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

摘要

This paper presents a novel approach for efficient feature extraction using mutual information (MI). In terms of mutualinformation, the optimal feature extraction is creating a feature set from the data which jointly have the largest dependency onthe target class. However, it is not always easy to get an accurate estimation for high-dimensional MI. In this paper, we proposean efficient method for feature extraction which is based on two-dimensional MI estimates. At each step, a new feature is createdthat attempts to maximize the MI between the new feature and the target class and to minimize the redundancy. We will refer tothis 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.