## 4 Materials and Methods
### Tweaked Parameters  
The parameters used in this research were tweaked to extract the most out of each use case experiment. The parameter overview is presented in Table 1. \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 Butterworth Filter & 250 \\ high\_pass\_cutoff & High-Pass Butterworth Filter & 0.5 \\ car\_montage\_filter\_axis & CAR Montage Filter & 1 \\ car\_montage\_filter\_keep\_dims & CAR Montage Filter & True \\ wavelet\_value & Wavelet Analysis & db4 \\ wavelet\_level & Wavelet Analysis & 5 \\  hos\_fs & Higher-Order Spectra & 250.0 \\  hos\_nperse & Higher-Order Spectra & 256 \\  hos\_noverlap & Higher-Order Spectra & 128 \\ fd\_higuchi\_k\_max\_value & Fractal Dimension: Higuchi & 10 \\ filter\_bank\_type & Filters Bank with Butterworth Filter & butter \\ filter\_bank\_order & Filters Bank with Butterworth Filter & 2 \\ filter\_bank\_max\_freq & Filters Bank with Butterworth Filter & 40 \\ time\_windows\_flt & Filters Bank with Butterworth Filter & [2.5, 3.5]..[2.5, 6] \\ bw & Bandwidth of Filtered Signals & [2, 4, 8, 16, 32] \\ no\_csp & Number of CSP Features & 24 \\ cart\_max\_depth & CART Classifier & 10 \\ cart\_random\_state & CART Classifier & 1 \\ cart\_criterion & CART Classifier & gini \\ cart\_splitter & CART Classifier & best \\ cart\_min\_samples\_split & CART Classifier & 2 \\ cart\_min\_samples\_leaf & CART Classifier & 1 \\ linear\_svc\_c & LinearSVM Classifier & 0.1 \\ linear\_svc\_intercept\_scaling & LinearSVM Classifier & 1 \\ linear\_svc\_loss & LinearSVM Classifier & hinge \\ linear\_svc\_max\_iter & LinearSVM Classifier & 1000 \\ linear\_multi\_class & LinearSVM Classifier & ovr \\ linear\_svc\_penalty & LinearSVM Classifier & l2 \\ linear\_svc\_random\_state & LinearSVM Classifier & 1 \\ linear\_svc\_tol & LinearSVM Classifier & 0.00001 \\  svc\_w\_poly\_kernel\_c & SVM with Polynomial Kernel Classifier & 0.1 \\  svc\_w\_poly\_kernel\_type & SVM with Polynomial Kernel Classifier & poly \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The list of tweaked parameters. The fine-tuning process required many attempts to find the most suitable parameters for this study. The approach started with optimizing the preprocessing filters, feature sets, and the performance of the classifiers used. The first step was preprocessing filters from the ER pipeline. The Notch filtering used a powerline frequency, \(f\)0, of 50 Hz, which describes the center frequency to be removed, and a quality factor value, \(Q\), of 30, which controls the bandwidth of the notch. Second, the high-pass filter used a cutoff frequency, _wc_, of 0.5 Hz, the lowest frequency retained. Third, the CAR montage used a filtered axis value of 1 and kept the dimension parameter set to true. This ensures that the CAR montage is correctly applied across channels and preserves the data structure. This was followed by the preprocessing filters from the MI pipeline. The total number of CSP filters across all classes and time windows is 24. The filter bank type is _butter_, the adopted order is 2, and the maximum frequency is 40 Hz. The bandwidth of filtered signals, _bw_, are grouped in the following list [2; 4; 8; 16; 32] in Hz. The latter provides band-specific filtering for the EEG signal, capturing distinct frequency bands like alpha and beta. Lastly, the time windows (start and end time) are set and then scaled by the current sampling frequency, 250 Hz, which allows the CSP filters to operate over multiple temporal windows, capturing time-specific features across different parts of the EEG signal. Next, the optimization of each feature set with a parameter to be fine-tuned is disclosed, which includes wavelet analysis, higher-order spectra, and the fractal dimension's Higuchi.