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

Autor: Chumerin, Nikolay, Manyakov, Nikolay V, Combaz, Adrien, Suykens, Johan, Yazicioglu, RF, Torfs, T, Merken, P, Neves, HP, Van Hoof, Chris, Van Hulle, Marc
Rok vydání: 2009
Předmět:
Zdroj: 2009 IEEE International Workshop on Machine Learning for Signal Processing.
DOI: 10.1109/mlsp.2009.5306244
Popis: 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
Databáze: OpenAIRE