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

Department of Electrical and Mechatronics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3715-3719
Number of pages	5
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00629
State	Published - 2 Jul 2018
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

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

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

Access to Document

10.1109/SMC.2018.00629

Cite this

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. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 3715-3719). Article 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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year = "2018",

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language = "English",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

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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. in 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, Japan, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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PY - 2018/7/2

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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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M3 - Conference contribution

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

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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. In 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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