## 1 Introduction
In the classification stage, machine learning models such as Support Vector Machine (SVM) and decision trees help to interpret EEG patterns and translate them into commands [19; 20]. Finally, performance evaluation ensures the reliability of the resulting system, using accuracy metrics or the Kappa Coefficient to measure the agreement between predicted and true labels [21; 22]. Several challenges affect the performance of MI-BCIs. Enhancing the efficiency of an MI-BCI requires improving the classification accuracy, robustness, and user experience through preprocessing, feature selection, and dimensionality reduction. Reducing the calibration time is essential, as traditional BCIs require extensive training; transfer learning and subject-independent approaches help to minimize this phase [23; 24; 25; 26; 27; 28; 29]. Another major issue is BCI illiteracy, where some users fail to achieve reliable control due to physiological and cognitive factors, necessitating better feedback mechanisms and training protocols [30]. Further advancements focus on asynchronous MI-BCI, which allows for continuous operations but increases the risk of false positives [31]. Efforts to expand the number of commands move beyond binary control to provide more nuanced interactions [32]. Additionally, adaptive BCIs aim to adjust to variations in brain signals caused by fatigue, learning, or emotional states [33]. The development of online MI-BCIs enables real-time operations, ensuring immediate responses for controlling external devices [34]. Finally, training protocols play a crucial role in improving MI proficiency through the incorporation of effective instruction, feedback, progressive difficulty levels, and structured session scheduling [35]. The primary challenge in MI-BCI research lies in improving the overall performance; thus, in our prior work, we proposed an innovative hybrid pipeline that integrates feature extraction methodologies originally developed for emotion recognition into the motor imagery domain. Our implementation incorporated six distinct feature sets: statistical measures; wavelet analysis; Hjorth parameters; Higher-Order Spectra; fractal dimensions (including the Katz, Petrosian, and Higuchi methods); and a combined five-dimensional feature set integrating all of the abovementioned sets. The classifiers employed in this study include GSVM, CART, LinearSVM, and SVM with a polynomial kernel. The outcome underlined the highest accuracy when three-dimensional fractal dimensions were combined with LinearSVM, surpassing state-of-the-art results when compared to other, previous works [36]. The fractal dimension (FD) is a quantitative metric that assesses the structural complexity of an object or a phenomenon, particularly when it exhibits self-similarity across a specific spatial or temporal scale [14]. As a nonlinear metric, the FD captures the intricate complexity and self-similarity of neurophysiological signals within the time domain [37]. [PERSON] et al. [38] employed [PERSON]'s fractal dimension to analyze neuronal dynamics, enabling the identification and classification of cortical brain regions. This approach contributes to a deeper understanding of the brain's structural and functional integration. [PERSON] et al. [39] used fractal geometry and provided a universal mathematical framework to describe neuronal architecture, connectivity, and pathological variations. [PERSON] et al. [40] analyzed electrophysiological properties such as spectral features, fractal dimension, and entropy, then identified higher gamma-band synchronization and fractal complexity in perceptual networks (PNs) compared to higher cognitive networks (HCNs), highlighting their role in the functioning of the brain and potential disruptions occurring in neurological disorders. [PERSON] et al. [41] conducted fractal dimension (FD) analysis of BOLD activity in resting-state networks (RSNs), then provided insights into the altered complexity of neural dynamics in chronic migraine (CM) patients compared to healthy controls.