METHODS: This study presents a comprehensive systematic review focusing on the applications of deep learning in detecting MCI and AD using electroencephalogram (EEG) signals. Through a rigorous literature screening process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research has investigated 74 different papers in detail to analyze the different approaches used to detect MCI and AD neurological disorders.
RESULTS: The findings of this study stand out as the first to deal with the classification of dual MCI and AD (MCI+AD) using EEG signals. This unique approach has enabled us to highlight the state-of-the-art high-performing models, specifically focusing on deep learning while examining their strengths and limitations in detecting the MCI, AD, and the MCI+AD comorbidity situations.
CONCLUSION: The present study has not only identified the current limitations in deep learning area for MCI and AD detection but also proposes specific future directions to address these neurological disorders by implement best practice deep learning approaches. Our main goal is to offer insights as references for future research encouraging the development of deep learning techniques in early detection and diagnosis of MCI and AD neurological disorders. By recommending the most effective deep learning tools, we have also provided a benchmark for future research, with clear implications for the practical use of these techniques in healthcare.