Enhancing EEG-based classification of depression patients using spatial information

Enhancing EEG-based classification of depression patients using spatial information

Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli.

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Bibliographic Details
Main Authors: Jiang, Chao, Li, Yingjie, Tang, Yingying, Guan, Cuntai
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Task Analysis
Depression
Online Access:https://hdl.handle.net/10356/160325
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-160325
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spelling sg-ntu-dr.10356-1603252022-07-19T06:09:49Z Enhancing EEG-based classification of depression patients using spatial information Jiang, Chao Li, Yingjie Tang, Yingying Guan, Cuntai School of Computer Science and Engineering Engineering::Computer science and engineering Task Analysis Depression Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli. Published version This work was supported in part by the National Natural Science Fund of China under Grant 61571283; in part by the Shanghai Municipal Science and Technology Major Project under Grant 2018SHZDZX01; in part by the ZJLab; in part by the Shanghai Science and Technology Committee Foundations under Grant 16ZR1430500, Grant 19411969100, Grant 19410710800, and Grant 18411952200. 2022-07-19T05:53:32Z 2022-07-19T05:53:32Z 2021 Journal Article Jiang, C., Li, Y., Tang, Y. & Guan, C. (2021). Enhancing EEG-based classification of depression patients using spatial information. IEEE Transactions On Neural Systems and Rehabilitation Engineering, 29, 566-575. https://dx.doi.org/10.1109/TNSRE.2021.3059429 1534-4320 https://hdl.handle.net/10356/160325 10.1109/TNSRE.2021.3059429 33587703 2-s2.0-85100945481 29 566 575 en IEEE Transactions on Neural Systems and Rehabilitation Engineering © 2021 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Task Analysis
Depression
spellingShingle Engineering::Computer science and engineering
Task Analysis
Depression
Jiang, Chao
Li, Yingjie
Tang, Yingying
Guan, Cuntai
Enhancing EEG-based classification of depression patients using spatial information
description Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Jiang, Chao
Li, Yingjie
Tang, Yingying
Guan, Cuntai
format Article
author Jiang, Chao
Li, Yingjie
Tang, Yingying
Guan, Cuntai
author_sort Jiang, Chao
title Enhancing EEG-based classification of depression patients using spatial information
title_short Enhancing EEG-based classification of depression patients using spatial information
title_full Enhancing EEG-based classification of depression patients using spatial information
title_fullStr Enhancing EEG-based classification of depression patients using spatial information
title_full_unstemmed Enhancing EEG-based classification of depression patients using spatial information
title_sort enhancing eeg-based classification of depression patients using spatial information
publishDate 2022
url https://hdl.handle.net/10356/160325
_version_ 1739837366709780480

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