Abstract
Brain-computer interface (BCI) systems need to work in real-time with large amounts of data, which makes the channel selection procedures essential to reduce over-fitting and to increase users’ comfort. In that sense, metaheuristics based on swarm intelligence (SI) have demonstrated excellent performances solving complex optimization problems and, to the best of our knowledge, they have not been fully exploited in P300-BCI systems. In this study, we propose a modified SI method, called binary bees algorithm (b-BA), that allows users to select the most relevant channels in an evolutionary way. This method has been compared to particle swarm optimization (PSO) and tested with the ‘III BCI Competition 2005’ dataset II. Results show that b-BA is suitable for use in this kind of systems, reaching higher accuracies (mean of 96.0 ± 0.0%) than PSO (mean of 93.5 ± 2.1%) and the original ones (mean of 94.0 ± 2.8%) using less than the half of the initial channels.
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Acknowledgments
This work was partially supported by the project TEC2014-53196-R of ‘Ministerio de Economía y Competitividad’ (MINECO) and FEDER. In addition, V. Martínez-Cagigal was in receipt of a ‘Promoción de Empleo Joven e Implantación de la Garanta Juvenil en I+D+i’ grant from MINECO and the University of Valladolid.
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Martínez-Cagigal, V., Hornero, R. (2017). A Binary Bees Algorithm for P300-Based Brain-Computer Interfaces Channel Selection. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_39
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DOI: https://doi.org/10.1007/978-3-319-59147-6_39
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