###### Abstract  
Effective management of EEG artifacts is pivotal for accurate neurological diagnostics, particularly in detecting early stages of Alzheimer's disease. This review delves into the cutting-edge domain of fuzzy logic techniques, emphasizing intuitionistic fuzzy systems, which offer refined handling of uncertainties inherent in EEG data. These methods not only enhance artifact identification and removal but also integrate seamlessly with other AI technologies to push the boundaries of EEG analysis. By exploring a range of approaches from standard protocols to advanced machine learning models, this paper provides a comprehensive overview of current strategies and emerging technologies in EEG artifact management. Notably, the fusion of fuzzy logic with neural network models illustrates significant advancements in distinguishing between genuine neurological activity and noise. This synthesis of technologies not only improves diagnostic accuracy but also enriches the toolset available to researchers and clinicians alike, facilitating earlier and more precise identification of neurodegenerative diseases. The review ultimately underscores the transformative potential of integrating diverse computational techniques, setting a new standard in EEG analysis and paving the way for future innovations in medical diagnostics. fuzzy logic techniques; intuitionistic fuzzy systems; neurodegenerative disease diagnosis; AI integration in EEG analysis; clinical EEG applications +
Footnote †: journal: Medical Image Analysis  
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Footnote 1: email: [EMAIL_ADDRESS]  
diagnosis and monitoring of AD, thanks to its ability to detect brain abnormalities typical of the disease [25, 26]. Unlike more advanced and invasive diagnostic techniques such as functional magnetic resonance imaging (fMRI) [27, 28] and positron emission tomography (PET) [29, 30], which involve staying in confined environments or exposure to radioactive substances and present logistical challenges, EEG--obtained via the 10-20 system--stands out for its non-invasiveness, ease of use, and cost-effectiveness, making it particularly suitable for repeated studies and monitoring of patients. This makes it essential in the early diagnosis and monitoring of AD [27, 28, 29, 30, 31]. The usefulness of EEG for the diagnosis and monitoring of AD is mainly linked to its ability to detect specific neurophysiological markers that indicate functional brain alterations, such as the slowing of global electrical activity of the brain as evidenced by changes in frequency bands named \(\delta\), \(\theta\), \(\alpha\), \(\beta\), and \(\gamma\)[32]. The band \(\delta\) (\(0.5\div 4\) Hz) signals slow brain activity linked to cortical damage, \(\theta\) (\(4\div 8\) Hz) indicates transitions between sleep and wakefulness suggesting potential dysfunctions, \(\alpha\) (\(8\div 12\) Hz) is associated with resting states and reflects the alteration of brain organization in AD, and \(\beta\) (\(12\div 30\) Hz) highlights levels of attention and mental activity, which is useful for observing cognitive changes in the patient [32, 33]. Finally, the \(\gamma\) rhythm, above 30 Hz, is associated with complex cognitive processes such as object recognition and meaning attribution, and it is mainly detectable in the frontal regions [32, 33, 34, 35, 36, 37]. Detailed EEG analysis, which includes the observation of specific changes in frequency bands, helps define a neurophysiological profile of AD [38, 39, 40]. However, the presence of artifacts in EEG signals, originating from both physiological and external sources, can mask or distort crucial frequency bands of the EEG signal, compromising the clarity and integrity of essential neural information. Therefore, there is a need for advanced techniques to classify and clean EEG signals [41, 42, 43].