## 1 Introduction  
The human brain is a complex organ that still amazes scientists with its numerous unlocked secrets. Comprising billions of neurons, it plays essential roles in processing sensory data; regulating physiological functions; and enabling cognitive processes, emotional responses, and consciousness. With the interactions between humans and computer systems witnessing a noticeable increase, a global scientific effort aimed at the digital recognition of all human senses (visual, audio, tactile, odor, and taste) and voluntary control (arm movement, foot movement, and tongue movement) has emerged. The information generated in the human body can be processed by various lobes of the brain and consequently transformed into digital form using cameras, sensor-based tracking systems, and biofeedback sensors [1]. Brain-computer interface devices (BCIs) function as instruments designed to capture and decode neural activities. There are two types of BCIs: invasive (requiring the surgical implantation of electrodes) and non-invasive (using external sensors). One of the non-invasive methods used to process brain signals is called electroencephalography (EEG) [2]. This method's popularity among researchers in the non-invasive BCI field arises from several key advantages: First, due to its high temporal resolution, EEG can detect rapid changes in brain activity; second, EEG is safe for users, requiring only the placement of electrodes on the scalp, making it suitable even for sensitive groups such as infants and children; third, it is cost-efficient when compared to other imaging techniques, such as magnetic resonance imaging (MRI); finally, EEG systems are portable and easy to use while still providing real-time data. Clinically, EEG is widely used to diagnose epilepsy, sleep disorders, brain tumors, and other neurological conditions [3]. When it comes to patients with movement disorders and physical disabilities, their common aim is to be able to perform motor tasks enabling them to eat or drink without external help. Being able to walk again and even talk is a luxury they have lost due to illness. This has resulted in the development of the motor imagery (MI) field, opening up the opportunity for these individuals to bypass their condition by controlling external assistive, mobility, and communication devices [4]. Motor imagery-based brain-computer interfaces (MI-BCIs) follow an eight-step framework to address key challenges [5]. The process begins with data acquisition, where electroencephalography (EEG) is commonly used to record brain activity. Electrodes--either gel-based or dry--capture signals while minimizing artifacts due to noise and movement [6]. MI training follows, in which users learn to imagine limb movements (e.g., left hand and right foot) without actual muscle activation, enhancing their proficiency in generating distinct neural patterns [7]. To ensure clean data, signal preprocessing is applied, incorporating bandpass filtering and artifact removal techniques such as independent component analysis (ICA) or common average referencing (CAR) [8; 9]. Once preprocessed, the signals undergo feature extraction, which allows for the identification of relevant neural patterns using methods such as common spatial pattern (CSP) to enhance class discrimination [10], power spectral density (PSD) to analyze frequency bands [11], Wavelet Transforms for time-frequency analysis [12; 13], and fractal dimensions to assess signal complexity [14]. To optimize performance further, channel and feature selection help in determining the most relevant electrodes, reducing the computational load while improving accuracy [15]. With a refined feature set, dimensionality reduction can be applied to handle high-dimensional data. Principal component analysis (PCA) eliminates redundancy, while Linear Discriminant Analysis (LDA) maximizes class separability [16; 17; 18].