Online Regulation of Task Difficulty Based on Neuro- and Motor-Feedback to Improve Engagement in Visual-Motor Task

Enhancing active engagement in post-stroke rehabilitation is critical for promoting neuroplasticity. Although adaptive feedback can optimize arousal to improve engagement, most approaches rely solely on motor or neural indicators, overlooking the integration of task-specific physical performance with neural adaptation. The purpose of this study is to validate the effectiveness of enhancing prefrontal cortex (PFC) neural activity through a closed-loop adaptive feedback system.

In this study, a neuro- and motor-feedback (NMF) system is proposed. It utilizes functional near infrared spectroscopy (fNIRS) and tracking error to continuously monitor real-time neural activity and motor performance during a visual-motor task, and realizes online adaptive regulation of task difficulty through fuzzy logic controller. 10 healthy participants were recruited for a 5-day training program, during which each participant completed 15 task trials at both fixed and adaptive difficulty levels, serving as the control group and the NMF group.

Compared to the control group, the NMF group showed increased tracking errors as well as heightened neural activity in the PFC and the sensorimotor cortex (SMC), in both single-task trial and after 5 days of training. Moreover, the NMF group exhibited significantly increased strength of brain functional connections between the PFC and sensorimotor areas after training compared to the control group.

Our findings suggest that the proposed NMF system can enable online neural activity regulation in visual-motor tasks and achieve enhanced integration between cognitive and sensorimotor areas, with the potential to improve the rehabilitation training outcomes.