A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

Journal: Journal of neural engineering

Published Date: Apr 1, 2018

Abstract

OBJECTIVE: Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used.

Authors

Farajollah Tahernezhad-Javazm

Department of Mechatronics, The Center of Excellence for Mechatronics, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran.
Vahid Azimirad
Maryam Shoaran

Keywords

Algorithms Animals Brain Brain-Computer Interfaces Databases, Factual Electroencephalography Humans Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (28718779)

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

Abstract

Authors

Keywords

External Resources

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