Synchronization-based fusion of EEG and eye blink signals for enhanced decoding accuracy

Abstract

Decoding locomotor tasks is crucial in cognitive neuroscience for understanding brain responses to physical tasks. Traditional methods like EEG offer brain activity insights but may require additional modalities for enhanced interpretative precision and depth. The integration of EEG with ocular metrics, particularly eye blinks, presents a promising avenue for understanding cognitive processes by combining neural and ocular behaviors. However, synchronizing EEG and eye blink activities poses a significant challenge due to their frequently inconsistent alignment. Our study with 35 participants performing various locomotor tasks such as standing, walking, and transversing obstacles introduced a novel methodology, pcEEG+, which fuses EEG principal components (pcEEG) with aligned eye blink data (syncBlink). The results demonstrated that pcEEG+ significantly improved decoding accuracy in locomotor tasks, reaching 78% in some conditions, and surpassed standalone pcEEG and syncBlink methods by 7.6% and 22.7%, respectively. The temporal generalization matrix confirmed the consistency of pcEEG+ across tasks and times. The results were replicated using two driving simulator datasets, thereby confirming the validity of our method. This study demonstrates the efficacy of the pcEEG+ method in decoding locomotor tasks, underscoring the importance of temporal synchronization for accuracy and offering a deeper insight into brain activity during complex movements.