Generative modeling and augmentation of EEG signals using improved diffusion probabilistic models

<i>Objective</i>. The development of deep learning models for electroencephalography (EEG) signal processing is often constrained by the limited availability of high-quality data. Data augmentation techniques are among the solutions to overcome these challenges, and deep neural generative models, with their data synthesis capabilities, are potential candidates.<i>Approach</i>. The current work investigates enhanced diffusion probabilistic models (DPM) and sampling methods for brain signal generation and data augmentation. We used implicit sampling and progressive distillation to shorten the inference and subsequently analyzed the effects of these methods on the generated data. To assess the feasibility of our method, four classification models were trained and evaluated in an inter-subject setting on datasets augmented with synthetic signals.<i>Main results.</i>Our analysis of generative metrics and statistical evaluations, including subject- and group-level tests, showed that our DPMs could generate visual evoked potentials and motor imagery signals. Distilled, single-step DPMs were trained on two publicly available datasets and were used to synthesize relatively high-quality EEG samples. The performance of the classifiers was improved by the application of the synthesized signals. The present work demonstrates that DPMs are capable of augmenting data with high fidelity and improving the diversity of EEG signals. Although samples can be generated in a single step, there is a significant trade-off between the data quality and sampling steps.<i>Significance.</i>The findings and results of this study demonstrate the promising capability of diffusion models for EEG synthesis, which marks progress toward an efficient and generalizable augmentation method for various EEG decoding tasks.