Lotte et al. (2018)) In recent years, deep learning (DL) algorithms have become more and more commonly used in EEG signal processing (Roy et al. (2019), Craik et al. (2019a), Kotowski et al. (2020)). DL models can decode brain signals with high accuracy. However, developing DL models requires a large amount of high-quality data. The size and quality of publicly available data sets are limited, also often insufficient and imbalanced. Recording a new data set can be highly resource-consuming and requires professionals to check the measurements. Another option to augment data sets is data synthesis. (Lashgari et al. (2020)) Score-based models (Tashiro et al. (2021), Song et al. (2021)), diffusion probabilistic models (DPMs) (Ho et al. (2020), Luo & Hu (2021)) and generative adversarial networks (GANs) (Liu et al. (2021), Chan et al. (2021)) hold the state-of-the-art in deep-learning-based generative modelling. The recent advances show the performance and effectiveness DPMs over GANs in both image (Dhariwal & Nichol (2021)) and audio generation (Kong et al. (2021)). There are a handful 1

