Title: An Application of Feature Selection to On-Line P300 Detection in Brain-Computer Interface
Authors: Chumerin, Nikolay ×
Manyakov, Nikolay V.
Combaz, Adrien
Suykens, Johan
Yazicioglu, RF
Torfs, T
Merken, P
Neves, HP
Van Hoof, Chris
Van Hulle, Marc #
Issue Date: Sep-2009
Host Document: Proc. of IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009) pages:1-6
Conference: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) location:Grenoble, France date:2-4 September 2009
Abstract: 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.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
ESAT - MICAS, Microelectronics and Sensors
Research Group Neurophysiology
× corresponding author
# (joint) last author

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