A Fuzzy Genetic Algorithm for Optimal Spatial Filter Selection for P300-Based Brain Computer Interfaces

2018 ◽  
Author(s):  
David Achanccaray ◽  
Christian Flores ◽  
Christian Fonseca ◽  
Javier Andreu-Perez
Keyword(s):  
Genetic Algorithm ◽  
Spatial Filter ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Fuzzy Genetic Algorithm ◽  
Selection For ◽  
Fuzzy Genetic ◽  
Filter Selection

scholarly journals Aluminium foam production control by using a combined fuzzy-genetic algorithm model

Procedia CIRP ◽  
2020 ◽  
Vol 88 ◽  
pp. 503-508
Author(s):  
Gennaro Salvatore Ponticelli ◽  
Stefano Guarino ◽  
Oliviero Giannini ◽  
Flaviana Tagliaferri ◽  
Simone Venettacci ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Production Control ◽  
Aluminium Foam ◽  
Fuzzy Genetic Algorithm ◽  
Fuzzy Genetic ◽  
Foam Production ◽  
Genetic Algorithm Model

Fuzzy-Genetic Algorithm for Pre-Processing Data at the RTU

2004 ◽  
Vol 19 (2) ◽  
pp. 718-723 ◽  
Author(s):  
P. Kumar ◽  
V.K. Chandna ◽  
M.S. Thomas
Keyword(s):  
Genetic Algorithm ◽  
Processing Data ◽  
Fuzzy Genetic Algorithm ◽  
Fuzzy Genetic

Fast Covariance Matching With Fuzzy Genetic Algorithm

2012 ◽  
Vol 8 (1) ◽  
pp. 148-157 ◽  
Author(s):  
Xuguang Zhang ◽  
Shuo Hu ◽  
Dan Chen ◽  
Xiaoli Li
Keyword(s):  
Genetic Algorithm ◽  
Covariance Matching ◽  
Fuzzy Genetic Algorithm ◽  
Fuzzy Genetic

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

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