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.

通过使用 DDPMs 和条件自编码器 Diff-E 对 EEG 信号进行解码来实现想象语言，结果表明 Diff-E 相较于传统机器学习技术和基准模型，显著提高了解码 EEG 信号想象语言的准确性，这一发现表明 DDPMs 可以成为 EEG 信号解码的有效工具，具有潜在的用于通过想象语言进行交流的脑机接口的意义。