Trait depressivity prediction with EEG signals via LSBoost

Shenghuan Zhang; Brendan McCane; Phoebe S-H Neo; Shabah M Shadli; Neil McNaughton

doi:10.1109/IJCNN48605.2020.9207020

Trait depressivity prediction with EEG signals via LSBoost

Shenghuan Zhang, Brendan McCane, Phoebe S-H Neo, Shabah M Shadli, Neil McNaughton

Psychology

University of Otago

Research output: Book chapter/Published conference paper › Conference paper › peer-review

Abstract

Purpose: This study aims to identify EEG biomarkers that predict the level of depressive personality (where extreme scores indicate disorder), as opposed to the presence or absence of a depressive state or a depression diagnosis. Methods: Fourier features were extracted from 2-second epochs of resting state EEG and used by LSBoost to maximise the correlation with depressive trait tendencies (PID-5 depressivity index). Results: Our method accounted for 25.75% of the variance in PID-5 scores, albeit in females only. The recording channel C3 and frequencies in the gamma band were the most important contributors to the prediction. The findings are consistent with previous psychological studies and suggest that our method is a feasible strategy for developing quantitative EEG biomarkers for trait depressivity in a neuropsychologically interpretable form. We have also shown that there might be different markers for depressivity between males and females.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks (IJCNN)
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781728169262
ISBN (Print)	9781728169279
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9207020
Publication status	Published - 20 Jul 2020
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9207579 (Front matter and program) https://ieeexplore.ieee.org/xpl/conhome/9200848/proceeding (Proceedings)

Publication series

Name	International Joint Conference on Neural Networks (IJCNN)
Publisher	IEEE
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom
City	Glasgow
Period	19/07/20 → 24/07/20
Internet address	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9207579 (Front matter and program) https://ieeexplore.ieee.org/xpl/conhome/9200848/proceeding (Proceedings)

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10.1109/IJCNN48605.2020.9207020

Cite this

Zhang, S., McCane, B., Neo, P. S.-H., Shadli, S. M., & McNaughton, N. (2020). Trait depressivity prediction with EEG signals via LSBoost. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). (International Joint Conference on Neural Networks (IJCNN)). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN48605.2020.9207020

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title = "Trait depressivity prediction with EEG signals via LSBoost",

abstract = "Purpose: This study aims to identify EEG biomarkers that predict the level of depressive personality (where extreme scores indicate disorder), as opposed to the presence or absence of a depressive state or a depression diagnosis. Methods: Fourier features were extracted from 2-second epochs of resting state EEG and used by LSBoost to maximise the correlation with depressive trait tendencies (PID-5 depressivity index). Results: Our method accounted for 25.75% of the variance in PID-5 scores, albeit in females only. The recording channel C3 and frequencies in the gamma band were the most important contributors to the prediction. The findings are consistent with previous psychological studies and suggest that our method is a feasible strategy for developing quantitative EEG biomarkers for trait depressivity in a neuropsychologically interpretable form. We have also shown that there might be different markers for depressivity between males and females.",

author = "Shenghuan Zhang and Brendan McCane and Neo, {Phoebe S-H} and Shadli, {Shabah M} and Neil McNaughton",

note = "Includes bibliographical references.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

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doi = "10.1109/IJCNN48605.2020.9207020",

language = "English",

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series = "International Joint Conference on Neural Networks (IJCNN)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "1--8",

booktitle = "2020 International Joint Conference on Neural Networks (IJCNN)",

address = "United States",

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Zhang, S, McCane, B, Neo, PS-H, Shadli, SM & McNaughton, N 2020, Trait depressivity prediction with EEG signals via LSBoost. in 2020 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN), IEEE, Institute of Electrical and Electronics Engineers, pp. 1-8, 2020 International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207020

Trait depressivity prediction with EEG signals via LSBoost. / Zhang, Shenghuan; McCane, Brendan; Neo, Phoebe S-H et al.
2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 1-8 (International Joint Conference on Neural Networks (IJCNN)).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Trait depressivity prediction with EEG signals via LSBoost

AU - Zhang, Shenghuan

AU - McCane, Brendan

AU - Neo, Phoebe S-H

AU - Shadli, Shabah M

AU - McNaughton, Neil

N1 - Includes bibliographical references.

PY - 2020/7/20

Y1 - 2020/7/20

N2 - Purpose: This study aims to identify EEG biomarkers that predict the level of depressive personality (where extreme scores indicate disorder), as opposed to the presence or absence of a depressive state or a depression diagnosis. Methods: Fourier features were extracted from 2-second epochs of resting state EEG and used by LSBoost to maximise the correlation with depressive trait tendencies (PID-5 depressivity index). Results: Our method accounted for 25.75% of the variance in PID-5 scores, albeit in females only. The recording channel C3 and frequencies in the gamma band were the most important contributors to the prediction. The findings are consistent with previous psychological studies and suggest that our method is a feasible strategy for developing quantitative EEG biomarkers for trait depressivity in a neuropsychologically interpretable form. We have also shown that there might be different markers for depressivity between males and females.

AB - Purpose: This study aims to identify EEG biomarkers that predict the level of depressive personality (where extreme scores indicate disorder), as opposed to the presence or absence of a depressive state or a depression diagnosis. Methods: Fourier features were extracted from 2-second epochs of resting state EEG and used by LSBoost to maximise the correlation with depressive trait tendencies (PID-5 depressivity index). Results: Our method accounted for 25.75% of the variance in PID-5 scores, albeit in females only. The recording channel C3 and frequencies in the gamma band were the most important contributors to the prediction. The findings are consistent with previous psychological studies and suggest that our method is a feasible strategy for developing quantitative EEG biomarkers for trait depressivity in a neuropsychologically interpretable form. We have also shown that there might be different markers for depressivity between males and females.

U2 - 10.1109/IJCNN48605.2020.9207020

DO - 10.1109/IJCNN48605.2020.9207020

M3 - Conference paper

SN - 9781728169279

T3 - International Joint Conference on Neural Networks (IJCNN)

SP - 1

EP - 8

BT - 2020 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

Trait depressivity prediction with EEG signals via LSBoost

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