Upper limb amputation significantly impacts individuals’ autonomy and quality of life, making the development of advanced prosthetic devices a key area of biomedical research. In recent years, myoelectric prostheses—driven by surface electromyographic (sEMG) signals—have gained increasing attention from researchers and companies, showing great promise in restoring motor function through intuitive control systems. This work presents a pattern recognition-based approach for controlling a four-electrode myoelectric hand prosthesis using a spatio-temporal feature-based Convolutional Neural Network (CNN). To support the development and validation of the proposed method, a dedicated sEMG database was created, referred to as the "EH Database", named after the prototypedevice “Electrode Hub” provided by BionIT Labs.