@inproceedings{f2fa6cb705bd4ac68bbf7192b5554b4e,
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.",
keywords = "Acuraccy, ANN, EEG datasets, k-NN, SVM",
author = "Islam, {Sheikh Md Rabiul} and Ahosanullah Sajol and Xu Huang and Ou, {Keng Liang}",
year = "2017",
month = mar,
day = "9",
doi = "10.1109/CEEICT.2016.7873150",
language = "English",
isbn = "9781509029068",
series = "2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--6",
booktitle = "2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016",
address = "United States",
note = "3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 ; Conference date: 22-09-2016 Through 24-09-2016",
}