## 4 Results
### Comparison with Previous Works  
This section compares our proposed method with several previously published approaches in motor imagery (MI) classification. The comparison focuses on mean classifier accuracy, a key measure of how well different algorithms classify EEG signals related to motor imagery tasks. Using these results, we ensure an objective evaluation of how our method performs relative to existing innovative techniques.  
We include results from various established methods such as SincNet [66], HSS-ELM [67], IFNet [68], TSLDA [69], TSFBCSP-GA [70], FBRTS [71], TWSB [72], multi-scale CSP/Riemannian approaches [73], and the hybrid ER/MI pipeline (KFD vs. PFD vs. HFD) [36]. Additionally, we compare these methods with the novel fractal dimension (FD) combination developed in this study (KFD vs. BCFD vs. CDFD). This allows us to examine both classical and more recent approaches, providing a deep perspective on improvements in MI classification and highlighting the strengths of different methodologies.  
To ensure consistency, we round the performance metrics in the current works results table to the nearest tenth of a percent. Furthermore, Figures 4 and 5 present a visualization of the classification results, showing how MI research has evolved over time. This study's results contribute to the field with a new state-of-the art result reaching 79.2% mean accuracy. A comparative analysis of the proposed fractal dimension combinations and previously published methods is presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fractal Dimensions** & **LinearSVM** & **GSVM** & **CART** & **SVM “Poly”** & **SGD** \\ \hline PFD vs. BCFD vs. DFA & 78.57 & 78.16 & 59.17 & 75.4 & 69.26 \\ PFD vs. MFDFA vs. DFA & 78.61 & 78.16 & 59.08 & 75.36 & 67.18 \\ PFD vs. CDFD vs. MFDFA & 78.66 & 78.41 & 58.34 & 75.4 & 68.38 \\ HFD vs. CDFD vs. DFA & 78.7 & 78.41 & 58.22 & 75.36 & 69.27 \\ KFD vs. PFD vs. HFD & 78.95 & 78.07 & 59.08 & 75.31 & 67.97 \\ KFD vs. PFD vs. BCFD & 79.04 & 78.11 & 59.04 & 75.31 & 68.14 \\ KFD vs. CDFD vs. MEDFA & 79.08 & 78.24 & 58.26 & 75.39 & 67.87 \\ KFD vs. PSD vs. CDFD & 79.12 & 78.24 & 59.08 & 75.39 & 67.02 \\ KFD vs. BCFD vs. CDFD & 79.16 & 78.24 & 59.23 & 75.39 & 66.46 \\ \hline Mean & **78.88** & 78.23 & 58.83 & 75.37 & 67.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mean of all classifiers’ performance, depending on each FD combination.  
Figure 4: Comparison of established motor imagery methods with our proposed approach.  
Figure 5: Classification accuracy of various techniques across different participants, together with the related mean performance.  
Our results show a clear discrepancy in classification accuracy for certain subjects, particularly A02 and A06, where the performance in several cases approaches or falls below chance-level performance. This phenomenon has been consistently observed in prior BCI studies and is commonly attributed to BCI illiteracy, where specific users fail to generate distinct and repeatable neural patterns during motor imagery tasks. This may arise due to a range of physiological, cognitive, or psychological factors, such as low signal-to-noise ratio, poor engagement, or inconsistencies in mental strategy.