###### Abstract
Through a detailed analysis of selected case studies, we aim not only to assess the performance of these systems, but also to illustrate how they can be implemented to significantly enhance the integrity of EEG data. With an approach that balances technical rigor and practical applicability, this review endeavors to provide a comprehensive overview that may serve as a springboard for further innovations in the field of biomedical signal processing. 2. _Comparative evaluation of fuzzy system performance versus traditional non-fuzzy methods in EEG signal artifact removal_ In an ongoing effort to refine the accuracy of EEG signal analysis techniques, which is essential for both clinical applications and research, the need for a comprehensive comparative analysis between fuzzy systems and traditional non-fuzzy methods emerges. This review aims to critically examine and compare the performance of newly developed fuzzy systems against established techniques such as Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Artifact Subspace Reconstruction (ASR). The goal is to determine which of these approaches is most effective in removing artifacts, thereby offering new insights into improving EEG data quality. Through this systematic comparison, we intend not only to identify the most performant method but also to contribute to setting more robust standards for future EEG signal processing applications, paving the way for more precise research and more reliable clinical interventions. 3. _Integrating fuzzy systems with advanced deep learning approaches to enhance EEG signal analysis_ This review explores how the fusion of fuzzy systems with cutting-edge deep learning technologies can enhance EEG signal analysis, particularly in the realms of clinical applications and research. We delve into the synergistic potential of combining fuzzy logic's robustness in handling uncertainty with the powerful feature extraction capabilities of deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The objective is to assess how these integrated approaches can significantly improve the accuracy and reliability of EEG analysis, thereby driving forward the frontiers of neuroscientific research and clinical diagnostics. 4. _Imnovative fuzzy techniques for artifact removal in EEG, with a special focus on intuitionistic fuzzy systems_ This review analyzes new fuzzy techniques, particularly those employing intuitionistic fuzzy systems, to tackle the challenges associated with artifact removal in EEG signals. We explore how these advanced fuzzy systems enhance the ability to distinguish between true neural activity and noise, thereby improving the reliability of EEG analyses in clinical and research settings. The focus on intuitionistic fuzzy systemshighlights their potential in handling the inherent uncertainties and ambiguities of EEG data, paving the way for more precise and effective artifact mitigation strategies. In the expansive field of scientific research on EEG signal analysis, the literature is replete with significant contributions, comprising hundreds of publications that explore various innovative methods for artifact removal. For this review, we have meticulously selected a limited number of studies to provide a focused overview showcasing the exceptional performances, especially of fuzzy approaches, in managing artifacts in EEG signals within the international 10-20 system context. This deliberate selection highlights how diverse methodologies, despite operating in vastly different contexts, converge in the efficacy of artifact removal, a crucial aspect for ensuring the accuracy of clinical diagnoses and neuroscientific research. Particularly, fuzzy approaches have proven to be extraordinarily versatile and effective, adapting to different configurations and analytical needs from standard to highly specialized ones. This flexibility suggests that the potential for future development of such methods is extensive and largely untapped.