NDLI: Three — Channel electro-encephalogram (EEG) signal analysis by independent component analysis and classification by linear discriminant analysis

Content Provider	IEEE Xplore Digital Library
Author	Mukul, M.K. Matsuno, F.
Copyright Year	2010
Description	Author affiliation: Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo - 182-8585, Japan (Mukul, M.K.) \|\| Department of Mechanical Engineering and Science, Kyoto University, Sakyo-ku, Kyoto, Japan (Matsuno, F.)
Abstract	Electro-encephalogram (EEG) based brain-computer interface (BCI) has aim to associate neurological phenomenon (It is the source of control for any BCI system) with target action (Intentional state). The association with target is highly dependent on signal processing methodology as well as selected features from brain signals. This paper proposes a novel pre-processing method for EEG based BCI system, which includes second order statistics (SOS) based on single time lagged covariance matrix of brain signals. AMUSE algorithm is a part of blind source separation (BSS) algorithm, which includes SOS based on single time lagged covariance matrix. To evaluate the effectiveness of this preprocessing method (AMUSE Filtering) linear discriminant classifier (LDC) is adopted to classify the Graze BCI data set which was used in BCI competitions 2003. The selected feature for classification is power spectral density (PSD) in frequency band 8–30Hz. The performance of a proposed method has been evaluated in the terms of classical evaluation criteria classification accuracy (ACC), error rate (ERR) and modern evaluation criteria mutual Information (MI) and Cohen's Kappa coefficient (k).
Starting Page	449
Ending Page	458
File Size	230371
Page Count	10
File Format	PDF
ISBN	9781424476428
e-ISBN	9784907764364
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Electroencephalography Classification algorithms Covariance matrix Feature extraction Brain modeling Accuracy Algorithm design and analysis Cohen's Kappa coefficient BSS ICA AMUSE Filtering LDA Mutual Information Classification Accuracy
Content Type	Text
Resource Type	Article

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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
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