## 12 Evolving Fuzzy Techniques for EEG Artifact Removal
### Fuzzy Intuitionistic Approaches for the Removal of Internal/External Artifacts: Case Studies
\begin{table}
\begin{tabular}{p{42.7 pt} p{284.5 pt}} \hline \hline \multicolumn{2}{c}{**Intuitionistic TS Fuzzy Systems**} \\ \hline \multirow{4}{*}{**Strengths**} & Better management of uncertainty and variability of EEG signals thanks to their ability to incorporate both degrees of membership and non-membership, significantly improving the discrimination between normal and artifact signals compared to classical fuzzy systems. Dynamically adapt to changes in the EEG signal, continuously refining their operating parameters based on real-time feedback, which classical fuzzy systems do to a lesser extent. Improved robustness against unexpected variations in EEG signals, ensuring reliability even in variable clinical and research environments where classical systems can fail. \\ \hline \multirow{4}{*}{**Weaknesses**} & More complex to implement and optimize than their classic counterparts, requiring greater technical expertise. Simultaneously, managing membership and non-membership degrees requires more computational and analytical resources, increasing the workload compared to classical systems. The quality of the training data is even more critical to ensure optimal performance. \\ \hline \multirow{4}{*}{**Opportunities**} & Superior efficacy paves the way for new applications and improvements in existing devices. They favor standardization as a best practice for EEG artifact removal, potentially rewriting industry guidelines. By improving performance in artifact removal, these systems can expand their applicability in clinical and remote monitoring environments, increasing their potential market. \\ \hline \multirow{4}{*}{**Threats**} & The continuous evolution of signal processing technologies could present new competitive challenges. Complexity and increased costs may slow the adoption of these systems by clinics and laboratories with limited resources. The reliance on advanced expertise for configuration and maintenance may limit the use of these systems to institutions or individuals with access to such resources. \\ \hline \hline \end{tabular}
\end{table} 
Table 13: SWOT Analysis intuitionistic TS fuzzy systems. of the approach to handle data uncertainty and ambiguity has led to greater classification accuracy, demonstrating that this new approach can overcome some limitations of existing methods, particularly in applications where data has a high degree of overlap and complexity. The paper [151] proposes an innovative approach for the processing of EEG signals, based on the use of an intuitionistic fuzzy strategy in the context of double-center large-margin distribution machines. This methodology stands out for the integration of an intuitionistic fuzzy function that improves computational efficiency through a precise determination of the degree of non-membership of a sample, calculated based on the distance from the centers of two different categories. The peculiarity of the model lies in its ability to optimize the margin distribution, rather than focusing solely on the minimum margin. Using mean and variance of the margin as key parameters, the model aims for more effective generalization, overcoming the limitations of previous methods that do not optimally handle uncertainty and noise. Furthermore, the model incorporates a regularization term to minimize structural risk, further improving robustness against noise and potentially facilitating the removal of artifacts from EEG signals. Through experiments on synthetic datasets and benchmarks, the approach has been shown to significantly outperform existing methods in terms of noise immunity and classification ability. The sensitivity analysis of the parameters confirmed the high accuracy and stability of the model, highlighting its superiority in managing EEG signals, even in the presence of artifacts. The paper [152] explores a new line of research that revolutionizes the analysis of EEG signals and the removal of artifacts through an integrated approach that uses complex Gaussian fuzzy numbers and multi-source information fusion techniques.