A brain-machine interface model based on EEG for automated navigation of mobile robotic device

The highly parallel neurophysiological recordings and the increasing number of signal processing tools open up new avenues for connecting technologies directly to neuronal processes. As the understanding of the neuronal signals is taking a better shape, lot more work to perform is coming up to properly interpret and use these signals for brain-machine interfaces. A simple brain-machine interface may be able to reestablish the broken loop of the persons with motor dysfunction. With time the brain-machine interfacing is growing more complex due to the increased availability of instruments and processes for implementation. In this work, the author proposes a brain-machine interface model through a few simple processes for automated navigation and control of robotic device using the extracted features from the EEG signals based on saccadic eye movement tasks.