期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 167, 期 1, 页码 115-125出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2007.03.005
关键词
brain-computer interface; P300; disabled subjects; Fisher's linear discriminant analysis; Bayesian linear discriminant analysis
A brain-computer interface (130) 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 25 bits/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. (C) 2007 Elsevier B.V. All rights reserved.
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