Resting-state frontal, frontlateral, and parietal alpha asymmetry:A pilot study examining relations with depressive disorder type and severity

Marcu, Gabriela; Szekely-Copîndean, Raluca D.; Radu, Ana-Maria; Bucuță, Mihaela D.; Fleacă, Radu S.; Tănăsescu, Ciprian; Roman, Mihai D.; Boicean, Adrian; Băcilă, Ciprian I.

doi:10.3389/fpsyg.2023.1087081

Cited by 3 publications

(1 citation statement)

References 65 publications

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“…Frontal asymmetry in EEG is frequently used as a risk predictor of depression, associated with emotion. Marcu et al [35] explored the correlation between resting-state EEG alpha asymmetry across various sites (frontal, frontolateral, and parietal) and diverse severities of depressive disorders to assess the biomarker's validity, reliability, and predictive value. Their findings suggested frontal asymmetry as a reliable marker, while parietal asymmetry was considered a potential marker for expanding the comprehension of depression's physiological foundations.…”

Section: Discussionmentioning

confidence: 99%

Effects of the Hyperparameters on CNNs for MDD Classification Using Resting-State EEG

Yang,

Lee

2023

Electronics

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To monitor patients with depression, objective diagnostic tools that apply biosignals and exhibit high repeatability and efficiency should be developed. Although different models can help automatically learn discriminative features, inappropriate adoption of input forms and network structures may cause performance degradation. Accordingly, the aim of this study was to systematically evaluate the effects of convolutional neural network (CNN) architectures when using two common electroencephalography (EEG) inputs on the classification of major depressive disorder (MDD). EEG data for 21 patients with MDD and 21 healthy controls were obtained from an open-source database. Five hyperparameters (i.e., number of convolutional layers, filter size, pooling type, hidden size, and batch size) were then evaluated. Finally, Grad-CAM and saliency map were applied to visualize the trained models. When raw EEG signals were employed, optimal performance and efficiency were achieved as more convolutional layers and max pooling were used. Furthermore, when mixed features were employed, a larger hidden layer and smaller batch size were optimal. Compared with other complex networks, this configuration involves a relatively small number of layers and less training time but a relatively high accuracy. Thus, high accuracy (>99%) can be achieved in MDD classification by using an appropriate combination in a simple model.

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Section: Discussionmentioning

confidence: 99%