  Interpreting EEG signals linked to spoken language presents a complex
challenge, given the data's intricate temporal and spatial attributes, as well
as the various noise factors. Denoising diffusion probabilistic models (DDPMs),
which have recently gained prominence in diverse areas for their capabilities
in representation learning, are explored in our research as a means to address
this issue. Using DDPMs in conjunction with a conditional autoencoder, our new
approach considerably outperforms traditional machine learning algorithms and
established baseline models in accuracy. Our results highlight the potential of
DDPMs as a sophisticated computational method for the analysis of
speech-related EEG signals. This could lead to significant advances in
brain-computer interfaces tailored for spoken communication.
