Feature extraction and classification of EEG signal for different brain control machine

Sheikh Md Rabiul Islam, Ahosanullah Sajol, Xu Huang, Keng Liang Ou

Research output: A Conference proceeding or a Chapter in BookConference contribution

3 Citations (Scopus)

Abstract

Brain computer interface is used for human and machine learning analysis. This paper represents the EEG datasets that are built with different cognitive task such as left, right, back and front imaginary movement with eye open. We have used different feature extraction method to classify these EEG signal using Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Artificial Neural Network (ANN). All these methods are compared with other work that have done with other datasets. The proposed work is obtained 95.21% accuracy 98.95% sensitivity for SVM and k-NN is 90.88% and ANN is 94.31%. The performance results have shown higher enough than all others.

Original languageEnglish
Title of host publication2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
Place of PublicationDhaka, Bangladesh
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509029068
ISBN (Print)9781509029068
DOIs
Publication statusPublished - 9 Mar 2017
Event3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 - Dhaka, Bangladesh
Duration: 22 Sep 201624 Sep 2016

Publication series

Name2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016

Conference

Conference3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
CountryBangladesh
CityDhaka
Period22/09/1624/09/16

Fingerprint

Electroencephalography
Support vector machines
Feature extraction
Brain
Neural networks
Brain computer interface
Learning systems

Cite this

Islam, S. M. R., Sajol, A., Huang, X., & Ou, K. L. (2017). Feature extraction and classification of EEG signal for different brain control machine. In 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 (pp. 1-6). [7873150] (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016). Dhaka, Bangladesh: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CEEICT.2016.7873150
Islam, Sheikh Md Rabiul ; Sajol, Ahosanullah ; Huang, Xu ; Ou, Keng Liang. / Feature extraction and classification of EEG signal for different brain control machine. 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016. Dhaka, Bangladesh : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 1-6 (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016).
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Islam, SMR, Sajol, A, Huang, X & Ou, KL 2017, Feature extraction and classification of EEG signal for different brain control machine. in 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016., 7873150, 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016, IEEE, Institute of Electrical and Electronics Engineers, Dhaka, Bangladesh, pp. 1-6, 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016, Dhaka, Bangladesh, 22/09/16. https://doi.org/10.1109/CEEICT.2016.7873150

Feature extraction and classification of EEG signal for different brain control machine. / Islam, Sheikh Md Rabiul; Sajol, Ahosanullah; Huang, Xu; Ou, Keng Liang.

2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016. Dhaka, Bangladesh : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1-6 7873150 (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016).

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Feature extraction and classification of EEG signal for different brain control machine

AU - Islam, Sheikh Md Rabiul

AU - Sajol, Ahosanullah

AU - Huang, Xu

AU - Ou, Keng Liang

PY - 2017/3/9

Y1 - 2017/3/9

N2 - Brain computer interface is used for human and machine learning analysis. This paper represents the EEG datasets that are built with different cognitive task such as left, right, back and front imaginary movement with eye open. We have used different feature extraction method to classify these EEG signal using Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Artificial Neural Network (ANN). All these methods are compared with other work that have done with other datasets. The proposed work is obtained 95.21% accuracy 98.95% sensitivity for SVM and k-NN is 90.88% and ANN is 94.31%. The performance results have shown higher enough than all others.

AB - Brain computer interface is used for human and machine learning analysis. This paper represents the EEG datasets that are built with different cognitive task such as left, right, back and front imaginary movement with eye open. We have used different feature extraction method to classify these EEG signal using Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Artificial Neural Network (ANN). All these methods are compared with other work that have done with other datasets. The proposed work is obtained 95.21% accuracy 98.95% sensitivity for SVM and k-NN is 90.88% and ANN is 94.31%. The performance results have shown higher enough than all others.

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Islam SMR, Sajol A, Huang X, Ou KL. Feature extraction and classification of EEG signal for different brain control machine. In 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016. Dhaka, Bangladesh: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 1-6. 7873150. (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016). https://doi.org/10.1109/CEEICT.2016.7873150