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

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

doi:10.1109/CEEICT.2016.7873150

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: Chapter in Book/Report/Conference proceeding › Conference contribution

14 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 language	English
Title of host publication	2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509029068
DOIs	https://doi.org/10.1109/CEEICT.2016.7873150
Publication status	Published - Mar 6 2017
Event	3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 - Dhaka, Bangladesh Duration: Sept 22 2016 → Sept 24 2016

Publication series

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

Other

Other	3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
Country/Territory	Bangladesh
City	Dhaka
Period	9/22/16 → 9/24/16

Keywords

ANN
Acuraccy
EEG datasets
SVM
k-NN

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Human-Computer Interaction
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/CEEICT.2016.7873150

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 Article 7873150 (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016). Institute of Electrical and Electronics Engineers Inc.. 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 et al.
2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7873150 (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016).

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

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, Institute of Electrical and Electronics Engineers Inc., 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016, Dhaka, Bangladesh, 9/22/16. https://doi.org/10.1109/CEEICT.2016.7873150

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. Institute of Electrical and Electronics Engineers Inc. 2017. 7873150. (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016). doi: 10.1109/CEEICT.2016.7873150

Islam, Sheikh Md Rabiul ; Sajol, Ahosanullah ; Huang, Xu et al. / 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. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016).

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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.",

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

Abstract

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Keywords

ASJC Scopus subject areas

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