EEG signal classification using neural network and support vector machine in brain computer interface

M. M. El Bahy; M. Hosny; Wael A. Mohamed; Shawky Ibrahim

doi:10.1007/978-3-319-48308-5_24

EEG signal classification using neural network and support vector machine in brain computer interface

M. M. El Bahy, M. Hosny, Wael A. Mohamed^*, Shawky Ibrahim

^*Corresponding author for this work

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

22 Scopus citations

Abstract

Classification of EEG signals is one of the biggest problems in Brain Computer Interface (BCI) systems. This paper presents a BCI system based on using the EEG signals associated with five mental tasks (baseline, math, mental letter composing, geometric figure rotation and visual counting). EEG data for these five cognitive tasks from one subject were taken from the Colorado University database. Wavelet Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis (PCA) were used for features extraction. Artificial Neural Network (ANN) trained by a standard back propagation algorithm and Support Vector Machines (SVMs) were used for classifying different combinations mental tasks. Experimental results show the classification accuracies achieved with the three used feature extraction techniques and the two classification techniques.

Original language	English
Title of host publication	Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016
Editors	Aboul Ella Hassanien, Khaled Shaalan, Ahmad Taher Azar, Tarek Gaber, Mohamed F. Tolba
Publisher	Springer Verlag
Pages	246-256
Number of pages	11
ISBN (Print)	9783319483078
DOIs	https://doi.org/10.1007/978-3-319-48308-5_24
State	Published - 2017
Externally published	Yes
Event	2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016 - Cairo, Egypt Duration: 24 Oct 2016 → 26 Oct 2016

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	533
ISSN (Print)	2194-5357

Conference

Conference	2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016
Country/Territory	Egypt
City	Cairo
Period	24/10/16 → 26/10/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Artificial neural network (ANN)
Brain computer interface (BCI)
Support vector machine (SVM)

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-319-48308-5_24

Cite this

El Bahy, M. M., Hosny, M., Mohamed, W. A., & Ibrahim, S. (2017). EEG signal classification using neural network and support vector machine in brain computer interface. In A. E. Hassanien, K. Shaalan, A. T. Azar, T. Gaber, & M. F. Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016 (pp. 246-256). (Advances in Intelligent Systems and Computing; Vol. 533). Springer Verlag. https://doi.org/10.1007/978-3-319-48308-5_24

El Bahy, M. M. ; Hosny, M. ; Mohamed, Wael A. et al. / EEG signal classification using neural network and support vector machine in brain computer interface. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016. editor / Aboul Ella Hassanien ; Khaled Shaalan ; Ahmad Taher Azar ; Tarek Gaber ; Mohamed F. Tolba. Springer Verlag, 2017. pp. 246-256 (Advances in Intelligent Systems and Computing).

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title = "EEG signal classification using neural network and support vector machine in brain computer interface",

abstract = "Classification of EEG signals is one of the biggest problems in Brain Computer Interface (BCI) systems. This paper presents a BCI system based on using the EEG signals associated with five mental tasks (baseline, math, mental letter composing, geometric figure rotation and visual counting). EEG data for these five cognitive tasks from one subject were taken from the Colorado University database. Wavelet Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis (PCA) were used for features extraction. Artificial Neural Network (ANN) trained by a standard back propagation algorithm and Support Vector Machines (SVMs) were used for classifying different combinations mental tasks. Experimental results show the classification accuracies achieved with the three used feature extraction techniques and the two classification techniques.",

keywords = "Artificial neural network (ANN), Brain computer interface (BCI), Support vector machine (SVM)",

author = "\{El Bahy\}, \{M. M.\} and M. Hosny and Mohamed, \{Wael A.\} and Shawky Ibrahim",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016 ; Conference date: 24-10-2016 Through 26-10-2016",

year = "2017",

doi = "10.1007/978-3-319-48308-5\_24",

language = "English",

isbn = "9783319483078",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "246--256",

editor = "Hassanien, \{Aboul Ella\} and Khaled Shaalan and Azar, \{Ahmad Taher\} and Tarek Gaber and Tolba, \{Mohamed F.\}",

booktitle = "Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016",

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El Bahy, MM, Hosny, M, Mohamed, WA & Ibrahim, S 2017, EEG signal classification using neural network and support vector machine in brain computer interface. in AE Hassanien, K Shaalan, AT Azar, T Gaber & MF Tolba (eds), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016. Advances in Intelligent Systems and Computing, vol. 533, Springer Verlag, pp. 246-256, 2nd International Conference on Advanced Intelligent Systems and Informatics, AISI 2016, Cairo, Egypt, 24/10/16. https://doi.org/10.1007/978-3-319-48308-5_24

EEG signal classification using neural network and support vector machine in brain computer interface. / El Bahy, M. M.; Hosny, M.; Mohamed, Wael A. et al.
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016. ed. / Aboul Ella Hassanien; Khaled Shaalan; Ahmad Taher Azar; Tarek Gaber; Mohamed F. Tolba. Springer Verlag, 2017. p. 246-256 (Advances in Intelligent Systems and Computing; Vol. 533).

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

TY - GEN

T1 - EEG signal classification using neural network and support vector machine in brain computer interface

AU - El Bahy, M. M.

AU - Hosny, M.

AU - Mohamed, Wael A.

AU - Ibrahim, Shawky

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Classification of EEG signals is one of the biggest problems in Brain Computer Interface (BCI) systems. This paper presents a BCI system based on using the EEG signals associated with five mental tasks (baseline, math, mental letter composing, geometric figure rotation and visual counting). EEG data for these five cognitive tasks from one subject were taken from the Colorado University database. Wavelet Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis (PCA) were used for features extraction. Artificial Neural Network (ANN) trained by a standard back propagation algorithm and Support Vector Machines (SVMs) were used for classifying different combinations mental tasks. Experimental results show the classification accuracies achieved with the three used feature extraction techniques and the two classification techniques.

AB - Classification of EEG signals is one of the biggest problems in Brain Computer Interface (BCI) systems. This paper presents a BCI system based on using the EEG signals associated with five mental tasks (baseline, math, mental letter composing, geometric figure rotation and visual counting). EEG data for these five cognitive tasks from one subject were taken from the Colorado University database. Wavelet Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis (PCA) were used for features extraction. Artificial Neural Network (ANN) trained by a standard back propagation algorithm and Support Vector Machines (SVMs) were used for classifying different combinations mental tasks. Experimental results show the classification accuracies achieved with the three used feature extraction techniques and the two classification techniques.

KW - Artificial neural network (ANN)

KW - Brain computer interface (BCI)

KW - Support vector machine (SVM)

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AN - SCOPUS:84994504701

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T3 - Advances in Intelligent Systems and Computing

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BT - Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016

A2 - Hassanien, Aboul Ella

A2 - Shaalan, Khaled

A2 - Azar, Ahmad Taher

A2 - Gaber, Tarek

A2 - Tolba, Mohamed F.

PB - Springer Verlag

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ER -

El Bahy MM, Hosny M, Mohamed WA, Ibrahim S. EEG signal classification using neural network and support vector machine in brain computer interface. In Hassanien AE, Shaalan K, Azar AT, Gaber T, Tolba MF, editors, Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, 2016. Springer Verlag. 2017. p. 246-256. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-48308-5_24

EEG signal classification using neural network and support vector machine in brain computer interface

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this