## 2 Novelty and Contributions  
This study, which explores advanced EEG data analysis, particularly for the early diagnosis of AD, clearly demonstrates how the innovative approaches proposed enhance the precision in artifact removal and refine the diagnostic process. The document establishes a robust methodological framework that integrates systems of advanced fuzzy logic and intuitionistic fuzzy systems, elements particularly effective in managing the uncertainties typical of EEG data. These systems, capable of accurately discerning between legitimate neurological activity and noise, significantly elevate diagnostic accuracy.  
The integration of such fuzzy systems with deep learning technologies represents a significant advancement, substantially expanding the boundaries of EEG analysis. This synergistic combination facilitates a deeper and more accurate interpretation of neurophysiological markers associated with AD, enhancing the potential for earlier and more precise diagnoses. This review conducts a rigorous comparison between the effectiveness of fuzzy systems and traditional non-fuzzy methods in artifact removal, providing a detailed evaluation that highlights the advantages of integrating advanced computational technologies. Through the analysis of specific case studies and empirical data, this article illuminates the superior capabilities of fuzzy systems in various clinical and research settings, emphasizing their value in enhancing the integrity of EEG data.  
It is important to note that the scientific studies analyzed during the review were conducted following rigorous protocols on samples of individuals with well-defined demographic characteristics, using standardized methodologies and instrumentation to ensure the reproducibility and validity of the results. This aspect is crucial, as it ensures that the conclusions are supported by data collected under controlled and comparable conditions, further enhancing the robustness and reliability of the methodological innovations proposed.  
These contributions, ranging from technological innovation to investigative methodology, not only strengthen the scientific knowledge base in the field of EEG analysis but also provide more effective and reliable tools for clinical professionals. The fusion of fuzzy logic and deep learning techniques in EEG signal analysis establishes new standards for neuroscientific research and opens new prospects for improving the diagnosis and management of neurodegenerative diseases, promising to guide the field towards future diagnostic innovations.  
The remainder of this review unfolds as follows, guiding the reader through a structured exploration of our findings and discussions. Once standardized protocols for advanced clinical practice in the use of EEG have been introduced (Section 3), Section 4 focuses on the identification and elimination of artifacts in EEG signals. Next, Section 5 explores and analyzes the fundamental principles of BSS techniques, and then Section 6 asks how wavelet transforms can improve the analysis of EEG signals. Section 7 introduces the basic concepts of EMD and then leaves room for the examination of PCA (Section 8). The key concepts of adaptive filters, and the basic principles of machine learning for the analysis of EEG signals, are discussed in Sections 9 and 10, respectively. Section 11 discusses the integration of different techniques for a hybrid approach to artifact removal, while Section 12 focuses attention on fuzzy techniques, with particular emphasis on more innovative intuitionistic approaches. Moreover, scientific development and technology transfer are discussed in Section 13.  
Finally, conclusions and some reflections on future research developments conclude the review, offering perspectives on how these technologies can be further developed and implemented.