## 3 Materials and Methods
### Parameter Optimization and Fine-Tuning Process  
The parameters in this study were adjusted and fine-tuned to optimize performance across different experimental scenarios. A detailed summary of these parameters is provided in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Related to** & **Value** \\ \hline notch\_filter\_fs & Notch Filter & 250.0 \\ notch\_filter\_powerline\_freq & Notch Filter & 50 \\ notch\_filter\_q & Notch Filter & 30.0 \\ high\_pass\_filter\_fs & High-Pass Filter & 250 \\ high\_pass\_cutoff & High-Pass Filter & 0.5 \\ car\_montage\_filter\_axis & CAR Montage Filter & 1 \\ car\_montage\_filter\_keep\_dims & CAR Montage Filter & True \\ higuchi\_k\_max\_value & Higuchi Fractal Dimension & 10 \\ box\_counting\_n\_scales & Box-Counting Fractal Dimension & 10 \\ box\_counting\_min\_box\_size & Box-Counting Fractal Dimension & 1 \\ box\_counting\_max\_box\_size & Box-Counting Fractal Dimension & 874 \\ correlation\_dimension\_emb\_dim & Correlation Dimension Fractal Dimension & 5 \\ correlation\_delay & Correlation Dimension Fractal Dimension & 5 \\ correlation\_dimension\_r\_values & Correlation Dimension Fractal Dimension & np\_logspace\(-3,0,30\)) \\ correlation\_dimension\_epsilon & Correlation Dimension Fractal Dimension & \(1\times 10^{-12}\) \\ mdfda\_q\_values & MEDFA Fractal Dimension & \([-5,-4,\ldots,4,5]\) \\ mdfda\_min\_scale & MEDFA Fractal Dimension & 4 \\ mdfda\_max\_scale\_ratio & MEDFA Fractal Dimension & 437 \\ mdfda\_min\_scale\_ratio & MEDFA Fractal Dimension & 2.0 \\ dfda\_min\_scale & DFA Fractal Dimension & 4 \\ dfda\_max\_scale & DFA Fractal Dimension & 437 \\ dfa\_scale\_ratio & DFA Fractal Dimension & 2.0 \\ filter\_bank\_type & Filters Bank Coefficients & butter \\ filter\_bank\_order & Filters Bank Coefficients & 2 \\ filter\_bank\_max\_freq & Filters Bank Coefficients & 40 \\ time\_window\_ft & Filters Bank Coefficients & \([2.5,3.5]\)\(\ldots\)\([2.5,6]\) \\ bw & Bandwidth of Filtered Signals & \([2,4,8,16,32]\) \\ no\_csp & No. of CSP Features & 24 \\ cart\_max\_depth & CART & 10 \\ cart\_random\_state & CART & 1 \\ cart\_criterion & CART & Gini \\ cart\_splitter & CART & best \\ cart\_min\_samples\_split & CART & 2 \\ cart\_min\_samples\_leaf & CART & 1 \\ linear\_svc\_c & LinearSVM & 0.1 \\ linear\_svc\_intercept\_scaling & LinearSVM & 1 \\ linear\_svc\_loss & LinearSVM & hinge \\ linear\_svc\_max\_iter & LinearSVM & 1000 \\ linear\_multi\_class & LinearSVM & ovr \\ linear\_svc\_penalty & LinearSVM & 12 \\ linear\_svc\_random\_state & LinearSVM & 0.00001 \\ \hline svc\_w\_poly\_kernel\_c & SVM Polynomial Kernel & 0.1 \\ svc\_w\_poly\_kernel\_type & SVM Polynomial Kernel & poly \\ svc\_w\_poly\_kernel\_degree & SVM Polynomial Kernel & 10 \\ svc\_w\_poly\_kernel\_gamma & SVM Polynomial Kernel & auto \\ svc\_w\_poly\_kernel\_coef & SVM Polynomial Kernel & 0.0 \\ svc\_w\_poly\_kernel\_to\_SVM & SVM Polynomial Kernel & 0.001 \\ svc\_w\_poly\_kernel\_c\_size & SVM Polynomial Kernel & 10,000 \\ svc\_w\_poly\_kernel\_max\_iter & SVM Polynomial Kernel & \(-1\) \\ svc\_w\_poly\_kernel\_decision\_fx & SVM Polynomial Kernel & ovr \\ ggvm\_c & GSVM & 20 \\ ggvm\_kernel\_type & GSVM & rbf \\ ggvm\_degree & GSVM & 10 \\ gsvm\_gamma & GSVM & auto \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The list of optimized parameters. Multiple iterations were conducted to determine the optimal parameters for this study. The process began by refining the preprocessing filters, selecting the most effective fractal dimension factors, and enhancing the performance of the classifiers used in the experiments. In the initial processing phase, we applied a series of filters. First, a notch filter was configured with a center frequency \(f_{0}\) of 50 Hz to remove line noise and a quality factor \(Q\) of 30 to determine its bandwidth. Next, a high-pass filter was established at 0.5 Hz, ensuring that frequencies below this threshold were neglected.