NDLI: A Comparison of Neural Feature Extraction Methods for Brain-Machine Interfaces

Content Provider	IEEE Xplore Digital Library
Author	Gilmour, T.P. Krishnan, L. Gaumond, R.P. Clement, R.S.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA (Gilmour, T.P.)
Abstract	Brain-machine interfaces (BMIs) have shown promise in augmenting people's control of their surroundings, especially for those suffering from paralysis due to neurological disorders. This paper describes an experiment using the rodent model to explore information available in neural signals recorded from chronically implanted intracortical microelectrode arrays. In offline experiments, a number of neural feature extraction methods were utilized to obtain neural activity vectors (NAVs) describing the activity of the underlying neural population while rats performed a discrimination task. The methods evaluated included standard techniques such as binned spike rates and local field potential spectra as well as more novel approaches including matched-filter energy, raw signal spectra, and an autocorrelation energy measure (AEM) approach. Support vector machines (SVMs) were trained offline to classify left from right going movements by utilizing features contained in the NAVs obtained by the different methods. Each method was evaluated for accuracy and robustness. Results show that most algorithms worked well for decoding neural signals both during and prior to movement, with spectral methods providing the best stability
Sponsorship	IEEE EMB
Starting Page	1268
Ending Page	1272
File Size	423945
Page Count	5
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.260518
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Rodents Microelectrodes Rats Measurement standards Potential well Autocorrelation Energy measurement Support vector machines Support vector machine classification
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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