An Efficient P300-based Brain-Computer Interface for Disabled Subjects

J Neurosci Methods. 2008 Jan 15;167(1):115-25. doi: 10.1016/j.jneumeth.2007.03.005. Epub 2007 Mar 13.

Abstract

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain / physiopathology*
  • Brain Diseases / physiopathology*
  • Brain Mapping
  • Disabled Persons*
  • Electroencephalography
  • Event-Related Potentials, P300*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Numerical Analysis, Computer-Assisted*
  • Photic Stimulation / methods
  • Reaction Time
  • User-Computer Interface*