NDLI: Identifying Local Ultrametricity of EEG Time Series for Feature Extraction in a Brain-Computer Interface

Content Provider	IEEE Xplore Digital Library
Author	Coyle, D. McGinnity, T.M. Prasad, G.
Copyright Year	2007
Description	Author affiliation: Univ. of Ulster, Ulster (Coyle, D.; McGinnity, T.M.; Prasad, G.)
Abstract	The accurate discrimination of EEG times-series is a challenging problem and has become a topic of prominent research interest, given the extent of the research activity in the area of brain-computer interface (BCI) technology. Many signal processing algorithms involving preprocessing, feature extraction/selection, and classification have been deployed and yet, the most appropriate and robust solutions are still being sought. This paper presents an analysis of a new methodology for feature extraction in a BCI which is based on identifying the extent of ultrametricity from EEG time-series. This work is inspired by the idea that there are natural, not necessarily unique, tree or hierarchy structures defined by the ultrametric topology of EEG time-series. The objective is to determine if coefficients which reflect the extent of ultrametricity can be used as distinct features of different EEG time series, recorded whilst subjects imagine left/right hand movements (motor imagery(MI)). The results show that MI based EEG time-series can be separated using a local ultrametricity quantifier and a linear discriminant classifier or Bayes classifier. Also, it is shown that neural-time-series-prediction-preprocessing (NTSPP) produces a higher dimensional space in which local ultrametricity is more separable for two classes of EEG signals.
Starting Page	701
Ending Page	704
File Size	356964
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4352387
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Feature extraction Brain computer interfaces Extraterrestrial measurements Time series analysis Robustness Signal processing Speech Handicapped aids Communication standards
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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
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