Multi-channel fusion models for the parametric classification of multi-category differential brain activity

Lalit Gupta, Beomsu Chung, Dennis L Molfese

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

This paper introduces multi-channel classification fusion and multi-channel data fusion models to fully exploit the different but complementary brain activity information recorded from multiple channels. The goal is to accurately classify differential brain activity into their respective categories. A parametric weighted classification fusion model and three weighted data fusion models (mixture, sum, and concatenation) are introduced. Parametric classifiers are developed for each fusion strategy and the performances of the different strategies are compared by classifying 14-channel evoked potentials (EPs) collected from subjects involved in making explicit match/mismatch comparisons between sequentially presented stimuli. The best performance is obtained using multi-channel EP concatenation and the performance improves by incorporating weights in the fusion rules. The fusion strategies introduced are also applicable to other problems involving the classification of multi-category multivariate signals generated from multiple sources.

Original languageEnglish (US)
Pages (from-to)940-943
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 II
StatePublished - Dec 1 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Fingerprint

Brain
Bioelectric potentials
Data fusion
Evoked Potentials
Classifiers
Weights and Measures

Keywords

  • Classification fusion
  • Data fusion
  • Evoked potentials
  • Multi-sensor fusion
  • Parametric classification

ASJC Scopus subject areas

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

Cite this

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N2 - This paper introduces multi-channel classification fusion and multi-channel data fusion models to fully exploit the different but complementary brain activity information recorded from multiple channels. The goal is to accurately classify differential brain activity into their respective categories. A parametric weighted classification fusion model and three weighted data fusion models (mixture, sum, and concatenation) are introduced. Parametric classifiers are developed for each fusion strategy and the performances of the different strategies are compared by classifying 14-channel evoked potentials (EPs) collected from subjects involved in making explicit match/mismatch comparisons between sequentially presented stimuli. The best performance is obtained using multi-channel EP concatenation and the performance improves by incorporating weights in the fusion rules. The fusion strategies introduced are also applicable to other problems involving the classification of multi-category multivariate signals generated from multiple sources.

AB - This paper introduces multi-channel classification fusion and multi-channel data fusion models to fully exploit the different but complementary brain activity information recorded from multiple channels. The goal is to accurately classify differential brain activity into their respective categories. A parametric weighted classification fusion model and three weighted data fusion models (mixture, sum, and concatenation) are introduced. Parametric classifiers are developed for each fusion strategy and the performances of the different strategies are compared by classifying 14-channel evoked potentials (EPs) collected from subjects involved in making explicit match/mismatch comparisons between sequentially presented stimuli. The best performance is obtained using multi-channel EP concatenation and the performance improves by incorporating weights in the fusion rules. The fusion strategies introduced are also applicable to other problems involving the classification of multi-category multivariate signals generated from multiple sources.

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