Objective ADHD Analysis with EEG and Ensemble Machine Learning

This project investigates objective differentiation of ADHD and healthy control groups using EEG signals and machine learning.

A multi-feature pipeline (including enhanced Higuchi fractal dimensions, spectral features, Hjorth parameters, and statistical descriptors) was combined with ensemble classifiers and cross-validation.

The study focuses on building reproducible, data-driven decision support methods for neurodiagnostic workflows.