###### Abstract
With the advancement of signal processing techniques and the integration of artificial intelligence (AI), the effectiveness of EEG in distinguishing AD from other neurological conditions has improved, offering promising prospects for clinical management [44, 45, 46], enabling more efficient and accurate classification and artifact removal, and overcoming the limitations of conventional methods [47]. AI not only lightens the clinical workload through automation [48, 49, 50, 51] but also opens up greater standardization in analysis protocols and the discovery of new neurophysiological markers [52, 53, 54, 55, 56, 57]. The introduction of methodologies based on fuzzy logic, in the field of AI, represents a significant advancement in the removal of artifacts from EEG signals. This approach, inspired by the way human reasoning manages ambiguous or incomplete information, is particularly effective in managing the uncertainty and imprecision typical of biological signals, proving essential to overcoming the challenges related to the intrinsic variability of EEG data [58, 59, 60, 61, 62, 63, 64, 65]. The use of fuzzy techniques is motivated by their effectiveness in managing the complexity of neurological signals, significantly improving the ability to differentiate between relevant brain activity and artifact distortions [66, 67, 68, 69, 70]. Takagi-Sugeno (TS) type fuzzy systems prove to be extremely effective tools in removing artifacts from EEG signals, thanks to their mathematical structure that allows the output functions to be defined based on mathematical models rather than on fuzzy sets, as happens in Mamdani systems. This feature gives TS systems not only greater precision and efficiency, but also facilitates analysis for clinicians, thanks to their intuitiveness and ease of use. Unlike other more complex AI techniques, which operate in a \"black-box\" mode, TS systems allow for greater transparency in the decision-making process. A further advantage of TS systems compared to Mamdani systems is the possibility of structuring the output in network mode. This configuration makes TS systems particularly suitable for the application of neural learning algorithms, significantly expanding the potential of these systems in modeling and analyzing EEG signals. The ability to integrate neural networks into fuzzy TS systems improves artifact removal and enhances the applicability of these techniques in complex clinical contexts, where the precision and reliability of signal analysis are critical [70]. Despite extensive literature on the use of fuzzy logic techniques, neural networks, and other artificial intelligence techniques to remove artifacts from EEG signals [71, 72, 73, 74], in-depth studies on intuitionistic fuzzy systems (IFS) are lacking, highlighting the need for a review examining this innovative area. IFSs, offering superior management of uncertainties and ambiguities, promise significant improvements in the removal of EEG artifacts. A review focused on IFS could bridge the gap between scientific discoveries and clinical applications, improving accuracy and efficiency in EEG analysis [75, 76]. The main contributions of this review are summarized in the following list. 1. _In-depth evaluation of Type-1 Takagi-Sugeno fuzzy systems in the removal of EEG signal artifacts_ In an era where neuroscience and technology increasingly intersect, the quality of EEG data emerges as a critical juncture for the success of numerous clinical and research applications. Against this backdrop, the central goal of this review is to rigorously explore and delineate the effectiveness of Type-1 Takagi-Sugeno fuzzy systems, a promising frontier in advanced EEG signal processing. Our investigation specifically focuses on their ability not only to identify but also to effectively eliminate artifacts, including those generated internally, such as involuntary muscle movements, as well as external ones, like electrical interference.