## 7 Conclusions  
Our study proposes a new approach that contributes to enhancing the MI-BCI-related domain of research. This approach combines methods from both emotion recognition and motor imagery. As this study uses all four classes of motor imagery tasks, the analysis of the EEG data is multi-classified instead of the use of binary classification. The addition of a five-dimensional combination of feature sets (statistical, wavelet, HOS, Hjorth, and fractal dimensions) showed that when merging the methods, noise was generated, leading to less accuracy. In addition to our previous study's classifiers, GSVM and CART, two more were introduced: LinearSVM and SVM with a polynomial kernel. This custom hybrid pipeline delivers noteworthy results. The fractal dimensions method, using the Katz, Petrosian, and Higuchi algorithms, achieves superior accuracy in motor imagery tasks, reaching \(\approx\) 79.1%. Regarding classifiers implemented on the BCI Competition IV-2a dataset, LinearSVM regularly accentuates better accuracy than GSVM, CART, and SVM with a polynomial kernel. Our novel method results were confirmed by the referenced emotion recognition and motor imagery articles, as 3D-FD combined with LinearSVM highlighted an increase in the success rate and overall average accuracy. Overall, the findings generated from this study can help create more robust and effective BCI systems, expanding the possibilities for real-world applications. This emphasizes that applying feature extraction and classifier methods in the emotion recognition field to the motor imagery field yields significant results.  
[PERSON] supervised the research, analyzed the results, provided feedback, revised the draft, and approved the final version of the article. [PERSON] implemented the research, executed experimental work, analyzed the results, and revised the final article. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in the BCI Competition IV 2a, [[https://www.bbci.de/competition/iv/](https://www.bbci.de/competition/iv/)]([https://www.bbci.de/competition/iv/](https://www.bbci.de/competition/iv/)), accessed on 13 March 2024 [15].  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BCI & Brain-computer interface. \\ CAR & Common average reference. \\ CART & Classification and regression tree. \\ CLI & Command Line Interface. \\ CSP & Common spatial pattern. \\ DWT & Discrete wavelet transform. \\ EEG & Electroencephalogram. \\ EOG & Electrooculography. \\ ER & Emotion recognition. \\ ER-EEG & Emotion Recognition Electroencephalogram. \\ FD & Fractal dimensions. \\ fMRI & Functional magnetic resonance imaging. \\ fNIRS & Functional near-infrared spectroscopy. \\ GDF & General Data Format. \\ GSVM & Gaussian support vector machine. \\ GUI & Graphical user interface. \\ HOS & Higher-order spectra. \\ HPFs & High-Pass Butterworth Filters. \\ ICA IDE & Independent component analysis. Integrated development environment. \\ MAT file & MATLAB file. \\ MI & Motor imagery. \\ MI-EEG & Motor Imagery Electroencephalogram. \\ Poly & Polynomial. \\ PSD & Power spectral density. \\ RBF & Radial basis function. \\ SNR & Signal-to-noise ratio. \\ SVM & Support vector machine. \\ \end{tabular}