Standard

Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. / Merkulova, Ekaterina a.; Kozulin, Igor a.; Savostyanov, Alexandr n. et al.

2023. 1740-1745 Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM).

Research output: Contribution to conference › Paper › peer-review

Harvard

Merkulova, EA, Kozulin, IA, Savostyanov, AN, Bocharov, AV & Privodnova, EY 2023, 'Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder', Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM), 29.06.2023 - 03.07.2023 pp. 1740-1745. https://doi.org/10.1109/EDM58354.2023.10225096

APA

Merkulova, E. A., Kozulin, I. A., Savostyanov, A. N., Bocharov, A. V., & Privodnova, E. Y. (2023). Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. 1740-1745. Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM). https://doi.org/10.1109/EDM58354.2023.10225096

Vancouver

Merkulova EA, Kozulin IA, Savostyanov AN, Bocharov AV, Privodnova EY. Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. 2023. Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM). doi: 10.1109/EDM58354.2023.10225096

Author

Merkulova, Ekaterina a. ; Kozulin, Igor a. ; Savostyanov, Alexandr n. et al. / Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder. Paper presented at 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM).

BibTeX

@conference{8c247a967fad4e89b1311a724e9120a0,
title = "Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder",
author = "Merkulova, {Ekaterina a.} and Kozulin, {Igor a.} and Savostyanov, {Alexandr n.} and Bocharov, {Andrey v.} and Privodnova, {Evgeniya yu.}",
year = "2023",
month = jun,
day = "29",
doi = "10.1109/EDM58354.2023.10225096",
language = "English",
pages = "1740--1745",
note = "2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM) ; Conference date: 29-06-2023 Through 03-07-2023",

}

RIS

TY - CONF

T1 - Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder

AU - Merkulova, Ekaterina a.

AU - Kozulin, Igor a.

AU - Savostyanov, Alexandr n.

AU - Bocharov, Andrey v.

AU - Privodnova, Evgeniya yu.

PY - 2023/6/29

Y1 - 2023/6/29

U2 - 10.1109/EDM58354.2023.10225096

DO - 10.1109/EDM58354.2023.10225096

M3 - Paper

SP - 1740

EP - 1745

T2 - 2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM)

Y2 - 29 June 2023 through 3 July 2023

ER -

ID: 61403819