RT info:eu-repo/semantics/article
T1 Brain-computer interface channel selection optimization using meta-heuristics and evolutionary algorithms
A1 Martínez Cagigal, Víctor
A1 Santamaría Vázquez, Eduardo
A1 Hornero Sánchez, Roberto
K1 Brain-computer interfaces
K1 Interfaces cerebro-computadora
K1 Evolutionary algorithms
K1 Algoritmos evolutivos
AB 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.
PB Elsevier
SN 1568-4946
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/50948
UL https://uvadoc.uva.es/handle/10324/50948
LA eng
NO Applied Soft Computing, 2021, vol. 115, 108176
NO Producción Científica
DS UVaDOC
RD 03-dic-2024