HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

Zhang, Wan; Huang, Jiajin; Zhang, Hao; Zhou, Hu; Yang, Jie; Zhong, Ning

doi:10.1109/access.2020.2971656

“…Moreover, the gained accuracy should be evaluated with more data. Z. Wan et al [45] set the objective of MDD prediction using HybridEEGNet which was a sort of Convolutional Neural Network with the assistance of feature analysis as the second main aspect of this proposed model. Two different sorts of convolutional filters adopted to learn the synchronous and regional characteristics from EEG signals in order to analyze them.…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Safayari¹,

Bolhasani²

2021

Preprint

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Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

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“…Moreover, the gained accuracy should be evaluated with more data. Z. Wan et al [46] set the objective of MDD prediction using HybridEEGNet which was a sort of Convolutional Neural Network with the assistance of feature analysis as the second main aspect of this proposed model. Two different sorts of convolutional filters adopted to learn the synchronous and regional characteristics from EEG signals in order to analyze them.…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Safayari¹,

Bolhasani²

2021

Preprint

View full text Add to dashboard Cite

Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

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“…[51] Visual [52] N/A [53], [54], [55], [56], [57], [58], [59], [60], [61], [45], [33], [62], [63], [64], [65], [66] Depression both mild and severe stages Emotional Note reading [67] Audio [68] Emotional Picture [69], [70], [71], [72] Distractor and target [73] N/A [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88] Depression for other reasons (stress/ epilepsy) Audio [89], [90] N/A [91], [92], [93] This…”

Section: Mild Depressionmentioning

confidence: 99%

“…Moreover, many software and toolboxes (such as-MATLAB EEGLAB epoch rejection function; Net Station Waveform; BESA; QExG; TrimOutlier) manage built-in plugins that are used for automatic artefacts removal. Blackman window [55], [69] Used to remove high-band noise caused by EMG Z-score normalization [44], [80], [85], [50] Used to eliminate the amplitude scaling problem and remove offset effects.…”

Section: Research Articles Remarksmentioning

confidence: 99%

“…Research Articles 100-90 [89], [74], [51], [53], [64], [101], [66], [54], [2], [56], [57], [77], [70], [75], [58], [80], [81], [44], [52], [45], [93], [65], [34], [62], [63], [86], [99], [87], [88] 89.9-80 [49], [68], [102], [69], [47], [59], [78], [92], [48], [43], [50], [72] 79.9-70 [67], [82], [100], [90], [61], [85], [73], [71] 69.9-60 [79], [91] In 2017, Cai et al presented a journal where he showed a depression identification model comparison between five classifiers on 265 participants where the control participants were 113 and depressed subjects were 152. Among five classifiers (SVM, KNN, DT, LR, R...…”

Section: Accuracy (%)mentioning

confidence: 99%

See 1 more Smart Citation

Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

Dev

¹

,

Roy

²

,

Islam

³

et al. 2022

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Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients' responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope, this study will create an opportunity to explore more in the future for EEG as diagnostic tools by analyzing brain functional connectivity for focusing on clinical interventions.

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HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

Cited by 45 publications

References 38 publications

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

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