NDLI: A New Classification Method Based on KF-SVM in Brain Computer Interfaces

Content Provider	IEEE Xplore Digital Library
Author	Yang Banghua Han Zhijun Wang Qian He Liangfei
Copyright Year	2013
Description	Author affiliation: Dept. of Autom., Shanghai Univ., Shanghai, China (Yang Banghua; Han Zhijun; Wang Qian; He Liangfei)
Abstract	This paper proposes a novel classification method named KF-SVM (Kernel Fisher, Support Vector Machine), which is used for the EEG (Electroencephalography) classification of two classes of imagery data in BCIs (brain-computer interfaces). This method combines the kernel fisher and SVM. Its detailed process is as follows: First, the CSP (Common Spatial Patterns) is used to obtain features, and then the within-class scatter is calculated based on these features. The scatter is added into the RBF (Radical Basis Function) kernel function to construct a new kernel function. The obtained new kernel is integrated into the support vector machine to get a new classification model. The KF-SVM may overcome the following defects of the SVM: 1) the SVM maximizes the classification margin without considering within-class scatter. 2) The classification surface of the SVM between two types of EEG data only depends on boundary samples and misclassified samples. To evaluate effectiveness of the proposed KF-SVM method, the data from the 2008 international BCI competition and experiments of our laboratory are processed. The experimental result shows that the proposed KF-SVM classification algorithm can well classify EEG data and improve the correct rate of EEG recognition in BCIs.
Sponsorship	Nanjing Sect. CIS Chapt.
Starting Page	193
Ending Page	196
File Size	195727
Page Count	4
File Format	PDF
ISBN	9780769550794
DOI	10.1109/ISCID.2013.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines SVM (Support Vector Machine) Accuracy CSP (Common Spatial Patterns) BCI (Brain Computer Interface) Feature extraction Electroencephalography Classification algorithms Kernel Kernel Fisher Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in