## 4 Results
### Comparison Between Classifiers  
The comparison table of the related fractal dimension combinations compared classifier-wise is presented in Table 4. The results in the latter show that LinearSVM and GSVM achieve the highest classification accuracy overall. LinearSVM performs best, with an average accuracy of 78.88%, slightly outperforming GSVM (78.23%). This is consistent with findings in EEG-based machine learning, where support vector machines (SVMs), especially those with linear or Gaussian kernels, are known for their robustness and ability to handle complex neural signals. In contrast, CART (decision trees) shows the lowest performance, with an average accuracy of 58.83%, likely due to its tendency to overfit when handling high-dimensional or nonlinear data, a common challenge in EEG classification.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fractal Dimensions** & **Classifier** & **Sub. 1** & **Sub. 2** & **Sub. 3** & **Sub. 4** & **Sub. 5** & **Sub. 6** & **Sub. 7** & **Sub. 8** & **Sub. 9** & **Mean** \\ \hline \multirow{8}{*}{PFD vs. BCFD vs. DFA} & LinearSVM & 84.95 & 70 & 90 & 62.58 & 81.69 & 47.88 & 92.59 & 90.13 & 87.31 & 78.57 \\  & GSVM & 84.61 & 69.25 & 90.74 & 63.33 & 79.78 & 48.35 & 91.11 & 89.37 & 86.87 & 78.16 \\  & CART & 64.90 & 45.55 & 74.44 & 39.29 & 53.05 & 31.03 & 84.16 & 62.90 & 77.20 & 59.17 \\  & SVMPoly & 83.14 & 62.59 & 84.81 & 56.40 & 79.76 & 51.07 & 86.70 & 89.37 & 84.76 & 75.40 \\  & SGD & 82.09 & 63.70 & 84.07 & 53.73 & 59.55 & 36.51 & 78.20 & 87.08 & 78.44 & 69.26 \\ \hline \multirow{8}{*}{PFD vs. MFDFA vs. DFA} & LinearSVM & 84.95 & 70 & 90 & 62.58 & 82.07 & 47.88 & 92.59 & 90.13 & 87.31 & 78.61 \\  & GSVM & 84.61 & 69.25 & 90.74 & 63.33 & 79.78 & 48.35 & 91.11 & 89.37 & 86.87 & 78.16 \\  & CART & 64.90 & 45.55 & 74.44 & 39.29 & 52.67 & 31.03 & 84.16 & 62.52 & 77.20 & 59.08 \\  & SVMPoly & 83.14 & 62.59 & 84.81 & 56.40 & 79.76 & 51.07 & 86.33 & 89.37 & 84.76 & 75.36 \\  & SGD & 81.73 & 63.70 & 84.07 & 49.51 & 56.90 & 34.21 & 80.80 & 73.12 & 80.55 & 67.18 \\ \hline \multirow{8}{*}{FFD vs. CDFD vs. MFDFA} & LinearSVM & 84.95 & 69.62 & 90 & 62.96 & 82.46 & 47.88 & 92.59 & 90.13 & 87.31 & 78.66 \\  & GSVM & 84.61 & 70 & 90.74 & 64.84 & 80.18 & 48.35 & 90.74 & 89.37 & 86.87 & 78.41 \\  & GART & 65.62 & 42.22 & 74.07 & 35.10 & 54.97 & 31.03 & 84.16 & 59.48 & 78.45 & 58.34 \\  & SVMPoly & 83.51 & 62.59 & 84.81 & 56.79 & 79.76 & 51.07 & 85.96 & 89.37 & 84.76 & 75.36 \\  & \multirow{2}{*}{FFD vs. CDFD vs. MFDFA} & LinearSVM & 84.95 & 69.25 & 90.37 & 64.88 & 82.07 & 47.88 & 92.96 & 90.88 & 87.31 & 78.95 \\  & GSVM & 84.24 & 69.25 & 90.37 & 63.32 & 79.41 & 47.89 & 91.48 & 89.75 & 86.87 & 78.07 \\  & GART & 64.90 & 45.55 & 74.44 & 38.91 & 53.05 & 31.03 & 8 Among the different fractal dimension (FD) feature sets, those including Katz fractal dimension (KFD) consistently rank among the best-performing combinations. For example, the \"KFD vs. BCFD vs. CDFD\" feature set achieves the highest accuracy (79.16%) with LinearSVM. Other classifiers, including GSVM, SVM with a polynomial kernel, and SGD, also tend to perform better when KFD features are included. This suggests that KFD effectively captures EEG complexities and provides useful information for motor imagery classification. While the accuracy differences among FD combinations are not large, the slight improvements seen with KFD-based sets indicate that KFD extracts unique signal features that other FD methods may miss.  
Regarding classifier performance, SGD ranks in the mid-range (67.95%), performing better than CART but remaining behind SVM models. SVM with a polynomial kernel maintains a stable accuracy (75.37%), indicating that nonlinear kernels can effectively model EEG data without requiring excessive computational resources. Overall, these results highlight the importance of both classifier selection and feature choice. Using a well-optimized SVM and integrating KFD-based fractal dimension features leads to a stronger performance in EEG-based motor imagery classification.