  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.
