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。

Diff-E：基于扩散的学习用于解码想象的言语脑电图

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

We propose a mixed deep neural network strategy, incorporating parallel
combination of Convolutional (CNN) and Recurrent Neural Networks (RNN),
cascaded with deep autoencoders and fully connected layers towards automatic
identification of imagined speech from EEG. Instead of utilizing raw EEG
channel data, we compute the joint variability of the channels in the form of a
covariance matrix that provide spatio-temporal representations of EEG. The
networks are trained hierarchically and the extracted features are passed onto
the next network hierarchy until the final classification. Using a publicly
available EEG based speech imagery database we demonstrate around 23.45%
improvement of accuracy over the baseline method. Our approach demonstrates the
promise of a mixed DNN approach for complex spatial-temporal classification
problems.

本文提出了一种混合深度神经网络策略，将卷积神经网络和递归神经网络并行组合，级联自编码器和全连接层，以自动识别 EEG 中的想象语音，并演示了相对于基线方法约 23.45％的准确度改进，展示了混合 DNN 方法在复杂时空分类问题上的潜力。