Using Passive BCI for Personalization of Assistive Wearable Devices: A Proof-of-Concept Study

Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in $\delta $ and $\theta $ activity and decreases in $\alpha $ and $\beta $ activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience.