Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification
Joint Authors
Dai, Mengxi
Zheng, Dezhi
Liu, Shucong
Zhang, Pengju
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-18
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification.
The CSP method is a supervised algorithm.
Therefore a lot of time-consuming training data is needed to build the model.
To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task.
To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects.
The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods.
In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method.
The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples.
Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.
American Psychological Association (APA)
Dai, Mengxi& Zheng, Dezhi& Liu, Shucong& Zhang, Pengju. 2018. Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1132286
Modern Language Association (MLA)
Dai, Mengxi…[et al.]. Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1132286
American Medical Association (AMA)
Dai, Mengxi& Zheng, Dezhi& Liu, Shucong& Zhang, Pengju. Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1132286
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132286