Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier

Christian Flores; Christian Fonseca; David Achanccaray; Javier Andreu-Perez

doi:10.1109/SMC.2018.00629

Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier

Christian Flores, Christian Fonseca, David Achanccaray, Javier Andreu-Perez

Departamento de Ingeniería Electrónica y Mecatrónica

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

5 Citas (Scopus)

Resumen

In this work, we present the use of Kernel Extreme Learning Machine (Kernel ELM) on electroencephalography EEG brain signals in order to classify the P300 wave during the subject development an oddball paradigm. Also, we propose a selection criteria in order to improve the classification accuracy. In this study, the brain signals of healthy and disabled subjects which suffered a stroke were recorded, analyzed and classified. The results reported that the best classification accuracy and average bitrate were 100% using target by block evaluation and 18.38 bits per minute, respectively. These results are compared to various machine learning algorithms so that our results outperformed them.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	3715-3719
Número de páginas	5
ISBN (versión digital)	9781538666500
DOI	https://doi.org/10.1109/SMC.2018.00629
Estado	Publicada - 2 jul. 2018
Evento	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japón Duración: 7 oct. 2018 → 10 oct. 2018

Serie de la publicación

Nombre	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conferencia

Conferencia	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
País/Territorio	Japón
Ciudad	Miyazaki
Período	7/10/18 → 10/10/18

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10.1109/SMC.2018.00629

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Flores, C., Fonseca, C., Achanccaray, D., & Andreu-Perez, J. (2018). Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier. En Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 3715-3719). Artículo 8616626 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00629

Flores, Christian ; Fonseca, Christian ; Achanccaray, David et al. / Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3715-3719 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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title = "Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier",

abstract = "In this work, we present the use of Kernel Extreme Learning Machine (Kernel ELM) on electroencephalography EEG brain signals in order to classify the P300 wave during the subject development an oddball paradigm. Also, we propose a selection criteria in order to improve the classification accuracy. In this study, the brain signals of healthy and disabled subjects which suffered a stroke were recorded, analyzed and classified. The results reported that the best classification accuracy and average bitrate were 100% using target by block evaluation and 18.38 bits per minute, respectively. These results are compared to various machine learning algorithms so that our results outperformed them.",

author = "Christian Flores and Christian Fonseca and David Achanccaray and Javier Andreu-Perez",

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Flores, C, Fonseca, C, Achanccaray, D & Andreu-Perez, J 2018, Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier. En Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616626, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3715-3719, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japón, 7/10/18. https://doi.org/10.1109/SMC.2018.00629

Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier. / Flores, Christian; Fonseca, Christian; Achanccaray, David et al.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3715-3719 8616626 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier

AU - Flores, Christian

AU - Fonseca, Christian

AU - Achanccaray, David

AU - Andreu-Perez, Javier

PY - 2018/7/2

Y1 - 2018/7/2

N2 - In this work, we present the use of Kernel Extreme Learning Machine (Kernel ELM) on electroencephalography EEG brain signals in order to classify the P300 wave during the subject development an oddball paradigm. Also, we propose a selection criteria in order to improve the classification accuracy. In this study, the brain signals of healthy and disabled subjects which suffered a stroke were recorded, analyzed and classified. The results reported that the best classification accuracy and average bitrate were 100% using target by block evaluation and 18.38 bits per minute, respectively. These results are compared to various machine learning algorithms so that our results outperformed them.

AB - In this work, we present the use of Kernel Extreme Learning Machine (Kernel ELM) on electroencephalography EEG brain signals in order to classify the P300 wave during the subject development an oddball paradigm. Also, we propose a selection criteria in order to improve the classification accuracy. In this study, the brain signals of healthy and disabled subjects which suffered a stroke were recorded, analyzed and classified. The results reported that the best classification accuracy and average bitrate were 100% using target by block evaluation and 18.38 bits per minute, respectively. These results are compared to various machine learning algorithms so that our results outperformed them.

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Flores C, Fonseca C, Achanccaray D, Andreu-Perez J. Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier. En Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3715-3719. 8616626. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00629

Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier

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