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Recognition of motor imagery EEG patterns based on common feature analysis

Recognition of motor imagery EEG patterns based on common feature analysis Motor imagery (MI) is particularly attractive in brain-computer interface (BCI) in the sense that it does not need any external stimuli. However, the overall performance is often severely affected by subject’s mental states. In this study, a method based on common feature analysis (CFA) was proposed for MI electroencephalogram (EEG) patterns recognition, which can not only improve the recognition accuracy but also help to find  reliable and interpretable features associated with specific MI patterns. Evaluation using several open competition datasets justifies that the common features could more accurately identify MI characteristics and hence substantially benefit MI EEG patterns recognition.  http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Recognition of motor imagery EEG patterns based on common feature analysis

Brain-Computer Interfaces , Volume 8 (4): 9 – Oct 2, 2021

Recognition of motor imagery EEG patterns based on common feature analysis

Abstract

Motor imagery (MI) is particularly attractive in brain-computer interface (BCI) in the sense that it does not need any external stimuli. However, the overall performance is often severely affected by subject’s mental states. In this study, a method based on common feature analysis (CFA) was proposed for MI electroencephalogram (EEG) patterns recognition, which can not only improve the recognition accuracy but also help to find  reliable and interpretable features associated with...
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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.2020.1783170
Publisher site
See Article on Publisher Site

Abstract

Motor imagery (MI) is particularly attractive in brain-computer interface (BCI) in the sense that it does not need any external stimuli. However, the overall performance is often severely affected by subject’s mental states. In this study, a method based on common feature analysis (CFA) was proposed for MI electroencephalogram (EEG) patterns recognition, which can not only improve the recognition accuracy but also help to find  reliable and interpretable features associated with specific MI patterns. Evaluation using several open competition datasets justifies that the common features could more accurately identify MI characteristics and hence substantially benefit MI EEG patterns recognition. 

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Oct 2, 2021

Keywords: Brain-computer interface; motor imagery; common feature analysis; tensor decomposition

References