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

研究成果: 書貢獻/報告類型 › 會議貢獻

11 引文斯高帕斯（Scopus）

摘要

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.

原文	英語
主出版物標題	2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781509029068
DOIs	https://doi.org/10.1109/CEEICT.2016.7873150
出版狀態	已發佈 - 3月 6 2017
事件	3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 - Dhaka, 孟加拉国持續時間: 9月 22 2016 → 9月 24 2016

其他

其他	3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
國家/地區	孟加拉国
城市	Dhaka
期間	9/22/16 → 9/24/16

ASJC Scopus subject areas

電腦網路與通信
電腦科學應用
人機介面
訊號處理
電氣與電子工程

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10.1109/CEEICT.2016.7873150

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引用此

Islam, S. M. R., Sajol, A., Huang, X., & Ou, K. L. (2017). Feature extraction and classification of EEG signal for different brain control machine. 於 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 [7873150] 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 等.

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

研究成果: 書貢獻/報告類型 › 會議貢獻

Islam, SMR, Sajol, A, Huang, X & Ou, KL 2017, Feature extraction and classification of EEG signal for different brain control machine. 於 2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016., 7873150, Institute of Electrical and Electronics Engineers Inc., 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016, Dhaka, 孟加拉国, 9/22/16. https://doi.org/10.1109/CEEICT.2016.7873150

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title = "Feature extraction and classification of EEG signal for different brain control machine",

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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AU - Islam, Sheikh Md Rabiul

AU - Sajol, Ahosanullah

AU - Huang, Xu

AU - Ou, Keng Liang

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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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ER -

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

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