EEG-Based ADHD Detection Framework Using Local Pattern Descriptors and Bayesian-Optimized Machine Learning Classifiers

Abstract

Although clinical diagnosis of childhood Attention-Deficit/Hyperactivity Disorder (ADHD) relies heavily on subjective behavioral assessments, electroencephalography (EEG) offers a low-cost, noninvasive objective alternative. This study proposes an approach employing 1D texture analysis of EEG signals for ADHD classification. Specifically, 1D Local Binary Pattern (1D-LBP), 1D Local Gradient Pattern (1D-LGP), and 1D Local Neighbor Descriptive Pattern (1D-LNDP) capture local amplitude relationships across 19 electrodes in 2-second frames, yielding a combined 570-dimensional feature space based on binary transitions. To optimize this, the minimum Redundancy Maximum Relevance (mRMR) method is used to select the 190 most discriminative features (Hybrid-190). System performance was then evaluated using various classifiers, with hyperparameters tuned via Bayesian optimization within a 5-fold cross-validation scheme. Tested on a benchmark dataset of 61 children with ADHD and 60 healthy controls, the framework achieved a peak segment-level accuracy of 99.63%, a specificity of 99.65%, an F1-score of 99.67%, and an Area Under the Curve (AUC) of 0.9987 using a k-Nearest Neighbor (KNN) classifier. Under a subject-independent protocol with per-participant aggregation, it attained a participant-level accuracy of 78.50% and an AUC of 0.8600 with a Random Forest classifier. A topographic accuracy peak was also observed in the right temporal-occipital region near the T6 electrode. Among individual descriptors, 1D-LGP achieved the highest standalone segment-level accuracy at 99.26%. Ultimately, this framework provides an interpretable and computationally efficient EEG-based approach whose subject-independent performance supports its potential for clinical decision-support systems.