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.

翻译：摘要：解码想象言语的脑电图（EEG）信号因数据的高维特性和低信噪比而颇具挑战。近年来，去噪扩散概率模型（DDPMs）已成为多个领域中表示学习的前沿方法。本研究提出了一种利用DDPMs及名为Diff-E的条件自编码器进行想象言语EEG信号解码的新方法。结果表明，与传统机器学习技术和基线模型相比，Diff-E显著提高了想象言语EEG信号解码的准确性。我们的发现表明，DDPMs可成为EEG信号解码的有效工具，为开发通过想象言语实现通信的脑机接口提供了潜在的应用前景。