## 12 Evolving Fuzzy Techniques for EEG Artifact Removal  
Fuzzy logic turns out to be a particularly suitable tool for removing artifacts in EEG signals, being capable of effectively dealing with the uncertainty and variability intrinsic to  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Domain** & **Method** & **Conclusions** \\ \hline \multirow{6}{*}{[140]} & Ocular, muscle, power line noise & CCA & The system proves substantial effectiveness in eliminating ocular artifacts across a broad range of SNR, from low to high. However, its ability to remove muscle artifacts is constrained due to their frequency overlap with EEG signals, indicating a limitation in its performance for specific artifact types. Intriguingly, the system’s capability to remove power line noise exhibits quantitative improvement as the ambient noise levels increase. This trend highlights the system’s robust performance and adaptability in more challenging noise environments, where it not only maintains but enhances its efficacy in noise mitigation, thus providing a clearer, more reliable EEG signal analysis under adverse conditions. & The system demonstrates a robust capability to significantly eliminate both ocular and muscular artifacts from EEG recordings. This performance is indicative of its advanced artifact removal technology, which effectively distinguishes and filters out these specific types of disturbances. By reducing the presence of ocular and muscular noise, the system enhances the overall clarity and reliability of EEG data analysis. This improvement is crucial for accurate neurological diagnostics and research, making the system particularly valuable in clinical and scientific settings where precision is paramount. & The approach not only surpasses other automatic methods based on Independent Component (IC) analysis in terms of processing speed but also demonstrates a high level of efficiency, making it suitable for real-time applications. Its swift performance ensures that it can be effectively integrated into EEG-based brain-computer interface (BCI) systems, providing reliable, rapid responses essential for online BCI operations. This capability enhances the usability and applicability of the system in dynamic, real-world environments where speed and accuracy are critical. & The Convolutional Neural Network (CNN) consistently delivers superior results in comparison to the Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms, demonstrating enhanced performance across various metrics. This superiority is quantitatively evident in its higher accuracy and faster processing speeds. CNNs excel in handling complex datasets by efficiently extracting and learning from features, which allows them to achieve more precise outcomes. The robust architecture of CNNs enables them to adapt better to complex data patterns, making them exceptionally useful in applications where precision and speed are critical, such as real-time signal processing and advanced analytics tasks. This performance edge highlights CNNs as a preferable choice in technological demanding environments. \\ \hline \hline \end{tabular}
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Table 10: Application of hybrid approaches for the removing of internal/external artifacts in EEG.  
these signals. Through the use of fuzzy sets and membership functions, it enables accurate representation of neural artifacts and features, facilitating a more intuitive and contextualized filtering process. The integration of fuzzy inference rules with EEG field expertise contributes to the creation of highly adaptive and customizable artifact removal systems, thus significantly improving the quality of EEG signals and, consequently, the accuracy of diagnoses and the effectiveness of clinical research.