DOI: 10.62520/fujece.1950290 ISSN: 2822-2881

LBP-Moment: A Lightweight and Interpretable EEG Framework for Automated ADHD Detection

İrem Taşcı, Ömer Faruk Göktaş, Kübra Yıldırım, Abdul Hafeez Baig, Mehmet Bayğın, Burak Tasci, Sengul Dogan, Türker Tuncer
Show PDF Cite
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Here we present LBP-Moment, a lightweight and interpretable framework for automated ADHD classification from electroencephalography (EEG). Each 5-s segment was represented by a 256-dimensional local binary pattern histogram and 40 statistical features (IST40). Iterative Chi-square selection reduced the resulting 296-dimensional space to 132 features. A k-nearest-neighbour classifier (k=1; Manhattan distance) was evaluated by segment-level 10-fold cross-validation. The model achieved 97.65% accuracy, a 97.65% F1-score, a Cohen's kappa of 0.9527 and an area under the receiver operating characteristic curve of 0.9762. Ablation analysis showed complementary contributions from LBP and IST40; feature selection removed 55.4% of the features and increased accuracy by 0.35 percentage points. These findings support a transparent, CPU-based feature-engineering approach, but participant-wise and external validation are required before clinical interpretation.

More from our Archive