Neural complexity in patients with poststroke depression: A resting EEG study

Zhang, Ying; Wang, Chunfang; Sun, Chongling; Zhang, Xi; Wang, Yongjun; Qi, Hongzhi; He, Feng; Zhao, Xin; Wan, Baikun; Du, Jingang; Ming, Dong

doi:10.1016/j.jad.2015.09.017

Cited by 38 publications

(43 citation statements)

References 63 publications

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“…A number of studies are based on the automated classification of normal and depression-related EEG signals. This proposed automatic classification system could serve as a useful diagnostic and monitoring tool for the detection of depression [20,79,133,135,226,228,231].…”

Section: Othermentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Othermentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…Patients with PSD also had significantly greater levels of alpha power in frontal and right temporal regions than those without PSD (Zhang et al, 2015). These findings suggest that PSD results in increased power across both slow delta and theta frequencies and fast alpha activity.…”

Section: Psd and Qeeg Measuresmentioning

confidence: 71%

“…In addition QEEG indices have been linked to cognitive performance in older healthy adults and in those with mild cognitive impairment (Cummins et al, 2008). Although not as comprehensively studied PSD has been associated with several resting state QEEG indices (Zhang et al, 2015). In non-stroke populations distinct EEG patterns have been found when depressed participants have been compared to healthy controls (Grin-Yatsenko et al, 2009).…”

Section: Eeg Methodologymentioning

confidence: 99%

“…EEG was recorded in 43 patients within a year of stroke, of those 21 had PSD diagnosed using DSM-IV criteria (Zhang et al, 2015). Patients with PSD were found to have significantly greater delta power in frontal and central electrodes, as well as greater theta power across the entire scalp than patients without PSD.…”

Section: Psd and Qeeg Measuresmentioning

confidence: 99%

“…Similar results involving delta frequency power and depression in a stroke population were reported in a recent study. Forty-three stroke patients were included; of those 21 had PSD (identified using DSM-IV criteria at the time of EEG recording) (Zhang et al, 2015). EEG was recorded within a year of stroke (using 16 electrodes following the 10-20 international system).…”

Section: Introductionmentioning

confidence: 99%

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Investigating the ability of post-stroke EEG measures of brain dysfuntion to inform early prediction of cognitive impairment or depression outcomes

Schleiger¹

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Electrophysiological changes in poststroke subjects with depressed mood: A quantitative EEG study

Wang

Chen

Sun

et al. 2018

Int J Geriat Psychiatry

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Neural complexity in patients with poststroke depression: A resting EEG study

Abstract: Compared with conventional spectral analysis, complexity of neural activity using LZC was more sensitive and stationary in the measurement of abnormal brain activity in PSD patients and may offer a potential approach to facilitate clinical screening of this disease.

Cited by 38 publications

References 63 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Investigating the ability of post-stroke EEG measures of brain dysfuntion to inform early prediction of cognitive impairment or depression outcomes

Electrophysiological changes in poststroke subjects with depressed mood: A quantitative EEG study

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