EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.
Comput Intell Neurosci
; 2022: 7571208, 2022.
Article
em En
| MEDLINE
| ID: mdl-35814562
Brain-computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain-computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Interfaces Cérebro-Computador
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Comput Intell Neurosci
Assunto da revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
México
País de publicação:
Estados Unidos