An efficient P300-based brain-computer interface for disabled subjects.
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.
Hoffmann
Ulrich
U
Ecole Polytechnique Fédérale de Lausanne, Signal Processing Institute, CH-1015 Lausanne, Switzerland. ulrich.hoffmann@epfl.ch
Vesin
Jean-Marc
JM
Ebrahimi
Touradj
T
Diserens
Karin
K
D000328:Adult; D001921:Brain / Q000503:physiopathology*; D001927:Brain Diseases / Q000503:physiopathology*; D001931:Brain Mapping; D006233:Persons with Disabilities; D004569:Electroencephalography; D018913:Event-Related Potentials, P300; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009716:Numerical Analysis, Computer-Assisted; D010775:Photic Stimulation / Q000379:methods; D011930:Reaction Time; D014584:User-Computer Interface