Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
This work addresses data scarcity for brain-computer interface researchers, though it is incremental as it builds on existing diffusion models with added flexibility.
The paper tackled the problem of data scarcity in brain-computer interfaces by developing a conditional diffusion model to generate subject-, session-, and class-specific EEG signals, achieving results that closely resemble real data.
Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models. While diffusion models have previously been successfully applied to electroencephalogram (EEG) data, existing models lack flexibility w.r.t.~sampling or require alternative representations of the EEG data. To overcome these limitations, we introduce a novel approach to conditional diffusion models that utilizes classifier-free guidance to directly generate subject-, session-, and class-specific EEG data. In addition to commonly used metrics, domain-specific metrics are employed to evaluate the specificity of the generated samples. The results indicate that the proposed model can generate EEG data that resembles real data for each subject, session, and class.