Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry
Joint Authors
Guan, Shan
Zhao, Kai
Yang, Shuning
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective.
For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM.
Method 2 includes a feature extraction algorithm and a classification algorithm.
The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane.
And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN.
By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a.
And method 2 also obtains high recognition rate on the other two datasets.
American Psychological Association (APA)
Guan, Shan& Zhao, Kai& Yang, Shuning. 2019. Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129501
Modern Language Association (MLA)
Guan, Shan…[et al.]. Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1129501
American Medical Association (AMA)
Guan, Shan& Zhao, Kai& Yang, Shuning. Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1129501
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129501