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Evoked Potentials Estimation in Brain-Computer Interface Using Support Vector Machine

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Rough Sets and Knowledge Technology (RSKT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4062))

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Abstract

The single-trial Visual Evoked Potentials estimation of brain-computer interface was investigated. Communication carriers between brain and computer were induced by ”imitating-human-natural-reading” paradigm. With carefully signal preprocess and feature selection procedure, we explored the single-trial estimation of EEG using ν-support vector machines in six subjects, and by comparison the results using P300 features from channel Fz and Pz, gained a satisfied classification accuracy of 91.3%, 88.9%, 91.5%, 92.1%, 90.2% and 90.1% respectively. The result suggests that the experimental paradigm is feasible and the speed of our mental speller can be boosted.

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, South-Central University for Nationalities, Wuhan, 430074, China

    Jin-an Guan

Authors
  1. Jin-an Guan
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Editors and Affiliations

  1. College of Computer Science and Technology, Chongqing, University of Posts and Telecommunication, 400065, Chongqing, P.R. China

    Guo-Ying Wang

  2. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Department of Computer Science, University of Regina, Regina,, S4S 0A2, Saskatchewan, Canada

    Yiyu Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Guan, Ja. (2006). Evoked Potentials Estimation in Brain-Computer Interface Using Support Vector Machine. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_102

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  • DOI: https://doi.org/10.1007/11795131_102

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36297-5

  • Online ISBN: 978-3-540-36299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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