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Brain Machine Interface (BMI) is a direct communication channel between brain and computer. It provides communication methods between the brain signals that are detected from the scalp Electroencephalogram (EEG) and an external device (robot arm, wheelchair...). People who are paralyzed or have other sever movement disorders need alternative methods for communication and control. It allows them to control the environment without the need to control muscle activity. We show that multi-electrode recordings from the primary motor cortex can be used to predict arm and hand muscle activity. The Electroencephalogram (EEG) signals that come from the scalp containing an electrical signals bearing what is going on in the human brain or the things that would like to do it. Current BMIs suffer from many problems including inaccuracies, delays between thought, false positive detections, and constraints on invasive technologies that need cost money and cause many dangerous during surgery to the subject so, this study overcomes all previous problems.