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.

翻译：解读与语音相关的脑电图（EEG）信号是一项复杂挑战，其原因在于数据具有复杂的时空属性以及多种噪声因素。本研究探索了近年来因在表征学习方面的能力而在多个领域备受关注的去噪扩散概率模型（DDPMs），以解决这一问题。通过将DDPMs与条件自编码器结合使用，我们的新方法在准确性上显著优于传统机器学习算法及现有基准模型。实验结果凸显了DDPMs作为一种先进计算方法在分析语音相关EEG信号方面的潜力，这或将为专用于口语交流的脑机接口带来重大突破。