Population-Based Algorithms Applied to Brain-Computer Interfaces upon Steady-State Visual Evoked Potentials
DOI:
https://doi.org/10.14738/tmlai.72.6215Keywords:
Population-Algorithms, EEG signal processing, BCI-SSVEP, Particle Swarm Optimization, Ants Colony Optimization, Genetic Algorithm, Differential EvolutionAbstract
The development of brain-computer interfaces based upon steady-state visual evoked potentials (SSVEP) requires the processing of electroencephalogram signals to detect brain activity triggered on the occipital region of the scalp caused by visual stimuli. Different algorithms based on stochastic and analytical processes have been proposed. However, most of them involve complex transformations and are highly susceptible to local errors. The present work presents algorithms based upon population to optimize the dimensionality of the characteristics of electroencephalogram signals focusing on SSVEP. Population-based algorithms are substantiated on the collective behavior of individuals observed in nature, such as flocks of birds, fish populations and some microorganisms, in order to find optimal solutions. This work shows the algorithms of optimization of particle swarm optimization, ant colony optimization, genetic algorithm and differential evolution algorithms in order to generate an optimum subset of features that improves the identification of features of electroencephalogram signals. Spectral Density of Power, Spectral Coherence methods and the computational cost between these algorithms are presented as measure of comparison.
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