A Novel Hybrid Swarm Algorithm for P300-Based BCI Channel Selection

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 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.