print

Ensemble approach for detection of depression using EEG features

author
Avots, Egils
Jermakovs, Klavs
Bachmann, Maie
Päeske, Laura
Ozcinar, Cagri
Anbarjafari, Gholamreza
statement of authorship
Egils Avots, Klavs Jermakovs, Maie Bachmann, Laura Päeske, Cagri Ozcinar and Gholamreza Anbarjafari
source
Entropy
publisher
MDPI
journal volume number month
vol. 24, 2
year of publication
2022
pages
art. 211
url
https://doi.org/10.3390/e24020211
subject term
depressioon
elektroentsefalogramm
tehisõpe
keyword
depression
electroencephalogram (EEG)
feature extraction and selection
machine learning
ensemble learning
ISSN
1099-4300
notes
Bibliogr.: 46 ref
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
Journal metrics at Scopus
Article at Scopus
WOS
Journal metrics at WOS
Article at WOS
category (general)
Mathematics
Physics and astronomy
Engineering
Computer Science
category (sub)
Mathematics. Mathematical physics
Physics and astronomy. Physics and astronomy (miscellaneous)
Physics and astronomy. General physics and astronomy
Engineering. Electrical and electronic engineering
Computer science. Information systems
kvartiil
Q2
TTÜ department
tervisetehnoloogiate instituut
language
inglise
Uurimisrühm
Brain bioelectrical signals research group
Avots, E., Jermakovs, K., Bachmann, M., Päeske, L., Ozcinar, C., Anbarjafari, G. Ensemble approach for detection of depression using EEG features // Entropy (2022) vol. 24, 2, art. 211. https://doi.org/10.3390/e24020211