Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

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...

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Veröffentlicht in:Computational and mathematical methods in medicine Jg. 2018; H. 2018; S. 1 - 9
Hauptverfasser: Zhang, Pengju, Liu, Shucong, Zheng, Dezhi, Dai, Mengxi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
Schlagworte:
Algorithms
Brain-Computer Interfaces
Electroencephalography
Humans
Imagery (Psychotherapy)
Learning
ISSN:1748-670X, 1748-6718, 1748-6718
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Marta Parazzini
ISSN:1748-670X
1748-6718
1748-6718
DOI:10.1155/2018/9871603