## 10 Machine Learning Basics: Unlocking AI's Core Principles  
Machine learning approaches for artifact removal in EEG signals leverage the capability of learning algorithms to learn from specific examples to identify and filter out undesired elements from the data [111; 112; 113]. These methods employ datasets that include both clean EEG signals and signals contaminated by artifacts, allowing models to distinguish useful signal characteristics from artifact characteristics through a supervised learning process. Accurate dataset preparation is crucial here, as each example must be clearly labeled to ensure that the model develops effective predictive capabilities. Integrating fuzzy systems with deep learning techniques can lead to significant improvements in this area. Fuzzy systems, with their ability to handle uncertainty and produce decisions based on rules that can simulate human reasoning, can optimize the classification of signal features in the presence of ambiguous labels or highly variable signals that might confuse conventional deep learning models [114; 115; 116; 117].  
A hybrid model that combines the automatic feature extraction capabilities of deep learning with the robustness of fuzzy systems in managing ambiguity can improve the accuracy of artifact removal and offer greater flexibility in dealing with different types of artifacts. Such integration can significantly enhance the quality of EEG signals for subsequent uses, such as clinical monitoring and neuroscientific research [118; 119; 120; 121; 122; 123].  
Validating such hybrid approaches through comparative studies that examine performance against methods that use solely machine learning or fuzzy systems can include sensitivity analyses and accuracy evaluations. The goal is to develop robust and reliable methodologies that can be effectively implemented in various biomedical signal analysis contexts, providing more sophisticated and accurate tools for industry professionals [124; 125; 126; 127; 128].  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline  & **LMS** & **RLS** \\ \hline
**Strengths** & Algorithmic simplicity. High adaptability. & High performance in real-time mode (fast updates). High precision. \\ \hline
**Weaknesses** & High sensitivity to parameters. With complex artifacts, it does not ensure effective artifact removal. Difficulty in processing data affected by uncertainties and/or inaccuracies. & Greater computational complexity (compared to other adaptive filters). Performance highly dependent on the choice of parameters. Difficulty in processing data affected by uncertainties and/or inaccuracies. \\ \hline
**Opportunities** & Integration with other techniques. Advanced applications. & Integration with machine learning algorithms. Development of low-consumption variants. \\ \hline
**Threats** & Poor efficiency for real-time applications. & Hardware resource limitations. \\ \hline \hline \end{tabular}
\end{table}
Table 6: SWOT analysis for adaptive filters.  
A common approach involves the use of neural networks, particularly Convolutional Neural Networks (CNNs), for their ability to automatically extract meaningful features from spatio-temporal data [129; 130; 131], and Recurrent Neural Networks (RNNs), which are effective in learning temporal dependencies in sequential data [132; 133; 134].  
We denote a dataset of EEG signals here, by \(\{(\mathbf{x}^{(i)},y^{(i)}\}_{i=1}^{N}\), where \(\mathbf{x}^{(i)}\in\mathbb{R}^{m\times n}\) is the signal matrix of the i-th EEG sample with \(m\) channels and \(n\) time points, and \(y^{(i)}\in\{0.1\}\) is the label indicating the presence (1) or absence (0) of artifacts.