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.

翻译：背景：注意缺陷多动障碍（ADHD）是一种普遍存在的神经发育障碍，严重影响生活的多个关键方面，需要准确的诊断方法。脑电图（EEG）信号被用于ADHD诊断，但适当的预处理对于避免噪声和伪迹导致不可靠结果至关重要。方法：本研究使用了一个来自ADHD诊断儿童和典型发育（TD）儿童的公开EEG数据集。应用了四种预处理技术：无预处理（原始数据）、有限脉冲响应（FIR）滤波、伪迹子空间重建（ASR）和独立成分分析（ICA）。对EEG记录进行分段，并基于统计显著性提取和选择特征。使用机器学习模型进行分类，如XGBoost、支持向量机和K近邻算法。结果：未进行预处理会因噪声导致分类准确率虚高。相比之下，ASR和ICA预处理技术显著提高了结果的可靠性。对EEG记录进行分段分析发现，后期分段提供了更好的分类准确率，这可能源于ADHD症状随时间推移而显现。最相关的EEG通道是P3、P4和C3。用于分类的最重要特征包括峰度、Katz分形维数以及Delta、Theta和Alpha频段的功率谱密度。结论：在基于EEG的ADHD诊断中，有效的预处理对于防止噪声引起的偏差至关重要。本研究识别了关键的EEG通道和特征，为进一步研究和提高ADHD诊断准确性奠定了基础。未来的工作应侧重于扩展数据集、优化预处理方法以及增强特征可解释性，以提高临床应用的诊断准确性和模型鲁棒性。