NDLI: A Comparison of Time, Frequency and ICA Based Features and Five Classifiers for Wrist Movement Classification in EEG Signals

Content Provider	IEEE Xplore Digital Library
Author	Navarro, I. Sepulveda, F. Hubais, B.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Essex Univ., Colchester (Navarro, I.; Sepulveda, F.; Hubais, B.)
Abstract	This study presents a comparison of two methods to extract features for the classification of wrist movements (flexion, extension, pronation, supination). For the first method, a set of 160 features was extracted from the filtered time and frequency domain EEG data and its alpha, beta, and theta bands. For the second method, a set of 40 features per movement type was extracted from the ICA-calculated source signals. The value of the Davies-Bouldin cluster separation index for each feature was used for selecting the best five features from each set so as to avoid the subjective selection or rejection of any of the features. Finally, five different kinds of classifiers were chosen to obtain classification error rates with which to compare both techniques. The results showed the advantage of using ICA source signals for wrist movement classification purposes, at least as compared to the simple time and frequency domain features. Left and right movements were correctly identified with accuracies ranging from 70% to 96%. However, the methodology presented here did not succeed in distinguishing the subclasses (e.g., flexion versus extension) with accuracy above 70%. This suggests that additional work is needed to explore different features as well as classifiers
Starting Page	2118
Ending Page	2121
File Size	352612
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1616878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent component analysis Wrist Electroencephalography Data mining Computer science Feature extraction Frequency domain analysis Error analysis Spinal cord injury Process design Movement-Related Potentials Brain-Computer Interface ICA Feature Selection EEG
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
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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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