## 1 Introduction  
The goal of rehabilitation is to repair injured muscles and motor functions through therapy and training. brain-computer interface (BCI) technology is an emerging field in neurotechnology. Applications for BCI have been used in many different fields to aid people with neuromuscular conditions such as stroke and spinal cord injuries to enhance their overall quality of life. One possible replacement for current prosthesis technology, assistance, and rehabilitation might be a system integrating BCI, robotics, neuroscience, machine learning, and deep learning. BCI may also help to improve or restore the motor skills that the brain has lost. BCI provides easy-to-use technological assistance and robotic prostheses, powered by brain activity. With BCI, a user's brain activity may be translated into the output command needed to perform any predefined operation on appropriate equipment [1]. More advanced neurophysiologically driven solutions that support behavioral training and recovery are the foundation of rehabilitation technology. For example, depending on the neuromotor activity of the operand, a robot-guided system may assist in the motion of a damaged limb [2]. For stroke survivors, BCI enables neurological stimulation and improvement of neuromotor outcomes [3]. The main purpose of BCI applications is to support people in their everyday lives who have severe motor impairments. A number of BCI-capable gadgets have been created to assist with rehabilitation and daily tasks for people [4]. Furthermore, a large number of investigations have confirmed that BCI can help those with severe impairments, such as paralysis. BCI provides direct brain-to-technology contact, which might aid in the rehabilitation of a handicapped person afflicted with musculoskeletal disorders. The location of the electrodes determines whether a method of monitoring brain activity is invasive or non-invasive. One or more BCI devices are utilized for direct electrode placement in the brain cavity to monitor the brain area during neurosurgical procedures that require invasive operations [5]. Although this process produces very high-quality signals, it has a significant negative impact since it increases the amount of scar tissue in the brain [6]. An example of an invasive technology is the Electrocorticograph (ECoG), which measures brain activity directly from the surface of the brain [7]. Compared to the invasive method, a non-invasive electroencephalogram (EEG) is more precise and less costly, but less pleasant [4]. For example, an EEG tracks and records waves of electrical activity in the brain by placing electrodes on the scalp. These signal waves are transmitted to a computing system based on the discovered patterns in the produced waves. However, from the view of clinical conditions and wearable electronics, implanted devices are more precise due to direct contact with target biological tissues. For instance, in heart and hemodynamic monitoring, implanted devices provide higher precision and reliability. Despite this precision, non-invasive methods offer significant advantages. Non-invasive EEG is more accessible and can be performed in various settings, including at home or in outpatient clinics, without the need for surgical procedures. This reduces the risk of complications such as infections and scarring, which are associated with invasive methods. Additionally, non-invasive EEG is more comfortable for patients, especially for those who may be apprehensive about surgical interventions. The lower cost of non-invasive EEG also makes it a more feasible option for widespread use, especially in resource-limited settings. While it may be less pleasant due to the need for repeated application of electrodes and the potential for discomfort during prolonged sessions, advancements in EEG cap designs and gel-free electrodes are continually improving patient comfort and usability. Non-invasive EEG thus represents a balance between precision, cost, and patient safety, making it a valuable tool in both clinical and research settings.