Chapter

KI 2009: Advances in Artificial Intelligence

Volume 5803 of the series Lecture Notes in Computer Science pp 339-346

P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface

  • Nikolay ChumerinAffiliated withCarnegie Mellon UniversityLaboratorium voor Neuro- en Psychofysiologie, K.U. Leuven
  • , Nikolay V. ManyakovAffiliated withCarnegie Mellon UniversityLaboratorium voor Neuro- en Psychofysiologie, K.U. Leuven
  • , Adrien CombazAffiliated withCarnegie Mellon UniversityLaboratorium voor Neuro- en Psychofysiologie, K.U. Leuven
  • , Johan A. K. SuykensAffiliated withCarnegie Mellon UniversityESAT-SCD, K.U. Leuven
  • , Refet Firat YaziciogluAffiliated withCarnegie Mellon UniversityIMEC
  • , Tom TorfsAffiliated withCarnegie Mellon UniversityIMEC
  • , Patrick MerkenAffiliated withCarnegie Mellon UniversityIMEC
  • , Herc P. NevesAffiliated withCarnegie Mellon UniversityIMEC
  • , Chris Van HoofAffiliated withCarnegie Mellon UniversityIMEC
    • , Marc M. Van HulleAffiliated withCarnegie Mellon UniversityIMEC

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Abstract

We propose a new EEG-based wireless brain computer interface (BCI) with which subjects can “mind-type” text on a computer screen. The application is based on detecting P300 event-related potentials in EEG signals recorded on the scalp of the subject. The BCI uses a simple classifier which relies on a linear feature extraction approach. The accuracy of the presented system is comparable to the state-of-the-art for on-line P300 detection, but with the additional benefit that its much simpler design supports a power-efficient on-chip implementation.