MSRLNet: A Multi-Source Fusion and Feedback Network for EEG Feature Recognition in ADHD

TL;DR

This paper introduces MSRLNet, a new network for improving ADHD recognition using EEG data with better accuracy and stability.

Contribution

The novel contribution is the MSRLNet framework combining multi-source fusion, parallel CNN-GRU modeling, and feedback-driven optimization for EEG-based ADHD recognition.

Findings

MSRLNet achieved 98.90% accuracy on a public ADHD EEG dataset.

The model outperformed existing methods with an F1-score of 98.98% and a kappa of 0.979.

The framework improves training stability and small-sample adaptability through feature fusion and data augmentation.

Abstract

Background: Electroencephalography (EEG) has been widely used in Attention Deficit Hyperactivity Disorder (ADHD) recognition, but existing methods still suffer from limitations in dynamic modeling, small-sample adaptability, and training stability. This study proposes a Multi-Source Fusion and Feedback Network (MSRLNet) to enhance EEG-based ADHD recognition. Methods: MSRLNet comprises three modules: (1) Multi-Source Feature Fusion (MSFF), combining microstate and statistical features to improve interpretability; (2) a CNN-GRU Parallel Module (CGPM) for multi-scale temporal modeling; and (3) Performance Feedback–driven Parameter Optimization (PFPO) to enhance training stability. Feature-level data augmentation is introduced to alleviate overfitting in small-sample scenarios. Results: On a public dataset, MSRLNet achieved an accuracy of 98.90%, an F1-score of 98.98%, and a kappa of 0.979, all exceeding comparative approaches. Conclusions: MSRLNet shows high accuracy and robustness in ADHD EEG feature recognition, verifying its potential application value in clinical auxiliary diagnosis.

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