Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly impacts various key aspects of life, requiring accurate diagnostic methods. Electroencephalogram (EEG) signals are used in diagnosing ADHD, but proper preprocessing is crucial to avoid noise and artifacts that could lead to unreliable results. Method: This study utilized a public EEG dataset from children diagnosed with ADHD and typically developing (TD) children. Four preprocessing techniques were applied: no preprocessing (Raw), Finite Impulse Response (FIR) filtering, Artifact Subspace Reconstruction (ASR), and Independent Component Analysis (ICA). EEG recordings were segmented, and features were extracted and selected based on statistical significance. Classification was performed using Machine Learning models, as XGBoost, Support Vector Machine, and K-Nearest Neighbors. Results: The absence of preprocessing leads to artificially high classification accuracy due to noise. In contrast, ASR and ICA preprocessing techniques significantly improved the reliability of results. Segmenting EEG recordings revealed that later segments provided better classification accuracy, likely due to the manifestation of ADHD symptoms over time. The most relevant EEG channels were P3, P4, and C3. The top features for classification included Kurtosis, Katz fractal dimension, and power spectral density of Delta, Theta, and Alpha bands. Conclusions: Effective preprocessing is essential in EEG-based ADHD diagnosis to prevent noise-induced biases. This study identifies crucial EEG channels and features, providing a foundation for further research and improving ADHD diagnostic accuracy. Future work should focus on expanding datasets, refining preprocessing methods, and enhancing feature interpretability to improve diagnostic accuracy and model robustness for clinical use.

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