Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.

Objective: This review aims to comprehensively review the methodologies and advancements in using electroencephalography (EEG) for the early diagnosis of Alzheimer's disease (AD), addressing the limitations of traditional diagnostic tools. Methods: We conducted a comprehensive analysis of current research, encompassing the complete EEG analysis pipeline from signal acquisition and preprocessing to feature extraction-including time-frequency and brain network metrics-and the application of machine learning and deep learning algorithms. Results: Characteristic EEG alterations, such as spectral slowing and reduced signal complexity, are associated with early AD. Advanced feature extraction combined with intelligent algorithms significantly enhances diagnostic performance, with some studies reporting classification accuracies exceeding 95%. Integration of EEG with multimodal data (e.g., MRI, genetic markers) further improves diagnostic robustness. Conclusions: EEG is a promising, non-invasive, and cost-effective tool for early AD detection in appropriate clinical and research settings. The integration of advanced signal processing with intelligent algorithms can significantly improves diagnostic precision, though clinical translation requires standardized protocols and validation on larger, diverse cohorts. Significance: This work highlights the potential of EEG to facilitate accessible, early-stage AD screening, which is crucial for timely intervention and may contribute to reducing the disease's future socioeconomic burden.