BioMed Central Page 1 of 13 (page number not for citation purposes) Journal of NeuroEngineering and Rehabilitation Open Access Research Application of a hybrid wavelet feature selection method in th design of a self-paced brain interface system Mehrdad Fatourechi* 1 , Gary E Birch 1,2,3 and Rabab K Ward 1,3 Address: 1 Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada, 2 Neil Squire Society, Burnaby, BC V5M 3Z3, Canada and 3 Institute 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 K Ward - rababw@ece.ubc.ca * Corresponding author 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 further reduce its dimensionality and select the best set of features. Results: An offline analysis of the EEG signals (18 bipolar EEG channels) of four able-bodied subjects showed that the proposed method acquires low false positive rates at a reasonably high true positive rate. The results also show that features selected from different channels varied considerably from one subject to another. Conclusion: The proposed hybrid method effectively reduces the high dimensionality of the feature space. The variability in features among subjects indicates that a user-customized BI system needs to be developed for individual users. Background A successful brain interface (BI) system enables individu- als with severe motor disabilities to control objects in their environment (such as a light switch, neural prosthe- sis or computer) by using only their brain signals. Such a system measures specific features of a person's brain sig- nal that relate to his or her intent to affect control, then translates them into control signals that are used to con- trol a device [1,2]. Brain interface systems are implemented in two ways: sys- tem-paced (synchronized) or self-paced (asynchronous). In system-paced BI systems, a user can initiate a command only during certain periods specified by the system. In a self-paced BI system, users can affect the output of the BI system whenever they want, by intentionally changing their brain state. The state in which a user is intentionally attempting to control a device is called an intentional con- trol (IC) state. At other times, users are said to be in a no- Published: 30 April 2007 Journal of NeuroEngineering and Rehabilitation 2007, 4:11 doi:10.1186/1743-0003-4-11 Received: 13 May 2006 Accepted: 30 April 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/licens ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.