Comparison of linear and nonlinear approaches on single trial ERP detection in rapid serial visual presentation tasks

Yonghong Huang, Deniz Erdogmus, Santosh Mathan, Misha Pavel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

In this paper, we describe a system for detecting encephalography (EEG) signatures of visual recognition events evoked in a single trial during rapid serial visual presentation (RSVP). In order to investigate the viability of nonlinear approaches in EEG detection and assess the performance comparison, we applied three classifiers (linear logistic regression model, Laplacian classifier, and spectral maximum mutual information projection) in the detection tasks. The EEG was recorded using 32 electrodes during the rapid image presentation (50ms/100ms per image). Subjects were instructed to push a button when they recognize a target image. The results suggest that while the detection of single trial EEG-based recognition is possible, taking advantage of the nonlinear techniques requires data representation that would overcome the non-stationarity of the EEG signals.

Original languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages1136-1142
Number of pages7
StatePublished - Dec 1 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

ASJC Scopus subject areas

  • Software

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    Huang, Y., Erdogmus, D., Mathan, S., & Pavel, M. (2006). Comparison of linear and nonlinear approaches on single trial ERP detection in rapid serial visual presentation tasks. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 1136-1142). [1716229] (IEEE International Conference on Neural Networks - Conference Proceedings).