ArticleBMC psychiatry2023
Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise.
Article in BMC psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Trial
- Depression Recognition Using Daily Wearable-Derived Physiological Data.Sensors (Basel, Switzerland) · 2025Article
- Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study.BMC psychiatry · 2024Article
- Enhanced diagnostics for generalized anxiety disorder: leveraging differential channel and functional connectivity features based on frontal EEG signals.Scientific reports · 2024Article
- A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.PloS one · 2024Article
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Authors and funding
9 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the method of resting EEG assessment of depressive symptoms in college students and to clarify the relationship between physical activity level and depressive symptoms in college students.
methodsUsing a cross-sectional study design, 140 current full-time college students were recruited to complete the Self-Rating Depression Scale and the International Physical Activity Questionnaire, and 10-min resting EEGs were obtained.
results1) The power values of δ and α2 in the central (C3, C4) and parietal (P3, P4) regions of depressed college students were significantly higher than those of normal college students. And the degree of lateralization of δ, θ, α1, and α2 in the prefrontal regions (F3, F4) of depressed college students was significantly higher than that of normal college students (all P < 0. 008). 2) The recall rate of the depression recognition model for college students based on resting EEG was 66.67%, the precision was 65.05%, and the AUCs of the training group and validation group were 0.791 and 0.786, respectively, with better detection effects. 3) The two indicators, δ (C3 + C4) and α1 (F4-F3), are significantly correlated with IPAQ scores, and among college students who engage in ball games most commonly, those with a higher level of physical activity have lower δ (C3 + C4) and higher α1 (F4-F3), while among those who engage in resistance training most commonly, higher levels of physical activity are associated with lower δ (C3 + C4).
conclusionThe resting EEG of depressed college students has a certain specificity that can objectively assess the risk of developing depressive symptoms in college students. Physical activity is associated with abnormal EEG signals of depressive symptoms. Different types of physical activity may modulate the relationship between physical activity levels and EEG indicators.
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