Over the years of research, Electroencephalogram (EEG) signal study has grown to give promising outcomes. A lot of research has been done on implementing brain-computer-interfaces, and the brain-computer interface (BCI) algorithm as well as the study of the effects of different stimuli on brain signals. This paper intends to make progress toward that goal by developing a responsive real-time EEG-based brain-to-machine communication system by generating distinct EEG signals at will and identification of the explicit pattern that they reflect for the presented self-induced internal visual and auditory stimuli. The brain-to-machine communication system delivers the real-time capture, analysis, and visualization of the brain signal patterns that can be used for smart medical applications such as rehabilitation robotic control, smart wheelchair, etc.