Characterizing the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton

2026-03-21

AI Summary

This study addresses the challenge of accurately decoding user movement intent during passive upper-limb exoskeleton motion, where interference from device-induced artifacts and instrumental noise severely hampers brain–computer interface (BCI)–rehabilitation robot coordination. For the first time, under conditions of exoskeleton-assisted passive movement, the authors develop offline and pseudo-online EEG-based decoders that integrate motor imagery paradigms with a real-time cueing system to effectively distinguish the onset and termination of motor imagery. Experimental results demonstrate average classification accuracies of 60.7% and 66.6% for detecting initiation and cessation states, respectively, thereby validating the feasibility of achieving naturalistic start–stop control despite exoskeletal interference. This work establishes a novel pathway toward practical implementation of non-invasive BCIs in rehabilitation robotics.

Technology Category

• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Two distinct technologies have gained attention lately due to their prospects for motor rehabilitation: robotics and brain-machine interfaces (BMIs). Harnessing their combined efforts is a largely uncharted and promising direction that has immense clinical potential. However, a significant challenge is whether motor intentions from the user can be accurately detected using non-invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. As an alternative to the straightforward continuous control approach, this study instead aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal-oriented reaching task. Using electroencephalogram signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7% and 66.6% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo-online evaluation could replicate this performance, forecasting reliable online exoskeleton control in the future. Our approach showed that participants could produce quality and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices.

Problem

• motor imagery
• exoskeleton
• brain-machine interface
• passive movement
• onset and offset detection

Innovation

• motor imagery onset/offset detection
• exoskeleton-assisted BMI
• passive movement decoding
• sensorimotor rhythms
• natural control of assistive devices