EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials
Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portab...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/350270 |
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| author | Alejandro Gonzalez Isao Nambu Haruhide Hokari Yasuhiro Wada |
| author_facet | Alejandro Gonzalez Isao Nambu Haruhide Hokari Yasuhiro Wada |
| author_sort | Alejandro Gonzalez |
| collection | DOAJ |
| description | Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy. |
| format | Article |
| id | doaj-art-2fb35e6a76f44c84b6dbf598a2f09382 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2fb35e6a76f44c84b6dbf598a2f093822025-02-03T06:01:36ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/350270350270EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related PotentialsAlejandro Gonzalez0Isao Nambu1Haruhide Hokari2Yasuhiro Wada3Department of Electrical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, JapanDepartment of Electrical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, JapanDepartment of Electrical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, JapanDepartment of Electrical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, JapanBrain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.http://dx.doi.org/10.1155/2014/350270 |
| spellingShingle | Alejandro Gonzalez Isao Nambu Haruhide Hokari Yasuhiro Wada EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials The Scientific World Journal |
| title | EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials |
| title_full | EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials |
| title_fullStr | EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials |
| title_full_unstemmed | EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials |
| title_short | EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials |
| title_sort | eeg channel selection using particle swarm optimization for the classification of auditory event related potentials |
| url | http://dx.doi.org/10.1155/2014/350270 |
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