Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
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: Application of a hybrid wavelet feature selection method in the design of a self-paced brain interface system | Journal of NeuroEngineering and Rehabilitation BioMed Central Research Open Access Application of a hybrid wavelet feature selection method in the design of a self-paced brain interface system Mehrdad Fatourechi 1 Gary E Birch1 2 3 and Rabab K Ward1 3 Address department of Electrical and Computer Engineering University of British Columbia Vancouver BC V6T 1Z4 Canada 2Neil Squire Society Burnaby BC V5M 3Z3 Canada and 3Institute for Computing Information and Cognitive Systems Vancouver BC V6T 1Z4 Canada Email Mehrdad Fatourechi - mehrdadf@ece.ubc.ca Gary E Birch - garyb@neilsquire.ca Rabab KWard - rababw@ece.ubc.ca Corresponding author Published 30 April 2007 Received 13 May 2006 Journal of NeuroEngineering and Rehabilitation 2007 4 11 doi 10.1186 1743-0003-4-1 1 Accepted 30 Apnl 2007 This article is available from http www.jneuroengrehab.com content 4 1 11 2007 Fatourechi et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.Org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Recently successful applications of the discrete wavelet transform have been reported in brain interface BI systems with one or two EEG channels. For a multi-channel BI system however the high dimensionality of the generated wavelet features space poses a challenging problem. Methods In this paper a feature selection method that effectively reduces the dimensionality of the feature space of a multi-channel self-paced BI system is proposed. The proposed method uses a two-stage feature selection scheme to select the most suitable movement-related potential features from the feature space. The first stage employs mutual information to filter out the least discriminant features resulting in a reduced feature space. Then a genetic algorithm is applied to the reduced feature space to .
