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 |
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Rok vydání: | 2009 |
Předmět: |
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 |
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 |
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