STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition

Yuhan Shi; Nan Zhao; Wenjie Yuan; Qiqi Zhao; Xiaowei Zhang; Bin Hu

doi:10.1109/BIBM58861.2023.10385598

STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition

Yuhan Shi, Nan Zhao, Wenjie Yuan, Qiqi Zhao, Xiaowei Zhang^*, Bin Hu^*

^*Corresponding author for this work

School of Medical and Technology

Lanzhou University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Event-related potentials (ERP) within the Electroencephalogram (EEG), in particular, are electric signals induced by stimuli that can reflect specific cognitive activities of the brain. Therefore, ERP can be used as an objective biomarker to distinguish patients with depression from healthy individuals. However, the analysis and classification of single-trial ERP are difficult due to the high trial-to-trial variability and the low signal-to-noise ratio (SNR). Therefore, how to improve the SNR and the single-trial classification accuracy has received much attention. In this study, we proposed a spatiotemporal linear discriminant analysis (STLDA) method for single-trial ERP-based depression recognition, which can obtain optimal temporal and spatial filters for ERP signals to enhance their SNR significantly and preserve the spatiotemporal characteristics of the signals for depression recognition using the minimum distance to mean (MDM) classification strategy. Experimental results on two public datasets showed that our method achieved higher classification accuracy in comparison with some baseline methods. Further, the depression recognition performance of our method is almost equal to the traditional trial-averaged strategy.

Original language	English
Title of host publication	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1413-1420
Number of pages	8
ISBN (Electronic)	9798350337488
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385598
Publication status	Published - 2023
Event	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/Territory	Turkey
City	Istanbul
Period	5/12/23 → 8/12/23

Keywords

depressive disorder
electroencephalogram
event-related potentials
linear discriminant analysis
spatiotemporal filter

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10.1109/BIBM58861.2023.10385598

Cite this

Shi, Y., Zhao, N., Yuan, W., Zhao, Q., Zhang, X., & Hu, B. (2023). STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 (pp. 1413-1420). (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM58861.2023.10385598

Shi, Yuhan ; Zhao, Nan ; Yuan, Wenjie et al. / STLDA : A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1413-1420 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

@inproceedings{5dbc8aa17ab3479fb9a03c15380d513a,

title = "STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition",

abstract = "Event-related potentials (ERP) within the Electroencephalogram (EEG), in particular, are electric signals induced by stimuli that can reflect specific cognitive activities of the brain. Therefore, ERP can be used as an objective biomarker to distinguish patients with depression from healthy individuals. However, the analysis and classification of single-trial ERP are difficult due to the high trial-to-trial variability and the low signal-to-noise ratio (SNR). Therefore, how to improve the SNR and the single-trial classification accuracy has received much attention. In this study, we proposed a spatiotemporal linear discriminant analysis (STLDA) method for single-trial ERP-based depression recognition, which can obtain optimal temporal and spatial filters for ERP signals to enhance their SNR significantly and preserve the spatiotemporal characteristics of the signals for depression recognition using the minimum distance to mean (MDM) classification strategy. Experimental results on two public datasets showed that our method achieved higher classification accuracy in comparison with some baseline methods. Further, the depression recognition performance of our method is almost equal to the traditional trial-averaged strategy.",

keywords = "depressive disorder, electroencephalogram, event-related potentials, linear discriminant analysis, spatiotemporal filter",

author = "Yuhan Shi and Nan Zhao and Wenjie Yuan and Qiqi Zhao and Xiaowei Zhang and Bin Hu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/BIBM58861.2023.10385598",

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editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

booktitle = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

address = "United States",

}

Shi, Y, Zhao, N, Yuan, W, Zhao, Q, Zhang, X & Hu, B 2023, STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1413-1420, 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkey, 5/12/23. https://doi.org/10.1109/BIBM58861.2023.10385598

STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition. / Shi, Yuhan; Zhao, Nan; Yuan, Wenjie et al.
Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1413-1420 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - STLDA

T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

AU - Shi, Yuhan

AU - Zhao, Nan

AU - Yuan, Wenjie

AU - Zhao, Qiqi

AU - Zhang, Xiaowei

AU - Hu, Bin

PY - 2023

Y1 - 2023

N2 - Event-related potentials (ERP) within the Electroencephalogram (EEG), in particular, are electric signals induced by stimuli that can reflect specific cognitive activities of the brain. Therefore, ERP can be used as an objective biomarker to distinguish patients with depression from healthy individuals. However, the analysis and classification of single-trial ERP are difficult due to the high trial-to-trial variability and the low signal-to-noise ratio (SNR). Therefore, how to improve the SNR and the single-trial classification accuracy has received much attention. In this study, we proposed a spatiotemporal linear discriminant analysis (STLDA) method for single-trial ERP-based depression recognition, which can obtain optimal temporal and spatial filters for ERP signals to enhance their SNR significantly and preserve the spatiotemporal characteristics of the signals for depression recognition using the minimum distance to mean (MDM) classification strategy. Experimental results on two public datasets showed that our method achieved higher classification accuracy in comparison with some baseline methods. Further, the depression recognition performance of our method is almost equal to the traditional trial-averaged strategy.

AB - Event-related potentials (ERP) within the Electroencephalogram (EEG), in particular, are electric signals induced by stimuli that can reflect specific cognitive activities of the brain. Therefore, ERP can be used as an objective biomarker to distinguish patients with depression from healthy individuals. However, the analysis and classification of single-trial ERP are difficult due to the high trial-to-trial variability and the low signal-to-noise ratio (SNR). Therefore, how to improve the SNR and the single-trial classification accuracy has received much attention. In this study, we proposed a spatiotemporal linear discriminant analysis (STLDA) method for single-trial ERP-based depression recognition, which can obtain optimal temporal and spatial filters for ERP signals to enhance their SNR significantly and preserve the spatiotemporal characteristics of the signals for depression recognition using the minimum distance to mean (MDM) classification strategy. Experimental results on two public datasets showed that our method achieved higher classification accuracy in comparison with some baseline methods. Further, the depression recognition performance of our method is almost equal to the traditional trial-averaged strategy.

KW - depressive disorder

KW - electroencephalogram

KW - event-related potentials

KW - linear discriminant analysis

KW - spatiotemporal filter

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DO - 10.1109/BIBM58861.2023.10385598

M3 - Conference contribution

AN - SCOPUS:85184937976

T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

SP - 1413

EP - 1420

BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

A2 - Jiang, Xingpeng

A2 - Wang, Haiying

A2 - Alhajj, Reda

A2 - Hu, Xiaohua

A2 - Engel, Felix

A2 - Mahmud, Mufti

A2 - Pisanti, Nadia

A2 - Cui, Xuefeng

A2 - Song, Hong

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 5 December 2023 through 8 December 2023

ER -

Shi Y, Zhao N, Yuan W, Zhao Q, Zhang X, Hu B. STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1413-1420. (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). doi: 10.1109/BIBM58861.2023.10385598

STLDA: A Spatiotemporal Linear Discriminant Analysis for Single-trial ERP-based Depression Recognition

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