Feature extraction of EEG signal upon BCI systems based on steady-state visual evoked potentials using the ant colony optimization algorithm
type: Item
instance of: scholarly article
title: Feature extraction of EEG signal upon BCI systems based on steady-state visual evoked potentials using the ant colony optimization algorithm
zbMATH Open document ID: 1417.92081
DOI: 10.1155/2018/2143873
author: S. M. Fernandez-Fraga
author: M. A. Aceves-Fernandez
author: J. C. Pedraza-Ortega
author: S. Tovar-Arriaga
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-20T00:00:00Z
review text: Summary: This work presents the use of swarm intelligence algorithms as a reliable method for the optimization of electroencephalogram signals for the improvement of the performance of the brain interfaces based on stable states visual events. The preprocessing of brain signals for the extraction of characteristics and the detection of events is of paramount importance for the improvement of brain interfaces. The proposed ant colony optimization algorithm presents an improvement in obtaining the key features of the signals and the detection of events based on visual stimuli. As a reference model, we used the Independent Component Analysis method, which has been used in recent research for the removal of nonrelevant and detection of relevant data from the brain's electrical signals and also allows the collection of information in response to a stimulus and separates the signals that were generated independently in certain zones of the brain.
Mathematics Subject Classification ID: 92C55
zbMATH DE Number: 7026516
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/2143873
OpenAlex ID: W2810972778
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Wikidata QID: Q129649429
description: scientific article; zbMATH DE number 7026516