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 |
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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 | |
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 |
Other
Other | 3rd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2016 |
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Country/Territory | Bangladesh |
City | Dhaka |
Period | 9/22/16 → 9/24/16 |
Keywords
- Acuraccy
- ANN
- EEG datasets
- k-NN
- SVM
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Human-Computer Interaction
- Signal Processing
- Electrical and Electronic Engineering