Brain-computer interfaces (BCIs) provide a connection between the human brain and a computer. BCI systems capture neural activities associated with external stimuli or mental tasks, without involving nerves or muscles, and provide alternative non-muscular communication. Interpreted brain activities are translated directly into a script to perform specific tasks, such as controlling wheelchairs, appliances, robotic arms, speech synthesizers, computers, and gaming applications. The level and pattern of neuromuscular activity can be measured by electromyography (EMG) during specific movements. As such, EMG measurements would be valuable in determining muscle stimulation during the different modes of generating gestures, orientation, position, and insertion angles of the upper extremities. These signals contain highly revealing information in everyday upper extremity gestures. This research article aims to develop artificial intelligence models for bioelectric signals (encephalographic and myoelectric), where the main characteristics of each signal are identified and the best computational model for each signal is established, for which tests are implemented with these acquisition devices from a study with real patients and subsequent processing to extract the characteristics of the bioelectric signals.