Characterizing the stimulation interference in electroencephalographic signals during brain‐computer interface‐controlled functional electrical stimulation therapy

2021 ◽  
Author(s):  
Lazar I. Jovanovic ◽  
Milos R. Popovic ◽  
Cesar Marquez‐Chin
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrical Stimulation Therapy ◽  
Electroencephalographic Signals

scholarly journals Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation

2020 ◽  
Vol 10 (8) ◽  
pp. 512
Author(s):  
Inchul Choi ◽  
Gyu Hyun Kwon ◽  
Sangwon Lee ◽  
Chang S. Nam
Keyword(s):  
Electrical Stimulation ◽  
Motor Imagery ◽  
Functional Electrical Stimulation ◽  
Adaptive Learning ◽  
Brain Computer Interface ◽  
Phase 1 ◽  
Chance Level ◽  
Computer Interface ◽  
Motor Deficits ◽  
Phase 2

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.

Brain–Computer Interface With Functional Electrical Stimulation Training for Upper-Extremity Rehabilitation Performance Outcomes

2017 ◽  
Vol 71 (4_Supplement_1) ◽  
pp. 7111515250p1
Author(s):  
Samantha Evander Elmore ◽  
Laura Kiekhoefer ◽  
Jessica Abrams ◽  
Rebecca Vermilyea ◽  
Dorothy Farrar-Edwards ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Upper Extremity ◽  
Functional Electrical Stimulation ◽  
Brain Computer Interface ◽  
Performance Outcomes ◽  
Computer Interface ◽  
Upper Extremity Rehabilitation
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