Pseudo Channel: Time Embedding for Motor Imagery Decoding
This work addresses the challenge of individual variability in EEG-based brain-computer interfaces, offering incremental improvements for neural control and rehabilitation applications.
The study tackled the problem of decoding motor imagery EEG signals by introducing a traveling-wave based time embedding technique as a pseudo channel, which improved classification accuracy and adaptability to individual differences, particularly for participants considered 'EEG-illiteracy'.
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.