## 5 Results
### Overall MI + ER Features Comparison  
By comparing the global classifiers and feature sets' performances (see Table 8 and Figure 3), we can deduce that LinearSVM performs the best overall, reaching an average rate of 76.78%, achieving high accuracy across all feature sets. GSVM performs moderately well, offering an average success rate of 62.85%, which is better when employed on wavelet and fractal dimension features, but still struggles with statistical, Hjorth, and all combined features. SVM with a polynomial kernel has an average success rate of 60.18%. The best performance is achieved with fractal dimensions and wavelet analysis, but they show weaker results on other feature sets. CART consistently underperforms compared to the other SVM-based classifiers, with an average success rate of 59.07%, resulting in modest variations across all six feature sets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **LinearSVM** & **CART** & **GSVM** & **SVM (Kernel = “Poly”)** \\ \hline Subject No. 1 & 82.05 & 66.35 & 53.47 & 60.08 \\ Subject No. 2 & 64.81 & 45.18 & 27.40 & 40.00 \\ Subject No. 3 & 84.07 & 74.07 & 29.25 & 46.29 \\ Subject No. 4 & 62.92 & 40.07 & 41.53 & 40.39 \\ Subject No. 5 & 73.68 & 59.93 & 43.15 & 41.64 \\ Subject No. 6 & 42.86 & 27.36 & 27.38 & 33.76 \\ Subject No. 7 & 85.23 & 84.54 & 49.46 & 50.92 \\ Subject No. 8 & 83.28 & 62.14 & 43.57 & 55.31 \\ Subject No. 9 & 77.57 & 72.57 & 39.69 & 49.32 \\ \hline Avg. Success Rate & 72.94 & 59.13 & 39.44 & 46.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of the combination of all feature sets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **LinearSVM** & **CART** & **GSVM** & **SVM (Kernel = “Poly”)** \\ \hline Statistical & 77.10 & **59.30** & 55.68 & 52.25 \\ Wavelet Analysis & 77.25 & 58.95 & 75.25 & 71.44 \\ Higher-Order Spectra & 77.20 & 58.97 & 72.71 & 63.41 \\ Hjorth & 77.19 & 59.00 & 55.90 & 52.26 \\ Fractal Dimensions & **79.04** & 59.04 & **78.11** & **75.31** \\ Combined All & 72.94 & 59.13 & 39.44 & 46.41 \\ \hline Avg. Success Rate & 76.78 & 59.07 & 62.85 & 60.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: A comparison of the results obtained considering the embraced hybrid pipeline. LinearSVM shows the most optimal results for statistical features (77.10%). Wavelet analysis's best classifier is LinearSVM (77.25%). Higher-order spectra have promising results when using LinearSVM (77.20%). Hjorth's best accuracy comes from LinearSVM (77.19%). Fractal dimensions, remarkably, provide the highest success rate compared to all other feature sets, reaching 79.04%. LinearSVM performs similarly well as using GSVM and SVM with a polynomial kernel. All combined exhibited a performance decline across all four classifiers, with LinearSVM being the most reliable. This indicates that noise is generated when combining all feature sets, resulting in a drop in the success rate. Student's t-tests were conducted to compare the fractal dimensions feature set with other feature sets (statistical, wavelet analysis, higher-order spectra, Hjorth, and all combined). No statistically significant differences were found between fractal dimensions and any other feature set (all \(p\)-values \(>\) 0.05), indicating that fractal dimensions did not show a significant performance advantage over the other feature sets (see Table 9). The lack of significant differences between fractal dimensions and other feature sets suggests that while fractal dimensions may offer unique features, they do not outperform other sets statistically in terms of classification accuracy. This finding highlights that while fractal dimensions capture complex data characteristics, other feature sets provide similarly effective information. Figure 3: Clustered column chart visualizing the results generated from the hybrid ER/MI pipeline.