Fingers Motion Prediction from ECoG Signal based on Deep-Fractal Modeling

By noticing to computer science, neuroscience and electronics have led to the emergence of another field called medical engineering and mechatronics, so the presentation of combined methods between these sciences is considered as an interdisciplinary science. Since methods can be combined and new applications can be obtained, this research also studies an interdisciplinary topic that is obtained by combining the named sciences and its subset which includes artificial intelligence, applied sciences of medical engineering and mechatronics. Different signals can be extracted from the human body which are vital signals. Because these signals can be extracted and then processed and analyzed, they provide physicians with useful information about their physical condition. One of these vital signals is the ECoG, a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex ,which monitors the body's activities and analyzes it. Moving organs can be examined by using the ECoG signal and its analysis, including the limbs. This occurs when there is a need in the area of the artificial arm or leg, as well as other organs. ECoG signal processing is also used for people with disabilities. In this research, we try to present an intelligent method by processing ECoG signals in order to visualize the movement of fingers, especially hands and feet, which is based on intelligent methods and machine learning. The proposed method is that the ECoG signal is extracted using the fractal model, and then using deep learning which it is analyzed based on classification, due to the nature of in-depth training in deep learning, it can visualize finger movements.

Keywords:

ECoG Signal , Fingers Movements , Fractal Model , Deep Learning