Directed brain network analysis in anxious and non-anxious depression based on EEG source reconstruction and graph theory

Alaei, Hesam Shokouh; Ghoshuni, Majid; Vosough, Iraj

doi:10.1016/j.bspc.2023.104666

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…(3) Video-based facial expression analysis method [9][10][11]. ( 4) Electroencephalogram (EEG) signal-based data analysis method [12][13][14]. Dinkel et al [3] proposed a text-based multi-task BGRU network with pretrained word embeddings for the responses of simulating patients in clinical interviews, demonstrating that pre-trained words are effective for depression detection.…”

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of depression based on attention mechanism and feature pyramid model

Xu,

Huo,

et al. 2024

PLoS ONE

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Currently, most diagnoses of depression are evaluated by medical professionals, with the results of these evaluations influenced by the subjective judgment of physicians. Physiological studies have shown that depressed patients display facial movements, head posture, and gaze direction disorders. To accurately diagnose the degree of depression of patients, this paper proposes a comprehensive framework, Cross-Channel Attentional Depression Detection Network, which can automatically diagnose the degree of depression of patients by inputting information from the facial images of depressed patients. Specifically, the comprehensive framework is composed of three main modules: (1) Face key point detection and cropping for video images based on Multi-Task Convolutional Neural Network. (2) The improved Feature Pyramid Networks model can fuse shallow features and deep features in video images and reduce the loss of miniscule features. (3) A proposed Cross-Channel Attention Convolutional Neural Network can enhance the interaction between tensor channel layers. Compared to other methods for automatic depression identification, a superior method was obtained by conducting extensive experiments on the depression dataset AVEC 2014, where the Root Mean Square Error and the Mean Absolute Error were 8.65 and 6.66, respectively.

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Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of depression based on attention mechanism and feature pyramid model

Xu,

Huo,

et al. 2024

PLoS ONE

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A review of graph theory-based diagnosis of neurological disorders based on EEG and MRI

Yan,

Liu,

Cai

et al. 2024

Neurocomputing

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-wave-based exploration of sensitive EEG features and classification of situation awareness

Feng,

Liu,

Wanyan

et al. 2024

Aeronaut. j.

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The purpose of this study was to explore the electroencephalogram (EEG) features sensitive to situation awareness (SA) and then classify SA levels. Forty-eight participants were recruited to complete an SA standard test based on the multi-attribute task battery (MATB) II, and the corresponding EEG data and situation awareness global assessment technology (SAGAT) scores were recorded. The population with the top 25% of SAGAT scores was selected as the high-SA level (HSL) group, and the bottom 25% was the low-SA level (LSL) group. The results showed that (1) for the relative power of $\beta$ 1 (16–20Hz), $\beta$ 2 (20–24Hz) and $\beta$ 3 (24–30Hz), repeated measures analysis of variance (ANOVA) in three brain regions (Central Central-Parietal, and Parietal) × three brain lateralities (left, midline, and right) × two SA groups (HSL and LSL) showed a significant main effect for SA groups; post hoc comparisons revealed that compared with LSL, the above features of HSL were higher. (2) for most ratio features associated with $\beta$ 1 ∼ $\beta$ 3, ANOVA also revealed a main effect for SA groups. (3) EEG features sensitive to SA were selected to classify SA levels with small-sample data based on the general supervised machine learning classifiers. Five-fold cross-validation results showed that among the models with easy interpretability, logistic regression (LR) and decision tree (DT) presented the highest accuracy (both 92%), while among the models with hard interpretability, the accuracy of random forest (RF) was 88.8%, followed by an artificial neural network (ANN) of 84%. The above results suggested that (1) the relative power of $\beta$ 1 ∼ $\beta$ 3 and their associated ratios were sensitive to changes in SA levels; (2) the general supervised machine learning models all exhibited good accuracy (greater than 75%); and (3) furthermore, LR and DT are recommended by combining the interpretability and accuracy of the models.

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Directed brain network analysis in anxious and non-anxious depression based on EEG source reconstruction and graph theory

Cited by 4 publications

References 49 publications

Automatic diagnosis of depression based on attention mechanism and feature pyramid model

Automatic diagnosis of depression based on attention mechanism and feature pyramid model

A review of graph theory-based diagnosis of neurological disorders based on EEG and MRI

-wave-based exploration of sensitive EEG features and classification of situation awareness

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