An application of feature selection to on-line P300 detection in brain-computer interface

We propose a new EEG-based wireless brain computer interface (BCI) with which subjects can ldquomind-typerdquo 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 linear classifier which takes as input a set of simple amplitude-based features that are optimally selected using the group method of data handling (GMDH) feature selection procedure. The accuracy of the presented system is comparable to the state-of-the-art systems for on-line P300 detection, but with the additional benefit that its much simpler design supports a power-efficient on-chip implementation. ispartof: pages:1-6 ispartof: Proc. of IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009) pages:1-6 ispartof: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) location:Grenoble, France date:2 Sep - 4 Sep 2009 status: published