A1 Journal article (refereed), original research

A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications

Open Access hybrid publication

Publication Details
Authors: Hekmatmanesh Amin, Wu Huapeng, Jamaloo Fatemeh, Li Ming, Handroos Heikki
Publisher: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals
Publication year: 2020
Language: English
Related Journal or Series Information: Multimedia Tools and Applications
Volume number: 79
Issue number: 25-26
Start page: 17521
End page: 17549
Number of pages: 29
ISSN: 1380-7501
JUFO-Level of this publication: 1
Open Access: Open Access hybrid publication


Several methods utilizing common spatial pattern (CSP) algorithm have been presented for
improving the identification of imagery movement patterns for brain computer interface
applications. The present study focuses on improving a CSP-based algorithm for detecting the motor imagery movement patterns. A discriminative filter bank of CSP method
using a discriminative sensitive learning vector quantization (DFBCSP-DSLVQ) system is
implemented. Four algorithms are then combined to form three methods for improving the
efficiency of the DFBCSP-DSLVQ method, namely the kernel linear discriminant analysis
(KLDA), the kernel principal component analysis (KPCA), the soft margin support vector
machine (SSVM) classifier and the generalized radial bases functions (GRBF) kernel. The
GRBF is used as a kernel for the KLDA, the KPCA feature selection algorithms and the
SSVM classifier. In addition, three types of classifiers, namely K-nearest neighbor (K-NN),
neural network (NN) and traditional support vector machine (SVM), are employed to evaluate the efficiency of the classifiers. Results show that the best algorithm is the combination of the DFBCSP-DSLVQ method using the SSVM classifier with GRBF kernel (SSVMGRBF), in which the best average accuracy, attained are 92.70% and 83.21%, respectively.
Results of the Repeated Measures ANOVA shows the statistically significant dominance of
this method at p < 0.05. The presented algorithms are then compared with the base algorithm of this study i.e. the DFBCSP-DSLVQ with the SVM-RBF classifier. It is concluded
that the algorithms, which are based on the SSVM-GRBF classifier and the KLDA with the
SSVM-GRBF classifiers give sufficient accuracy and reliable results.

Research Areas

Last updated on 2020-24-11 at 07:40