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A Novel Hybrid Swarm Algorithm for P300-Based BCI Channel Selection
World Congress on Medical Physics & Biomedical Engineering (IUPESM 2018)
Año del Documento
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 2Nc possible subsets for an Nc 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.
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