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.

翻译：脑机接口领域的数据稀缺问题可通过生成模型（特别是扩散模型）得到缓解。尽管扩散模型此前已成功应用于脑电图数据，但现有模型在采样灵活性方面存在局限，或需要采用脑电图数据的替代表示形式。为解决上述问题，我们提出了一种新颖的条件扩散模型方法，该方法利用无分类器引导技术直接生成具有受试者、实验会话及类别特异性的脑电图数据。除常规评估指标外，本研究还采用领域特定指标对生成样本的特异性进行评价。结果表明，所提模型能够为每个受试者、实验会话及类别生成与真实数据高度相似的脑电图数据。