A brain-computer interface based on mental tasks with a zero false activation rate

Abstract

Most brain-computer interface applications in real-life suffer from the high rate of false activations. The ultimate goal when designing brain-computer interfaces is to reach the zero false activation rate while the true activation rate is kept at a high level. In this study, a brain-computer interface design is shown to have a zero false activation rate. The interface is based on different mental tasks. It is custom designed to every subject and to every mental task. The most discriminatory mental task for each subject is determined. We use the autoregressive modeling as the feature extraction method. The classification is performed by a radial basis function neural network. The EEG signals of four subjects during five mental tasks are used. The order of autoregressive model is varied from 2 to 20 and custom designed for each mental task and each subject in the cross-validation stage. The performance of the brain-computer interfaces based on the most discriminatory mental tasks is shown to be highly promising since the false positive rate reaches zero while the mean of the true positive rate obtained is above 70%.