Motor imagery (MI) based EEG represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation. This study introduces a novel time embedding technique, termed traveling-wave based time embedding, utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures. Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference, our approach captures time-related changes for different participants based on a priori knowledge. Through extensive experimentation with multiple participants, we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture. Significantly, our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy, particularly for participants typically considered "EEG-illiteracy". As a novel direction in EEG research, the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.

翻译：基于运动想象的脑电信号是实现外部设备直接神经控制和推进神经康复的前沿领域。本研究引入了一种新颖的时间嵌入技术，称为基于行波的时间嵌入，将其作为伪通道来提升多种神经网络架构对运动想象脑电信号的解码精度。与传统神经网络方法未能考虑个体差异中运动想象脑电信号的时间动态特性不同，我们的方法基于先验知识捕捉不同受试者的时间相关变化。通过对多位受试者进行大量实验，我们证明该方法不仅提高了分类准确率，而且相较于Transformer架构中使用的位置编码，对个体差异表现出更强的适应性。值得注意的是，我们的结果表明基于行波的时间嵌入显著提升了解码精度，特别是对于那些通常被认为是"脑电盲"的受试者。作为脑电研究的新方向，基于行波的时间嵌入不仅为神经网络解码策略提供了新的见解，也为神经科学中注意力机制的研究和更深入理解脑电信号开辟了新途径。