1. An application of feature selection to on-line P300 detection in brain-computer interface
- Author
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Chumerin, Nikolay, Manyakov, Nikolay V, Combaz, Adrien, Suykens, Johan, Yazicioglu, RF, Torfs, T, Merken, P, Neves, HP, Van Hoof, Chris, and Van Hulle, Marc
- Subjects
Computer science ,Group method of data handling ,Feature extraction ,power-efficient on-chip implementation ,Feature selection ,Linear classifier ,Electroencephalography ,event-related potentials ,Synchronization ,Set (abstract data type) ,brain-computer interfaces ,feature selection ,on-line P300 detection ,linear classifier ,EEG signals ,medicine ,wireless brain computer interface ,medical signal processing ,group method-of-data handling ,Brain–computer interface ,signal classification ,data recording ,medicine.diagnostic_test ,business.industry ,feature extraction ,Pattern recognition ,Artificial intelligence ,business ,electroencephalography - 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. 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
- Published
- 2009
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