Extracting motor imagery features from EEG signal (BCI) using FFT in order to control a two-degree-of-freedom arm robot

Brain Computer Interface (BCI) is a recently emerging technology which is advancing and developing at a fast pace. This interface provides a potential to establish a communication bridge that enables a person to send commands and messages to an automated system such as a robot or prosthesis, by means of his/her brain signals. A real time EEG based robotic arm movement control is an open area of research where signals extracted from the human brain are used for generation of controlling signals even for gaming, remote control/teleportation and other applications. This paper introduces a simpler method of realization of robotic arm control in real time from Electroencephalographic (EEG) signals. The purpose of this study is to mimic the real simulation of a robotic arm being controlled from EEG signals. This can be helpful to develop an on-line robotic arm movement using EEG signals for assisting disabled persons with their every-day needs. Using EEG signals, robotic arm is simulated on the onset of movement in real time. Simple algorithms with less computational overhead are implemented for detecting movement related information of right arm using EEG signals. Preliminary results have been found encouraging for this technique to be used in real time scenarios.

Keywords:

Electroencephalogram (EEG) , brain-computer interface (BCI) , Fast Fourier Transform (FFT) , Power Spectral Density (PSD)