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 | 9/22/16 → 9/24/16 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Human-Computer Interaction
- Signal Processing
- Electrical and Electronic Engineering
指紋 深入研究「Feature extraction and classification of EEG signal for different brain control machine」主題。共同形成了獨特的指紋。
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