EURASIP Journal on Advances in Signal Processing 
Volume 2008 (2008), Article ID 673040, 8 pages
doi:10.1155/2008/673040
Research Article

A Minimax Mutual Information Scheme for Supervised Feature Extraction and Its Application to EEG-Based Brain-Computer Interfacing

Farid Oveisi and Abbas Erfanian

 Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 16844, Iran
Received 5 December 2007; Revised 29 May 2008; Accepted 3 July 2008

Recommended by Chein-I Chang

Abstract

This paper presents a novel approach for efficient feature extraction using mutual information (MI). In terms of mutual information, the optimal feature extraction is creating a feature set from the data which jointly have the largest dependency on the target class. However, it is not always easy to get an accurate estimation for high-dimensional MI. In this paper, we propose an efficient method for feature extraction which is based on two-dimensional MI estimates. At each step, a new feature is created that attempts to maximize the MI between the new feature and the target class and to minimize the redundancy. We will refer to this algorithm as Minimax-MIFX. The effectiveness of the method is evaluated by using the classification of electroencephalogram (EEG) signals during hand movement imagination. The results confirm that the classification accuracy obtained by Minimax-MIFX is higher than that achieved by existing feature extraction methods and by full feature set.