Online Performance Evaluation and Motor Intent Decoding of an Integrated Wearable Brain-Computer Interface System

Abstract

Addressing the demand for portable, real-time brain-computer interface systems in stroke rehabilitation, this chapter completes the physical integration and online experimental validation of a wearable system. The system utilizes a specialized EEG headset with miniaturized acquisition circuits secured via pogo pins, featuring 10 core recording channels strategically positioned over the sensorimotor cortex. During the experimental evaluation phase, the research team recruited 6 healthy subjects and 2 stroke-affected hemiplegic patients to conduct closed-loop experiments based on motor imagery and motor attempts. This study employed Common Space Pattern (CSP) for spatial feature extraction, combined with Linear Discriminant Analysis (LDA) for intention classification. Recognition performance was further enhanced through personalized sub-band optimization techniques. Experimental results demonstrate an average offline recognition rate of 84.91%. In the more challenging online real-time testing, the classification accuracy reached 79.38%. Furthermore, by analyzing spatiotemporal spectra and R² value distributions, the study validated the activation patterns in the brain's sensorimotor areas during motor intention triggering at the neurophysiological level. The findings in this chapter provide critical data support and technical solutions for advancing brain-computer interface technology from laboratory settings to community rehabilitation.