The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifold Domain enhanced transformer algorithm is designed to holistically capture EEG’s spatio-temporal information. Our method projects the time-domain information of EEG signals into the Riemannian spaces to fully decode the time dependence of EEG signals. Using wavelet transform, the time domain information is converted into frequency domain information, and the spatial information contained in the frequency domain information of EEG signal is mined through the spectrogram. The effectiveness of the proposed TBEEG algorithm is validated on BCIC-IV-2a dataset and MAMEM-SSVEP-II datasets.

中文翻译：

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TBEEG：一种用于学习脑电图解码的两分支流形域增强变压器算法

基于脑电图（EEG）的脑机接口（BCI）在最近的研究中引起了极大的关注。然而，由于缺乏高效的脑电图解码技术，脑电图的实用性仍然受到限制。挑战在于如何有效地将复杂的脑电图转化为有意义的、可概括的信息。脑电图信号解码主要依赖于时域或频域信息。目前缺乏一种能够同时有效地提取时域和频域特征并有效融合这些特征的方法。为了解决这些限制，设计了两分支流形域增强变换算法来整体捕获脑电图的时空信息。我们的方法将脑电信号的时域信息投影到黎曼空间中，以完全解码脑电信号的时间依赖性。利用小波变换将时域信息转换为频域信息，并通过频谱图挖掘脑电信号频域信息中包含的空间信息。所提出的 TBEEG 算法的有效性在 BCIC-IV-2a 数据集和 MAMEM-SSVEP-II 数据集上得到验证。