## 2 Related Work  
This section will discuss the classification methods commonly applied in the emotion recognition and motor imagery fields. Additionally, it will cover the feature extraction techniques and preprocessing steps that are typically employed. Then, it will discuss the related work in the motor imagery field, highlighting the key issues tackled in this study. Commencing with the preprocessing techniques in [7], the ground truth is established by converting the ER-EEG datasets into self-report scales. All the data from the latter went through a four-sequence preprocessing phase: a notch filter, a high-pass filter, downsampling, and the implementation of a common average reference (CAR) montage. Then, the feature extraction techniques were applied. Following the preprocessing phase, the reference article digests five main feature extraction methods: statistical, wavelet, fractal dimensions, Hirth parameters, and higher-order spectra (HOS), in addition to a combination of all of the latter simultaneously. Finally, regarding performance classifiers, this study employed two primary algorithms: the Gaussian radial basis function support vector machine (GSVM) and the classification and regression tree (CART). As the mentioned study only utilized the binary valence-arousal (positive and negative) plane, the findings indicated that the CART classifier outperformed the GSVM in recognizing emotions from EEG data. Moreover, the fractal dimensions feature set, which includes the Katz [11], Petrosian [12], and Higuchi [13] algorithms, achieved the highest performance classifier accuracy. After interpreting the motor imagery field, regarding dataset usage, the considered research article [14] underlines one prominent motor imagery-related EEG dataset: the BCI Competition IV 2a set [15]. This dataset involved motor imagery tasks, with four event types representing cues for left-hand, right-hand, foot, and tongue movements. Runs four through nine focused on these motor imagery tasks, while runs one to three were dedicated to recording eye movement. Nine subjects participated in the study, each completing nine runs. The data were sampled at 250 Hz using 25 electrodes, 22 of which recorded EEG signals, while the remaining 3 were used for electrooculography (EOG). Applying feature extraction techniques underscores a pipeline of two main feature extraction methods: common spatial patterns (CSP) with multiscale temporal and Riemannian covariance methods. Regarding classifiers, the support vector machine (SVM) was used with linear, rbf, and poly sub-kernels. The results showed that the multiscale CSP features reached an average classification accuracy of 73.70%, which is better than the current leading method, which achieved 70.60%. During the usage of the Riemannian covariance features, it performed better than CSP, with an accuracy of 74.27%; with more time windows, the accuracy jumped to 75.47%. The latter outcomes were achieved across all nine subjects using all four classes from the BCI Competition IV-2a dataset [14]. All researchers in the current field of study, the MI field, have one primary concern: enhancing MI-BCI performance. Five main improvement procedures can be followed [16]: first is preprocessing, which can be achieved by improving or filtering the signal. The latter can be achieved by reconstructing the signal [17; 18], improving the spatial resolution [19], or adding artificial noise [20]. Second is using channel selection by removing redundant and non-task-relevant channels [21] and reducing the device's power consumption [22]. Third is using feature selection, which improves performance by finding the most optimal features. Fourth is dimensionality reduction, which is accomplished by reducing the number of features while retaining as much relevant information as possible. Lastly, combining all the previously mentioned techniques and aiming for general improvement can also achieve higher performance accuracy [16].