Artificial intelligence-driven electroencephalogram analysis for early attention deficit hyperactivity disorder detection in children to prevent learning disabilities and mental health challenges

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.