NDLI: EEG signal classification

Content Provider	IEEE Xplore Digital Library
Author	Stastny, J. Sovka, P. Stancak, A.
Copyright Year	2001
Description	Author affiliation: Fac. of Electr. Eng., Czech Tech. Univ., Prague, Czech Republic (Stastny, J.; Sovka, P.)
Abstract	The article describes the classification of simple movements using a system based on Hidden Markov Models (HMM). Brisk extensions and flexions of the index finger, and movements of the proximal arm (shoulder) and distal arm (finger) were classified using scalp EEG signals. The aim of our study was to develop a system for the classification of movements which show EEG changes at identical scalp electrodes of one hemisphere. The classification of EEG patterns related to movements of one hand is difficult because the disentanglement of movements can only rely on the temporal evolution of EEG changes at one recording site. A large variability-of EEG waveforms requires the use of the context information. The classification procedure was optimized in all parts to increase the recognition score and it was extensively tested on a set of EEG data. The average classification score was 80%, std. deviation 9% for the classification of distal and proximal movements. The classification of extension/flexion reached even better results (due to more accurate localization of the signal source on the scalp). The classification of movement-related EEG data based on HMM yielded higher recognition scores than previously reported classification scores based on artificial neural networks.
Starting Page	2020
Ending Page	2023
File Size	444932
Page Count	4
File Format	PDF
ISBN	0780372115
ISSN	1094687X
DOI	10.1109/IEMBS.2001.1020628
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-10-25
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Pattern classification Hidden Markov models Scalp Electrodes Pattern recognition Speech recognition Testing Shape Physiology
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

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