Paper Info
Title
Brain-computer interface channel selection optimization using meta-heuristics and evolutionary algorithms
Abstract
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 multiobjective (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. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Year
DOI
Venue
2022
10.1016/j.asoc.2021.108176
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Brain-computer interface (BCI), Channel selection, Multi-objective optimization, Evolutionary algorithms, P300 event-related potentials
Journal
115
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Víctor Martínez-Cagigal133.11
Eduardo Santamaría-Vázquez232.10
Roberto Hornero341.74