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Neural activities classification of left and right finger gestures during motor execution and motor imagery

Neural activities classification of left and right finger gestures during motor execution and... In this study, a new paradigm containing motor observation, motor execution, and motor imagery was designed to investigate whether motor imagery (MI) and motor execution (ME) of finger gestures can be used to extend commands of practical mBCIs. The subjects were instructed to perform or imagine 30 left and right finger gestures. Hierarchical support vector machine (hSVM) method was applied to classify four tasks (i.e., ME and MI tasks between left and right gestures). The average classification accuracies of motor imagery and execution tasks using fivefold cross-validation were 90.89 ± 9.87% and 74.08 ± 13.42% in first layer and second layer, respectively. The average accuracy of classification of four classes is 83.06 ± 7.29% overall. These results show that performing or imaging finger movements have the potential to extend the commands of the existing BCI, especially for healthy elderly living. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Neural activities classification of left and right finger gestures during motor execution and motor imagery

Neural activities classification of left and right finger gestures during motor execution and motor imagery

Abstract

In this study, a new paradigm containing motor observation, motor execution, and motor imagery was designed to investigate whether motor imagery (MI) and motor execution (ME) of finger gestures can be used to extend commands of practical mBCIs. The subjects were instructed to perform or imagine 30 left and right finger gestures. Hierarchical support vector machine (hSVM) method was applied to classify four tasks (i.e., ME and MI tasks between left and right gestures). The average...
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Publisher
Taylor & Francis
Copyright
© 2020 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2020.1782124
Publisher site
See Article on Publisher Site

Abstract

In this study, a new paradigm containing motor observation, motor execution, and motor imagery was designed to investigate whether motor imagery (MI) and motor execution (ME) of finger gestures can be used to extend commands of practical mBCIs. The subjects were instructed to perform or imagine 30 left and right finger gestures. Hierarchical support vector machine (hSVM) method was applied to classify four tasks (i.e., ME and MI tasks between left and right gestures). The average classification accuracies of motor imagery and execution tasks using fivefold cross-validation were 90.89 ± 9.87% and 74.08 ± 13.42% in first layer and second layer, respectively. The average accuracy of classification of four classes is 83.06 ± 7.29% overall. These results show that performing or imaging finger movements have the potential to extend the commands of the existing BCI, especially for healthy elderly living.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Oct 2, 2021

Keywords: Brain–Computer Interface (BCI); motor imagery (MI); motor execution (ME); hierarchical support vector machine (hSVM)

References