tailieunhanh - Báo cáo hóa học: " Research Article A Minimax Mutual Information Scheme for Supervised Feature Extraction and Its Application to EEG-Based Brain-Computer Interfacing"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Minimax Mutual Information Scheme for Supervised Feature Extraction and Its Application to EEG-Based Brain-Computer Interfacing | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 673040 8 pages doi 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 Correspondence should be addressed to Abbas Erfanian erfanian@ Received 5 December 2007 Revised 29 May 2008 Accepted 3 July 2008 Recommended by Chein-I Chang 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. Copyright 2008 F. Oveisi and A. Erfanian. 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. 1. INTRODUCTION Classification of the EEG signals associated with mental tasks plays an important role in the performance of the most .