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

Srinivas Kota, Phani Yarlagadda, Lalit Gupta, D. L. Molfese

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)2575-2578
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2009
Externally publishedYes

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Bioelectric potentials
Evoked Potentials
Classifiers
Electric fuses
Random variables

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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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.",
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