Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG

2023-07-26 · 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.

📄 PDF Abstract BibTeX arXiv:2307.14389

Code (1)

yorgoon/diffe 공식 구현 pytorch

Tasks

DenoisingEEGRepresentation Learning

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