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.
Index Terms—Brain Computer Interface, Electroencephalogram, Band Power, Artificial Neural Networksand Multi Layer Perceptorn.
Senthilmurugan M., Assistant Professor, Department of Computer Applications, A. V. C College of Engineering, Mayiladuthurai, Mannampandal – 609305, Nagapattinam District, Tamil Nadu, S. India, Ph-+91-9842091911, (email: email@example.com)
Latha M., Senior Lecturer, Department of Electrical and Electronics Engineering, A. V. C Polytechnic College, Mayiladuthurai, Mannampandal –609305, Nagapattinam District, Tamil Nadu, S. India
Dr. Malmurugan N., Director, CARE College of Engineering and Technology, Tamilnadu, India
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.