A New Gaze-BCI-Driven Control of an Upper Limb Exoskeleton for Rehabilitation in Real-World Tasks

TLDR

The study proposes a gaze‑independent BCI‑driven multimodal architecture to control a robotic upper‑limb exoskeleton for stroke rehabilitation, enabling active assistance during reaching tasks in real‑world settings. The system decodes patient intention using an active vision system that fuses a Kinect‑based 3‑D object tracker with an eye‑tracking selection module, then applies a BCI to translate motor‑imagery brain activity into continuous control of the exoskeleton’s kinematic parameters (speed, acceleration, jerk) during movement generation. Experimental evaluation with three healthy volunteers and four chronic stroke patients showed that all subjects could operate the exoskeleton via BCI with an average classification error of 89.4 ± 5.0 %, and performance did not differ between stroke patients and healthy controls, indicating high potential for early‑phase motor‑impairment rehabilitation.

Abstract

This paper proposes a new multimodal architecture for gaze-independent brain-computer interface (BCI)-driven control of a robotic upper limb exoskeleton for stroke rehabilitation to provide active assistance in the execution of reaching tasks in a real setting scenario. At the level of action plan, the patient's intention is decoded by means of an active vision system, through the combination of a Kinect-based vision system, which can online robustly identify and track 3-D objects, and an eye-tracking system for objects selection. At the level of action generation, a BCI is used to control the patient's intention to move his/her own arm, on the basis of brain activity analyzed during motor imagery. The main kinematic parameters of the reaching movement (i.e., speed, acceleration, and jerk) assisted by the robot are modulated by the output of the BCI classifier so that the robot-assisted movement is performed under a continuous control of patient's brain activity. The system was experimentally evaluated in a group of three healthy volunteers and four chronic stroke patients. Experimental results show that all subjects were able to operate the exoskeleton movement by BCI with a classification error rate of 89.4±5.0% in the robot-assisted condition, with no difference of the performance observed in stroke patients compared with healthy subjects. This indicates the high potential of the proposed gaze-BCI-driven robotic assistance for neurorehabilitation of patients with motor impairments after stroke since the earliest phase of recovery.