A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls

Wei Zhang; Tao Gong; Jianxiu Li; Xiaowei Li; Bin Hu

doi:10.1007/978-981-16-2540-4_16

A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls

Wei Zhang, Tao Gong, Jianxiu Li, Xiaowei Li, Bin Hu^*

^*Corresponding author for this work

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

Abstract

The investigation of attentional bias of depression based on P300 component has drawn interest within the last decades. Follow-up of previous research suggested the differential amplitudes between depression and normal controls (NCs) in response to various facial stimuli. In this paper, we used single-trials features in the occurrence of P300 to recognize depression from NCs. EEG activity was recorded from 24 patients and 29 NCs in a dot-probe task. We considered two traditionally used feature-extraction methods: ReliefF and principal component analysis (PCA). Then, the k-nearest neighbor (KNN), BFTree, C4.5, logistic regression and NaiveBayes were adopted in this study to make a performance comparison. The combination of NaiveBayes and PCA was applied to classify the P300 component evoked by sad-neutral pairs, which achieved higher classification accuracy than other classifiers. The classification accuracy was 98%. The classification results support that the P300 component of ERPs may reflect information processing of the specific response of depression to specific stimuli and may be applied as a physiologic index for aided diagnosis of depression in future research.

Original language	English
Title of host publication	Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers
Editors	Yuqing Sun, Dongning Liu, Hao Liao, Hongfei Fan, Liping Gao
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	209-221
Number of pages	13
ISBN (Print)	9789811625398
DOIs	https://doi.org/10.1007/978-981-16-2540-4_16
Publication status	Published - 2021
Externally published	Yes
Event	15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020 - Shenzhen, China Duration: 7 Nov 2020 → 9 Nov 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1330 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020
Country/Territory	China
City	Shenzhen
Period	7/11/20 → 9/11/20

Keywords

Classification
Depression
P300
Single-trials

Access to Document

10.1007/978-981-16-2540-4_16

Cite this

Zhang, W., Gong, T., Li, J., Li, X., & Hu, B. (2021). A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls. In Y. Sun, D. Liu, H. Liao, H. Fan, & L. Gao (Eds.), Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers (pp. 209-221). (Communications in Computer and Information Science; Vol. 1330 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-2540-4_16

Zhang, Wei ; Gong, Tao ; Li, Jianxiu et al. / A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls. Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers. editor / Yuqing Sun ; Dongning Liu ; Hao Liao ; Hongfei Fan ; Liping Gao. Springer Science and Business Media Deutschland GmbH, 2021. pp. 209-221 (Communications in Computer and Information Science).

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title = "A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls",

abstract = "The investigation of attentional bias of depression based on P300 component has drawn interest within the last decades. Follow-up of previous research suggested the differential amplitudes between depression and normal controls (NCs) in response to various facial stimuli. In this paper, we used single-trials features in the occurrence of P300 to recognize depression from NCs. EEG activity was recorded from 24 patients and 29 NCs in a dot-probe task. We considered two traditionally used feature-extraction methods: ReliefF and principal component analysis (PCA). Then, the k-nearest neighbor (KNN), BFTree, C4.5, logistic regression and NaiveBayes were adopted in this study to make a performance comparison. The combination of NaiveBayes and PCA was applied to classify the P300 component evoked by sad-neutral pairs, which achieved higher classification accuracy than other classifiers. The classification accuracy was 98%. The classification results support that the P300 component of ERPs may reflect information processing of the specific response of depression to specific stimuli and may be applied as a physiologic index for aided diagnosis of depression in future research.",

keywords = "Classification, Depression, P300, Single-trials",

author = "Wei Zhang and Tao Gong and Jianxiu Li and Xiaowei Li and Bin Hu",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; 15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020 ; Conference date: 07-11-2020 Through 09-11-2020",

year = "2021",

doi = "10.1007/978-981-16-2540-4_16",

language = "English",

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publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "209--221",

editor = "Yuqing Sun and Dongning Liu and Hao Liao and Hongfei Fan and Liping Gao",

booktitle = "Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers",

address = "Germany",

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Zhang, W, Gong, T, Li, J, Li, X & Hu, B 2021, A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls. in Y Sun, D Liu, H Liao, H Fan & L Gao (eds), Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1330 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 209-221, 15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020, Shenzhen, China, 7/11/20. https://doi.org/10.1007/978-981-16-2540-4_16

A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls. / Zhang, Wei; Gong, Tao; Li, Jianxiu et al.
Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers. ed. / Yuqing Sun; Dongning Liu; Hao Liao; Hongfei Fan; Liping Gao. Springer Science and Business Media Deutschland GmbH, 2021. p. 209-221 (Communications in Computer and Information Science; Vol. 1330 CCIS).

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

TY - GEN

T1 - A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls

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AU - Gong, Tao

AU - Li, Jianxiu

AU - Li, Xiaowei

AU - Hu, Bin

PY - 2021

Y1 - 2021

N2 - The investigation of attentional bias of depression based on P300 component has drawn interest within the last decades. Follow-up of previous research suggested the differential amplitudes between depression and normal controls (NCs) in response to various facial stimuli. In this paper, we used single-trials features in the occurrence of P300 to recognize depression from NCs. EEG activity was recorded from 24 patients and 29 NCs in a dot-probe task. We considered two traditionally used feature-extraction methods: ReliefF and principal component analysis (PCA). Then, the k-nearest neighbor (KNN), BFTree, C4.5, logistic regression and NaiveBayes were adopted in this study to make a performance comparison. The combination of NaiveBayes and PCA was applied to classify the P300 component evoked by sad-neutral pairs, which achieved higher classification accuracy than other classifiers. The classification accuracy was 98%. The classification results support that the P300 component of ERPs may reflect information processing of the specific response of depression to specific stimuli and may be applied as a physiologic index for aided diagnosis of depression in future research.

AB - The investigation of attentional bias of depression based on P300 component has drawn interest within the last decades. Follow-up of previous research suggested the differential amplitudes between depression and normal controls (NCs) in response to various facial stimuli. In this paper, we used single-trials features in the occurrence of P300 to recognize depression from NCs. EEG activity was recorded from 24 patients and 29 NCs in a dot-probe task. We considered two traditionally used feature-extraction methods: ReliefF and principal component analysis (PCA). Then, the k-nearest neighbor (KNN), BFTree, C4.5, logistic regression and NaiveBayes were adopted in this study to make a performance comparison. The combination of NaiveBayes and PCA was applied to classify the P300 component evoked by sad-neutral pairs, which achieved higher classification accuracy than other classifiers. The classification accuracy was 98%. The classification results support that the P300 component of ERPs may reflect information processing of the specific response of depression to specific stimuli and may be applied as a physiologic index for aided diagnosis of depression in future research.

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SN - 9789811625398

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BT - Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers

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A2 - Liu, Dongning

A2 - Liao, Hao

A2 - Fan, Hongfei

A2 - Gao, Liping

PB - Springer Science and Business Media Deutschland GmbH

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ER -

Zhang W, Gong T, Li J, Li X, Hu B. A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls. In Sun Y, Liu D, Liao H, Fan H, Gao L, editors, Computer Supported Cooperative Work and Social Computing - 15th CCF Conference, Chinese CSCW 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 209-221. (Communications in Computer and Information Science). doi: 10.1007/978-981-16-2540-4_16

A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls

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