Abstract
Channel selection procedures are essential to reduce the curse of dimensionality in Brain-Computer Interface systems. However, these selection is not trivial, due to the fact that there are \( 2^{{N_{c} }} \) possible subsets for an N c channel cap. The aim of this study is to propose a novel multi-objective hybrid algorithm to simultaneously: (i) reduce the required number of channels and (ii) increase the accuracy of the system. The method, which integrates novel concepts based on dedicated searching and deterministic initialization, returns a set of pareto-optimal channel sets. Tested with 4 healthy subjects, the results show that the proposed algorithm is able to reach higher accuracies (97.00%) than the classic MOPSO (96.60%), the common 8-channel set (95.25%) and the full set of 16 channels (96.00%). Moreover, these accuracies have been obtained using less number of channels, making the proposed method suitable for its application in BCI systems.
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Acknowledgements
This study was partially funded by projects TEC2014-53196-R of ‘Ministerio of Economía y Competitividad’ and FEDER, the project “Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer” (Inter-regional cooperation program VA Spain-Portugal POCTEP 2014–202) of the European Commission and FEDER, and project VA037U16 of the ‘Junta de Castilla y León’ and FEDER. V. Martínez-Cagigal was in receipt of a PIF-UVa grant of the University of Valladolid. The authors declare no conflict of interest.
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Martínez-Cagigal, V., Santamaría-Vázquez, E., Hornero, R. (2019). A Novel Hybrid Swarm Algorithm for P300-Based BCI Channel Selection. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/3. Springer, Singapore. https://doi.org/10.1007/978-981-10-9023-3_8
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DOI: https://doi.org/10.1007/978-981-10-9023-3_8
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