Pseudo Channel: Time Embedding for Motor Imagery Decoding

TL;DR

This paper introduces a traveling-wave based time embedding technique as a pseudo channel to improve motor imagery EEG decoding accuracy, especially for individuals with EEG-illiteracy, by capturing temporal dynamics and enhancing neural network adaptability.

Contribution

The study presents a novel time embedding method that effectively captures temporal changes in MI-EEG signals, outperforming traditional position encoding in neural network decoding across diverse participants.

Findings

Enhanced classification accuracy across multiple neural network architectures.

Greater adaptability to individual differences compared to position encoding.

Significant improvements for participants with EEG-illiteracy.

Abstract

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.