Influence of aerobic exercise and dietary intervention on executive function in obese children: depthwise separable convolutional network-based electroencephalogram analysis

Introduction: Childhood obesity not only impairs physical health but also adversely affects executive function. Although aerobic exercise and dietary interventions can improve cognition, traditional assessment methods are inadequate for capturing real-time brain activity. Moreover, there is a lack of research utilizing deep learning to analyze the electroencephalogram (EEG) for investigating the neural mechanisms underlying executive function in children with obesity. Methods: This study enrolled 100 students (50 in the obese group, 50 in the normal-weight group), all of whom underwent aerobic exercise training and dietary intervention. Executive function was assessed before and after the intervention, and EEG signals were collected. A deep separable convolutional network (DSCN) algorithm was employed to analyze the EEG data to improve classification accuracy and feature extraction efficiency. Results: the DSCN algorithm demonstrated significantly higher performance in EEG signal classification across accuracy, precision, recall, F1-score and AUC compared to traditional algorithms (P < 0:05). After the intervention, both behavioral indicators of executive function and EEG features showed significant improvement in the obese group, with effect sizes (Cohen's d) ranging from medium to large (0.65–0.90). Discussion: The aerobic exercise training system integrated with the intelligent DSCN algorithm effectively enhances executive function in children with obesity. The DSCN-based EEG analysis tool offers a novel approach for studying the neural mechanisms of exercise interventions, demonstrating significant theoretical innovation and potential for clinical application.

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