NDLI: Central-tendency estimation and nearest-estimate classification of multi-channel evoked potentials

Content Provider	IEEE Xplore Digital Library
Author	Kota, S. Yarlagadda, P. Gupta, L. Molfese, D.L.
Copyright Year	2009
Description	Author affiliation: Department of Electrical & Computer Engineering, Southern Illinois University, Carbondale, IL 62901 (Kota, S.; Yarlagadda, P.; Gupta, L.) \|\| Birth Defects Center, University of Louisville, Louisville, KY 40292 (Molfese, D.L.)
Abstract	By modeling evoked potentials (EPs) as random vectors in which the EP samples are random variables, a generalized strategy is introduced to determine multivariate central-tendency estimates such as the arithmetic mean, geometric mean, harmonic mean, median, tri-mean, and trimmed-mean. Additionally, a generalized strategy is introduced to develop minimum-distance classifiers based on central tendency estimates. Furthermore, procedures are developed to fuse the decisions of the nearest-estimate classifiers for multi-channel EP classification. The central-tendency estimates of real EPs are compared and it is shown that although the mathematical operations to compute the estimates are quite different, the EP estimates are similar with respect to their overall waveform shapes and latencies. It is also shown that by fusing the classifier decisions across multiple channels, the classification accuracy can be improved significantly when compared with the accuracies of individual channel classifiers.
Starting Page	2575
Ending Page	2578
File Size	356515
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5335281
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Arithmetic Random variables Delay Enterprise resource planning USA Councils Solid modeling Fuses Shape Humans Cognition Evoked potentials Central tendency estimation EP averaging EP classification
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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