Abstract
This paper shows that parallel processing is useful for feature selection in brain-computer interfacing (BCI) tasks. The classification problems arising in such application usually involve a relatively small number of high-dimensional patterns and, as curse of dimensionality issues have to be taken into account, feature selection is an important requirement to build suitable classifiers. As the number of features defining the search space is high, the distribution of the searching space among different processors would contribute to find better solutions, requiring similar or even smaller amount of execution time than sequential counterpart procedures. We have implemented a parallel evolutionary multiobjective optimization procedure for feature selection, based on the island model, in which the individuals are distributed among different subpopulations that independently evolve and interchange individuals after a given number of generations. The experimental results show improvements in both computing time and quality of EEG classification with features extracted by multiresolution analysis (MRA), an approach widely used in the BCI field with useful properties for both temporal and spectral signal analysis.
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Acknowledgements
This work was partly funded by grant TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad” and FEDER funds).
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Ortega, J., Kimovski, D., Gan, J.Q., Ortiz, A., Damas, M. (2017). A Parallel Island Approach to Multiobjective Feature Selection for Brain-Computer Interfaces. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_2
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DOI: https://doi.org/10.1007/978-3-319-59153-7_2
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