Adaptive Channel Learning for Early ADHD Prediction using Attention-based EEGNet

Attention Deficit Hyperactivity Disorder (ADHD) has been one of the most significant clinical issues because of the difficulty of diagnosing the disorder at an early childhood developmental stage, where normal behavioral tests tend to be subjective and unreliable. The article proposes an EEG-based automated model of predicting ADHD at an early stage through an Attention-Enhanced EEGNet (AE-EEGNet) architecture. One of the proposed models incorporates an adaptive channel attention process to highlight the most informative EEG channels and suppress those that are redundant and noisy. Multi-channel EEG data are subject to extensive preprocessing such as missing values, standardisation, sliding window time segmentation, bandpass filters and more sophisticated data augmentation techniques such as time shifting, amplitude scaling, Gaussian noise injection and channel dropout to enhance model robustness. The AE-EEGNet structure is a hybrid of time-based convolution, depthwise separable convolution and attention guided feature learning, with the subsequent optimization of classification layers. The rigorous 5-fold cross-validation protocol is used as a model training and evaluation protocol to guarantee the statistical reliability and generalization. Based on the experimental data, it is clear that the suggested framework is always superior to the baseline EEGNet model in terms of classification accuracy and also high levels of precision, recall, F1-score, and area under the ROC curve. The results have shown that attention directed deep learning of EEG data has a potent and dependable solution to early ADHD detection and has a high chance of objective clinical decision support.