## 12 Evolving Fuzzy Techniques for EEG Artifact Removal
### Fuzzy Approaches for the Removal of Artifacts: Case Studies
\begin{table}
\begin{tabular}{p{34.1 pt} p{34.1 pt}} \hline \hline \multicolumn{2}{c}{**TS Fuzzy Systems**} \\ \hline
**Strengths** & Accurate modeling of nonlinearities of EEG signals, thanks to the use of linear functions as conclusions in the fuzzy rules, which can be easily calibrated to adapt to specific types of artifacts. Computationally lightweight because they use linear or constant functions for the defuzzifier. Easily integrated with machine learning algorithms to dynamically adapt model parameters in response to changes in EEG signals. Ease of integration with other signal processing technologies such as wavelet transform to further improve accuracy in artifact removal. \\ \hline
**Weaknesses** & Design and optimization can be complex, requiring knowledge of both fuzzy logic and the specifics of the EEG signal. The effectiveness of the system strongly depends on the quality and variety of the training data used to configure the fuzzy rules and membership functions. Overfitting if the system is too specifically calibrated on the training data, losing generality and ability to adapt to new data or unexpected situations. \\ \hline
**Opportunities** & Development of new machine learning and deep learning algorithms could improve the effectiveness in removing artifacts. Collaborations with neuroscientists and biomedical engineers can lead to a more in-depth understanding of EEG signals and the customization of fuzzy systems for specific applications. Effectiveness in removing artifacts may lead to wider adoption of these technologies in clinical and research settings, expanding the potential market. \\ \hline
**Threats** & The emergence of new signal processing technologies that could offer more efficient or less expensive solutions for artifact removal. Strict regulations in the healthcare industry can limit the use or implementation of new technologies without extensive testing and validation periods. System performance can be highly sensitive to initial parameters, making it difficult to standardize the system for broad use without adequate expertise. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: SWOT Analysis for TS fuzzy systems. computer interfacing, respectively, demonstrate the applicability of fuzzy techniques in real-time EEG signal processing. These approaches emphasize the ability of fuzzy techniques to provide accurate analysis and fine modulation of the artifact removal process, maintaining the integrity of the original signal for more precise diagnoses and advanced applications such as device control via BCI. In addition to the reasons already outlined in the introduction of the review, the choice of ANFIS over more sophisticated fuzzy techniques is based on their excessive computational complexity. Although advanced TS techniques can offer a slight improvement in performance, the increase in computational complexity they require is not proportionate to the benefits obtained. This makes ANFIS a preferable choice, as it provides an optimal balance between computational efficiency and performance improvement, effectively adapting to application contexts where computing resources may be limited. Table 12 summarizes the above information.