Identifying informative features for ERP speller systems based on RSVP paradigm

Tian Lan, Deniz Erdogmus, Lois Black, Jan Van Santen

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

2 Scopus citations

Abstract

This preliminary study focused on identifying informative features in the frequency and spatial domains for single-trial Event Related Potential (ERP) detection for ERP spelling systems. A predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A Linear Discriminant Analysis (LDA) classifier was selected as ERP detector for its simplicity and robustness. A range of features in different frequency bands and EEG channel subsets was extracted and detection accuracies were compared for different classes of features.

Original languageEnglish (US)
Title of host publicationProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages351-356
Number of pages6
StatePublished - Dec 1 2010
Event18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - Bruges, Belgium
Duration: Apr 28 2010Apr 30 2010

Publication series

NameProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

Other

Other18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010
CountryBelgium
CityBruges
Period4/28/104/30/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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  • Cite this

    Lan, T., Erdogmus, D., Black, L., & Van Santen, J. (2010). Identifying informative features for ERP speller systems based on RSVP paradigm. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (pp. 351-356). (Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010).