Abstract
Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented.
Highlights
Healthy individuals whose brains and neuromuscular systems enable normal motor functions can naturally perform Activities of Daily Living (ADLs)
brain–computer interface (BCI) experiment was conducted with stroke and traumatic brain injuries (TBI) patients to address the following questions: (1) Is it feasible to classify a 2-class Motor Imagery (MI) task such as grasping or opening in a single hand? (2) Is it feasible to use sensorimotor rhythm (SMR) features evoked by voluntary MI to stop or keep Functional Electrical Stimulation (FES)? (3) What effect does the existence of electrical stimulation have on task performance? (4) Will the ensemble algorithms increase the classification accuracy when compared to traditional classification algorithms such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM)? The following hypotheses were formulated to answer the research questions: Hypotheses 1.1 (H1.1): The classification accuracy to classify a 2-class MI task in a single hand will be significantly higher than the true chance level
The period consists of the SMR, ACT, and FES period, while the classification method includes LDA, SVM, and the ensemble method
Summary
Healthy individuals whose brains and neuromuscular systems enable normal motor functions can naturally perform Activities of Daily Living (ADLs). For some people who have disabilities in these functions due to injury or disease, simple tasks become very difficult or impossible to do To assist this population, researchers in many fields, from physical therapy to engineering, have developed various rehabilitation technologies that help them perform ADLs [1,2]. Researchers in many fields, from physical therapy to engineering, have developed various rehabilitation technologies that help them perform ADLs [1,2] One such technology, Functional Electrical Stimulation (FES), delivers electrical impulses to either paralyzed or impaired limbs to generate artificial muscle contraction [3,4]. These neuromuscular disorders cause upper and/or lower extremity impairments, such as hemiparesis or hemiplegia, and they hinder patients from performing
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