  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.
