## 4 EEG Clarity: Unraveling Artifacts  
In the analysis of EEG signals, artifacts not only introduce noise but also introduce elements of ambiguity or fuzziness, significantly complicating the interpretation of the data. This fuzziness derives both from physiological sources internal to the patient (eye movements, muscle activity, and heart rhythms that overlap with the signal frequencies) and from external environmental influences (electromagnetic interference or electrode movements that alter the spectral profile of the signal) [85; 86; 87]. Table 1 illustrates the different types of artifacts. Figure 1 displays a spike artifact caused by head shaking and/or a possible loose electrode; only P3 is involved because the patient is resting their head on a pillow. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Internal** & & & & **External** & & \\ \hline ocular & cardiac & muscle & others & instrumental & interference & movement \\ \hline Eye blink & EEG pulse & Chewing & Gloss kinetic & Electrode & Electrical & Head movement \\ Eye movement & & Swallowing & Skin & Displacement and pop-up & Magnetic & Body movement \\ Eye Flatter & & Clenching & Respiration & & & Limbs movement \\ REM sleep & & Sniffing & & & Sound & Tremor \\  & & Talking & & & & Optical & \\  & & Scalp & & & & Other movements \\  & & Contraction & & Poor ground & EM waves & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Exploring the spectrum: different artifact types and their origins in EEG analysis. Then, it appears useful to adopt advanced filtering techniques and signal processing algorithms to isolate and remove artifacts, preserving data integrity (i.e., balance between minimizing ambiguity and maximizing useful information). These approaches exploit both temporal, spatial, and spatio-temporal procedures, allowing the identification and selective removal of artifactual components from the EEG signal, preserving the relevant neurophysiological information. Of note, the integration of AI and machine learning techniques is emerging as a promising area to further improve artifact removal by offering adaptive predictive models for artifact removal. Although each method of removing artifacts from EEGs has its peculiarities, none are universally effective on their own. Therefore, to significantly improve the quality of the EEG signal, it is essential to integrate different techniques into a single framework, exploiting the strengths of each to obtain better results and more accurate data interpretation [75, 88]. We will begin by exploring a series of EEG artifact removal techniques, recognized today as the gold standard in the field. These methodologies will be subjected to a SWOT analysis to reveal their strengths and weaknesses, thus outlining the context in which they fit within current research and clinical practice. This critical examination will serve as a foundation for introducing and discussing more advanced hybrid approaches. In particular, we will focus on the evolution towards systems that incorporate fuzzy elements, even in an intuitionistic form, to deal more precisely with the intrinsic uncertainty in EEG signals. Through this path, we will delve into the dynamics and potential of hybrid approaches that blend different technologies and methodologies to overcome the limits of standard techniques. In this context, the fuzzy element emerges as a vital component, offering a sophisticated means to model the ambiguity and uncertainty often present in neurological data. The introduction of systems based on intuitionistic fuzzy logic represents a qualitative leap in the ability to analyze EEG signals, proposing a more accurate and flexible approach in the management of artifacts. This transition towards more complex and integrated methodologies not only aims to improve the efficiency in removing artifacts but also aims to refine our understanding of brain signals, expanding the possibilities for diagnosis and monitoring of neurological conditions.