Closed-Loop Multimodal Sensory Training Enhances the Proprioceptive-Motor Pathway: Low-Load Automaticity and Fine Motor Control
Qian Cheng, Zhouhaotian Yin, Yuan Liu
AI summary
Problem
Noninvasive artificial-feedback systems often impose high cognitive load, fail to generalize to new conditions, and lack clear neural evidence for efficiency gains, hindering reliable upper-limb human-machine interfaces.
Approach
The authors implemented a six-day progressive training protocol that gradually shifts user reliance from vision to bidirectional electrotactile feedback mapped to three robotic joint angles, assessing performance through kinematic tracking, dual-task tests, and EEG.
Key results
- Approximately 30% reduction in tracking errors and response times
- Improved motor and cognitive performance under dual-task load
- Successful generalization to reversed sensory mappings
- Enhanced maze completion times indicating finer motor control
Why it matters
Offers a reproducible, low-load training route for strengthening proprioceptive pathways, directly benefiting prosthetic design, exoskeleton rehabilitation, and teleoperation systems.
Abstract
Designing reliable upper-limb human-machine interfaces (HMIs) with low attentional demand requires strengthening the proprioceptive-motor pathway (PMP). We propose a closed-loop multimodal sensory training that maps three robot joint angles to six bidirectional electrotactile channels and combines visual fading with degrees-of-freedom (DoF) progression to shift reliance from vision to tactile-proprioceptive guidance. The objective is low-load automaticity for supplemental cues and improved native-limb fine motor control. Twenty right-handed adults completed a six-day protocol. Using synchronized kinematics and EEG, we evaluated electrotactile-driven tasks: eyes-closed continuous tracking and static posture reproduction, dual-task posture reproduction with serial subtraction, reversed-mapping generalization, and a proprioceptively constrained maze. Training produced robust gains under tactile-proprioceptive dominance: errors decreased (~30%) and response time shortened. Under dual-task load, posture error and response time decreased while correct subtractions increased and mistakes decreased, supporting low-load automaticity of electrotactile decoding. Although group-level β-event-related desynchronization (ERD) changes were not significant, contralateral ERD reductions and post-movement beta rebound (PMBR) enhancements during tactile decoding were consistent with reduced cortical effort and emerging automatic control. Performance generalized to reversed mapping, and maze completion time decreased significantly, evidencing improved fine motor control. These findings show that closed-loop vision-tactile-proprioceptive integration offers a compact, reproducible route to PMP enhancement, enabling low-load automaticity and finer control, with actionable design targets for prosthetics, exoskeleton rehabilitation, and vision-limited teleoperation.