Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding

Konstantin Sonkin; Lev Stankevich; Yulia Khomenko; Zhanna Nagornova; Natalia Shemyakina; Alexandra Koval; Dmitry Perets

doi:10.1007/978-3-319-40663-3_12

Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding

Konstantin Sonkin^*, Lev Stankevich, Yulia Khomenko, Zhanna Nagornova, Natalia Shemyakina, Alexandra Koval, Dmitry Perets

^*Corresponding author for this work

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

Abstract

This study aimed to finding effective approaches for electroencephalographic (EEG) multiclass classification of imaginary movements. The combined classifier of EEG signals based on artificial neural network (ANN) and support vector machine (SVM) algorithms was applied. Effectiveness of the classifier was shown in 4-class imaginary finger movement classification. Nine right-handed subjects participated in the study. The mean decoding accuracy using combined heterogeneous classifier committee was −60 ± 10%, max: 77 ± 5%, while application of homogeneous classifier based on committee of ANNs −52 ± 9% and 65 ± 5% correspondingly. This work supports the feasibility of the approach, which is presumed suitable for imaginary movements decoding of four fingers of one hand. These results could be used for development of effective non-invasive BCI with enlarged amount of degrees of freedom.

Original language	English
Title of host publication	Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings
Editors	Long Cheng, Qingshan Liu, Andrey Ronzhin
Publisher	Springer Verlag
Pages	100-107
Number of pages	8
ISBN (Print)	9783319406626
DOIs	https://doi.org/10.1007/978-3-319-40663-3_12
State	Published - 2016
Externally published	Yes
Event	13th International Symposium on Neural Networks, ISNN 2016 - St. Petersburg, Russian Federation Duration: 6 Jul 2016 → 8 Jul 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9719
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Symposium on Neural Networks, ISNN 2016
Country/Territory	Russian Federation
City	St. Petersburg
Period	6/07/16 → 8/07/16

Keywords

Artificial neural network
Classifier committee
Electroencephalography
Imaginary finger movements
Support vector machine

Access to Document

10.1007/978-3-319-40663-3_12

Cite this

Sonkin, K., Stankevich, L., Khomenko, Y., Nagornova, Z., Shemyakina, N., Koval, A., & Perets, D. (2016). Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding. In L. Cheng, Q. Liu, & A. Ronzhin (Eds.), Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings (pp. 100-107). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9719). Springer Verlag. https://doi.org/10.1007/978-3-319-40663-3_12

Sonkin, Konstantin ; Stankevich, Lev ; Khomenko, Yulia et al. / Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding. Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings. editor / Long Cheng ; Qingshan Liu ; Andrey Ronzhin. Springer Verlag, 2016. pp. 100-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding",

abstract = "This study aimed to finding effective approaches for electroencephalographic (EEG) multiclass classification of imaginary movements. The combined classifier of EEG signals based on artificial neural network (ANN) and support vector machine (SVM) algorithms was applied. Effectiveness of the classifier was shown in 4-class imaginary finger movement classification. Nine right-handed subjects participated in the study. The mean decoding accuracy using combined heterogeneous classifier committee was −60 ± 10%, max: 77 ± 5%, while application of homogeneous classifier based on committee of ANNs −52 ± 9% and 65 ± 5% correspondingly. This work supports the feasibility of the approach, which is presumed suitable for imaginary movements decoding of four fingers of one hand. These results could be used for development of effective non-invasive BCI with enlarged amount of degrees of freedom.",

keywords = "Artificial neural network, Classifier committee, Electroencephalography, Imaginary finger movements, Support vector machine",

author = "Konstantin Sonkin and Lev Stankevich and Yulia Khomenko and Zhanna Nagornova and Natalia Shemyakina and Alexandra Koval and Dmitry Perets",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 13th International Symposium on Neural Networks, ISNN 2016 ; Conference date: 06-07-2016 Through 08-07-2016",

year = "2016",

doi = "10.1007/978-3-319-40663-3_12",

language = "אנגלית",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Sonkin, K, Stankevich, L, Khomenko, Y, Nagornova, Z, Shemyakina, N, Koval, A & Perets, D 2016, Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding. in L Cheng, Q Liu & A Ronzhin (eds), Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9719, Springer Verlag, pp. 100-107, 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russian Federation, 6/07/16. https://doi.org/10.1007/978-3-319-40663-3_12

Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding. / Sonkin, Konstantin; Stankevich, Lev; Khomenko, Yulia et al.
Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings. ed. / Long Cheng; Qingshan Liu; Andrey Ronzhin. Springer Verlag, 2016. p. 100-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9719).

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

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AU - Sonkin, Konstantin

AU - Stankevich, Lev

AU - Khomenko, Yulia

AU - Nagornova, Zhanna

AU - Shemyakina, Natalia

AU - Koval, Alexandra

AU - Perets, Dmitry

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - This study aimed to finding effective approaches for electroencephalographic (EEG) multiclass classification of imaginary movements. The combined classifier of EEG signals based on artificial neural network (ANN) and support vector machine (SVM) algorithms was applied. Effectiveness of the classifier was shown in 4-class imaginary finger movement classification. Nine right-handed subjects participated in the study. The mean decoding accuracy using combined heterogeneous classifier committee was −60 ± 10%, max: 77 ± 5%, while application of homogeneous classifier based on committee of ANNs −52 ± 9% and 65 ± 5% correspondingly. This work supports the feasibility of the approach, which is presumed suitable for imaginary movements decoding of four fingers of one hand. These results could be used for development of effective non-invasive BCI with enlarged amount of degrees of freedom.

AB - This study aimed to finding effective approaches for electroencephalographic (EEG) multiclass classification of imaginary movements. The combined classifier of EEG signals based on artificial neural network (ANN) and support vector machine (SVM) algorithms was applied. Effectiveness of the classifier was shown in 4-class imaginary finger movement classification. Nine right-handed subjects participated in the study. The mean decoding accuracy using combined heterogeneous classifier committee was −60 ± 10%, max: 77 ± 5%, while application of homogeneous classifier based on committee of ANNs −52 ± 9% and 65 ± 5% correspondingly. This work supports the feasibility of the approach, which is presumed suitable for imaginary movements decoding of four fingers of one hand. These results could be used for development of effective non-invasive BCI with enlarged amount of degrees of freedom.

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KW - Classifier committee

KW - Electroencephalography

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KW - Support vector machine

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ER -

Sonkin K, Stankevich L, Khomenko Y, Nagornova Z, Shemyakina N, Koval A et al. Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding. In Cheng L, Liu Q, Ronzhin A, editors, Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings. Springer Verlag. 2016. p. 100-107. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-40663-3_12

Neurological classifier committee based on artificial neural networks and support vector machine for single-trial EEG signal decoding

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