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Interpreting EEG signals linked to spoken language presents a complexchallenge, given the data's intricate temporal and spatial attributes, as wellas the various noise factors. Denoising diffusion probabilistic models (DDPMs),which have recently gained prominence in diverse areas for their capabilitiesin representation learning, are explored in our research as a means to addressthis issue. Using DDPMs in conjunction with a conditional autoencoder, our newapproach considerably outperforms traditional machine learning algorithms andestablished baseline models in accuracy. Our results highlight the potential ofDDPMs as a sophisticated computational method for the analysis ofspeech-related EEG signals. This could lead to significant advances inbrain-computer interfaces tailored for spoken communication.

Brain-Driven Representation Learning Based on Diffusion Model