P300 detection based on feature extraction in on-line brain-computer interface

  • Authors:
  • Nikolay Chumerin;Nikolay V. Manyakov;Adrien Combaz;Johan A. K. Suykens;Refet Firat Yazicioglu;Tom Torfs;Patrick Merken;Herc P. Neves;Chris Van Hoof;Marc M. Van Hulle

  • Affiliations:
  • Laboratorium voor Neuro- en Psychofysiologie, K.U. Leuven, Leuven, Belgium;Laboratorium voor Neuro- en Psychofysiologie, K.U. Leuven, Leuven, Belgium;Laboratorium voor Neuro- en Psychofysiologie, K.U. Leuven, Leuven, Belgium;ESAT-SCD, K.U. Leuven, Heverlee, Belgium;IMEC, Leuven, Belgium;IMEC, Leuven, Belgium;IMEC, Leuven, Belgium;IMEC, Leuven, Belgium;IMEC, Leuven, Belgium;IMEC, Leuven, Belgium

  • Venue:
  • KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

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.