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oai:uvadoc.uva.es:10324/509482025-02-21T07:45:21Zcom_10324_1191com_10324_931com_10324_894col_10324_1379
UVaDOC author Martínez Cagigal, Víctor author SantaMaría Vazquez, Eduardo author Hornero Sánchez, Roberto 2021-12-15T09:10:03Z 2021-12-15T09:10:03Z 2021 Applied Soft Computing, 2021, vol. 115, 108176 1568-4946 https://uvadoc.uva.es/handle/10324/50948 10.1016/j.asoc.2021.108176 Many brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful channel selection increases the performance and users’ comfort while reducing the cost of the system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, have not been fully exploited yet in this context. The purpose of the study is two-fold: (1) to propose a novel algorithm to find an optimal channel set for each user and compare it with other existing meta-heuristics; and (2) to establish guidelines for adapting these optimization strategies to this framework. A total of 3 single-objective (GA, BDE, BPSO) and 4 multi-objective (NSGA-II, BMOPSO, SPEA2, PEAIL) existing algorithms have been adapted and tested with 3 public databases: ‘BCI competition III–dataset II’, ‘Center Speller’ and ‘RSVP Speller’. Dual-Front Sorting Algorithm (DFGA), a novel multi-objective discrete method especially designed to the BCI framework, is proposed as well. Results showed that all meta-heuristics outperformed the full set and the common 8-channel set for P300-based BCIs. DFGA showed a significant improvement of accuracy of 3.9% over the latter using also 8 channels; and obtained similar accuracies using a mean of 4.66 channels. A topographic analysis also reinforced the need to customize a channel set for each user. Thus, the proposed method computes an optimal set of solutions with different number of channels, allowing the user to select the most appropriate distribution for the next BCI sessions. eng Attribution-NonCommercial-NoDerivatives 4.0 Internacional Brain-computer interface channel selection optimization using meta-heuristics and evolutionary algorithms info:eu-repo/semantics/article URL https://uvadoc.uva.es/bitstream/10324/50948/1/Brain%e2%80%93computer-interface-channel.pdf File MD5 9246887e58cd38c454c7577305206fca 4773928 application/pdf Brain–computer-interface-channel.pdf