Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments.Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.

Design and Characterization of a Rolling-Contact Involute Joint and Its Applications in Finger Exoskeletons

The hand exoskeleton has been widely studied in the fields of hand rehabilitation and grasping assistance tasks. Current hand exoskeletons face challenges in combining a user-friendly design with a lightweight structure and accurate modeling of hand motion. In this study, we developed a finger exoskeleton with a rolling contact involute joint. Specific implementation methods were investigated, including an analysis of the mechanical characteristics of the involute joint model, the formula derivation of the joint parameter optimization algorithm, and the design process for a finger exoskeleton with an involute joint. Experiments were conducted using a finger exoskeleton prototype to evaluate the output trajectory and grasping force of the finger exoskeleton. An EMG-controlled hand exoskeleton was developed to verify the wearability and functionality of the glove. The experimental results show that the proposed involute joint can provide sufficient fingertip force (10N) while forming a lightweight exoskeleton to assist users with functional hand rehabilitation and grasping activities.