## 1 Introduction  
To explore the complexity of the human brain in terms of electrical signals emitted during cerebral activity, the measurement and monitoring of cerebral activity are performed using several approaches. In this study, we start by providing an overview of brain activity measurement techniques. First is functional magnetic resonance imaging (fMRI), which uses electromagnetic fields to track changes in blood flow, blood volume, and oxygen levels during brain activity imaging; however, its suitability for fast BCI systems is not great, and head movements easily affect the tracking technique [1]. Second is functional near-infrared spectroscopy (fNIRS), which tracks brain metabolism; this technique uses infrared light, but it is limited to the outer layers of the brain due to the light penetration depth [2]. The most important technique involves electroencephalography (EEG) signals; during brain neural activation, electric currents are emitted. EEG is used to detect electrical brain activity, but signals can sometimes be affected by other electrical currents in the body [3]. The brain-computer interface system captures EEG signals through sensors and electrodes integrated into the device. These signals are then digitized and stored in a format suitable for research-ready explorations [4]. The technique commonly used by scientists and neurologists nowadays is EEG. It has essential advantages, which is why it is popular among researchers in this field of study: its non-intrusive nature, the fact that it does not necessitate a surgical procedure, its low cost compared to the other approaches mentioned above, and the fact that it operates entirely through a brain-computer interface placed on the subject's scalp [5]. The importance of the emotion recognition (ER) field is significantly increasing with the number of electronic devices used by individuals; people are spending more time on social media, playing online video games, shopping online, and using other electronic products [6]. The capability of processing human emotions and comprehending them is crucial for studying and investigating emotional recognition procedures with different emotion states (i.e., valence, arousal, happiness, and sadness) and how they influence the subject's interaction with their surroundings and even their decision-making abilities [7]. The motor imagery (MI) field introduces numerous potential applications to improve lives; it helps patients regain the ability to perform tasks they lost due to a debilitating illness, a brain injury, or even a severe accident. Detecting abnormal brain activity in clinical diagnosis can help with epilepsy or seizures, help people restore vision, or even allow a person with physical disabilities to perform motor imagery tasks (such as the imagination of movement, considering the right and left hands, feet, and tongue) and control a complex external device using their brain [8]. There are several challenges facing scientists in the motor imagery field, including some limitations regarding current feature extraction and classification methods in MI tasks. First is the low signal-to-noise ratio (SNR) due to the noisy characteristics of EEG signals (i.e., muscle movements, eye blinks, and electrical interference), making the hunt for valuable features that reflect MI tasks, considering brain activity, hard to discern [9]. Second is the use of traditional features (i.e., power spectral densities and band power), which often face difficulties in capturing the most significant information needed to differentiate between motor imagery tasks, causing lower accuracy [10]. Finally, advanced feature extraction methods and performance classifiers can be computationally demanding, making them difficult to use effectively for motor imagery tasks. This is where our research can be used in practice, as this study's main idea is the cross-application of both techniques from emotion recognition (ER-EEG) and motor imagery (MI-EEG) tasks, leveraging ER feature extraction techniques to improve MI classification.