## 6 Discussion  
In this section, we discuss the associated results and conduct a thorough analysis of the outcomes obtained from the study, offering insights into their significance and exploring any possible future study areas. The results obtained in this study benefit the field of motor imagery. The outcomes show the efficacy of the LinearSVM classification method, which consistently delivered the best overall performance and had the highest success rate among most feature sets in terms of fractal dimensions and statistical features. This high performance can be linked to the classifier's ability to handle highly dimensional data while minimizing the risk of overfitting. GSVM is in second place and sometimes performs better on specific feature sets, like fractal dimensions and wavelet analysis, and sometimes struggles with others, like statistical and all combined. The third place is attributed to SVM with a polynomial kernel. It also performs better with fractal dimensions and wavelet analysis and lags behind other feature sets. GSVM and SVM with a polynomial kernel show moderate performance. As they are non-linear SVMs, they offer an easy way of capturing complex relationships in EEG signals. However, they may be more disposed to overfitting with highly dimensional features, and the latter can affect the consistency of the results when using different feature extraction techniques. Lastly, CART remains the least effective classifier, with a low average success rate across all five feature sets except statistical features. This is likely because CART is sensitive to noise and tends to overfit with complex, highly dimensional data. While CART is usually used for non-linear decision making, this sensitivity can lead to a lower accuracy when applied to multi-class MI tasks, where the data can vary. Considering the feature extraction methods used, three-dimensional fractal dimensions (Katz, Petrosian, and Higuchi) outperform all other feature sets, except in the case of use of the CART classifier, where the statistical method outperforms fractal dimensions. Moreover, all combined features led to lower classification accuracy, suggesting that by combining different feature sets, extra noise is generated, which makes it harder for the classifiers to identify the differences in the EEG signals. This can highlight a potential limitation of combining features. The sequential preprocessing approach adopted in this study is effective for noise reduction and feature extraction in motor imagery EEG signals, but there are alternative techniques, such as regression-based filtering [46] and independent component analysis (ICA) [35], which can enhance filtering artifact corrections, specifically in the case of motion noise or muscle movements. Despite the latter, these methods may create more complexity and computational demands, which is beyond the scope of this current research. During our previous study [47], we only utilized the binary classification of left and right classes using the BCI Competition IV 2a dataset and all five feature sets. Meanwhile, a combination of only three feature sets (fractal dimensions, wavelet analysis, and Hjorth) was implemented. However, in this latest study, all four motor imagery classes were used, left and right hands, both feet, and tongue, and all combined (statistical, wavelet analysis, higher-order spectra, Hjorth, and fractal dimension) features were employed. Unlike GSVM and CART, LinearSVM and SVM with a polynomial kernel were added. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison** & **t-Statistic** & \(p\)**-Value** \\ \hline
3D-FD vs. TSLDA & 2.0942 & 0.0696 \\
3D-FD vs. Multi-Scale CSP & 2.1535 & 0.0634 \\
3D-FD vs. Multi-Scale Riemannian & 1.2626 & 0.2423 \\
3D-FD vs. HSS-ELM & 2.8574 & 0.0212 \\
3D-FD vs. SincNet & 6.8718 & 0.0001 \\
3D-FD vs. FBRTS & 1.2607 & 0.2429 \\
3D-FD vs. TSFBCSP-GA & 1.6665 & 0.1342 \\
3D-FD vs. IFNet & 0.2899 & 0.7793 \\
3D-FD vs. TWSB & \(-\)0.0282 & 0.9782 \\ \hline \hline \end{tabular}
\end{table}