Mental health (MH) and attention deficit hyperactivity disorder (ADHD) are inextricably linked, having the same symptoms and complications. The goal of this research is to pinpoint the precise brain areas that cause ADHD in children and to make it possible to diagnose the disorder early. The study intends to provide a trustworthy diagnosis framework that enables prompt intervention using cutting-edge electroencephalogram (EEG) data analysis and machine learning (ML) approaches. This study uses EEG decomposition for improved ADHD detection. Decomposition techniques, such as the discrete cosine transform (DCT), short-time Fourier transform (STFT), and empirical mode decomposition (EMD), are used to break down EEG signals into sub-bases. As STFT demonstrated the highest accuracy, in further studies ML algorithms use STFT sub-bands on various combinations of brain regions as feed-ins to detect ADHD. The results demonstrate that STFT methods outperform DCT and EMD. The trial outcomes revealed that, when utilizing a combination of 19 electrode sites, the STFT approach achieved the best accuracies, specifically 96% with light gradient-boosting machine (LightGBM) models. However, when utilizing STFT with LightGBM, the combination of Fp1F3C3C4P4 (5 electrode placements) yields 91% accuracy and 93% on Fp1F3C3C4P4 as well as Fp1F3C3C4F8. While our previous research has separately investigated the efficacy of EMD and STFT/DCT, this presents the first comprehensive, head-to-head comparison of all three techniques within a unified framework. We conclusively demonstrate that STFT-based features, when paired with a LightGBM classifier, achieve a new state-of-the-art accuracy of 96%. Building on this superior model, we conduct a novel and granular electrode-reduction analysis to identify a minimal 5-channel configuration that maintains over 91% accuracy, directly addressing the need for scalable and cost-effective diagnostic systems and establishing a clear pathway for their development.
Deshmukh et al. (2025) studied this question.