doi: 10.7763/IJCTE.2011.V3.316
Classification in EEG-Based Brain Computer Interfaces Using Inverse Model
Senthilmurugan M.1
, ,
- 1Department of Computer Applications, A. V. C College of Engineering, Mayiladuthurai, Mannampandal – 609305, Nagapattinam District, Tamil Nadu, S. India, Ph-+91-9842091911
- 2Department of Electrical and Electronics Engineering, A. V. C Polytechnic College, Mayiladuthurai, Mannampandal –609305, Nagapattinam District, Tamil Nadu, S. India
Abstract
We propose a novel approach using Inference System for classification in EEG based Brain Computer Interfaces. Our FIS algorithm is based on inverse model. Our result shows that FIS classifier reached the same level of accuracy as SVM classifier. FIS is outperformed with MultiLayer Perceptron and Linear classifier. As a result FIS based classification is suitable for Brain Computer Interface design. In addition to FIS algorithm is easily readable.
Keywords
- Brain Computer Interface
- Electroencephalogram
- Band Power
- Artificial Neural Networksand Multi Layer Perceptorn
How to Cite
Senthilmurugan M., Latha M., and Malmurugan N., "Classification in EEG-Based Brain Computer Interfaces Using Inverse Model," International Journal of Computer Theory and Engineering, vol. 3, no. 2, pp. 274-276, 2011. https://doi.org/10.7763/IJCTE.2011.V3.316
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).