Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG
Soowon Kim Young-Eun Lee Seo-Hyun Lee Seong-Whan Lee
Decoding EEG signals for imagined speech is a challenging task due to the
high-dimensional nature of the data and low signal-to-noise ratio. In recent
years, denoising diffusion probabilistic models (DDPMs) have emerged as
promising approaches for representation learning in various domains. Our study
proposes a novel method for decoding EEG signals for imagined speech using
DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E
significantly improves the accuracy of decoding EEG signals for imagined speech
compared to traditional machine learning techniques and baseline models. Our
findings suggest that DDPMs can be an effective tool for EEG signal decoding,
with potential implications for the development of brain-computer interfaces
that enable communication through imagined speech.