###### Abstract  
In this research, five systems were developed to classify four distinct motor functions--forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV)--from EEG signals, using the WAY-EEG-GAL dataset where participants performed a sequence of hand movements. During preprocessing, band-pass filtering was applied to remove artifacts and focus on the mu and beta frequency bands. The initial system, a preliminary study model, explored the overall framework of EEG signal processing and classification, utilizing time-domain features such as variance and frequency-domain features such as alpha and beta power, with a KNN model for classification. Insights from this study informed the development of a baseline system, which innovatively combined the common spatial patterns (CSP) method with continuous wavelet transform (CWT) for feature extraction and employed a GoogLeNet classifier with transfer learning. This system classified six unique pairs of events derived from the four motor functions, achieving remarkable accuracy, with the highest being 99.73% for the GP-RV pair and the lowest 80.87% for the FW-GP pair in inter-subject classification. Building on this success, three additional systems were developed for four-way classification. The final model, ML-CSP-OVR, demonstrated the highest intersubject classification accuracy of 78.08% using all combined data and 76.39% for leave-one-out intersubject classification. This proposed model, featuring a novel combination of CSP-OVR, CWT, and GoogLeNet, represents a significant advancement in the field, showcasing strong potential as a general system for motor imagery (MI) tasks that is not dependent on the subject. This work highlights the prominence of the research contribution by demonstrating the effectiveness and robustness of the proposed approach in achieving high classification accuracy across different motor functions and subjects. EEG signal processing; motor imagery; common spatial patterns (CSP); continuous wavelet transform (CWT); GoogLeNet; transfer learning; K-Nearest Neighbors (KNN); intrasubject classification; intersubject classification +
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can translate neural activity into commands for prosthetic limbs, wheelchairs, and other assistive technologies, thereby restoring a semblance of mobility and autonomy. One of the key social implications of BCI technology is its potential to drastically improve the quality of life for individuals who have lost their motor abilities. For instance, according to the World Health Organization, around 250,000 to 500,000 people suffer a spinal cord injury each year, many of whom experience permanent disability. BCIs can offer these individuals a new avenue for interaction with their environment, which traditional rehabilitation methods might not provide. Case studies highlight the transformative power of BCIs. In one notable instance, a quadriplegic individual was able to control a robotic arm using only their brain signals, allowing them to perform basic tasks such as drinking and eating independently. Another study demonstrated that stroke patients could use BCIs to regain control of their paralyzed limbs after extensive training and therapy. These examples underscore the practical applications of BCIs and their potential to reintegrate individuals into their daily lives. Moreover, statistical data reinforce the need for such advancements. According to a report by the National Spinal Cord Injury Statistical Center, the lifetime costs for an individual with a high cervical injury can exceed USD 4 million. BCIs not only promise to improve the quality of life but also offer a cost-effective solution in the long run by reducing the need for continuous care and assistance. In conclusion, the development and application of BCI systems hold significant social value. They provide a lifeline for individuals who have lost motor functions, offering them a path to regain independence and improve their overall quality of life.