Abstract
A Brain Computer Interface (BCI) provides a direct form of communication between a person and the outside world using brain signals, either to increase his/her integration in society or to provide a way to control the environment where he/she lives. BCIs are communication systems based on electroencephalographic (EEG) signals, such as event-related evoked potentials (ERP). P300 is one of there ERP. It is a peak that usually appears in the EEG signals around 300 ms in response to an infrequent stimulus. The BCI based on P300 is usually composed by different blocks: input (data acquisition), feature selection/extraction, classification, output (e.g. control commands) and, eventually, feedback. In this work, a Genetic Algorithm (GA) is proposed as a feature selection method before the classification stage, implemented using Fisher’s Linear Discriminant Analysis (LDA). A dataset of input patterns was generated from a database of EEG recordings of healthy people, in order to train and test the proposed configuration. The addition of the GA as a feature selection method resulted in a significant improvement in classification performance ( p < 0.001 ) and in a reduction of the amount of features needed to reach such performance ( p < 0.001 ). The results of this work suggest that this configuration could be implemented in a portable BCI.
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© 2015 Springer International Publishing Switzerland
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Atum, Y.V., Manresa, J.A.B., Rufiner, L., Acevedo, R.C. (2015). Genetic Feature Selection for a P300 Brain Computer Interface. In: Braidot, A., Hadad, A. (eds) VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. IFMBE Proceedings, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-13117-7_154
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DOI: https://doi.org/10.1007/978-3-319-13117-7_154
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13116-0
Online ISBN: 978-3-319-13117-7
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