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

3 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
出版狀態已發佈 - 三月 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: 九月 22 2016九月 24 2016

其他

其他3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016
國家孟加拉国
城市Dhaka
期間9/22/169/24/16

ASJC Scopus subject areas

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

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    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