EEG Signal Identification Approach to Robotic Arm Control by Enhancement of Hand Position and Velocity Estimation
The use of brain signals in the control context drew a lot of attention nowadays. Unlike the absurd notion exist in this context that by considering specific features of brain activities, classifying them is possible, there exists a lot of difficulties. The aim of this study is to declare control commands using brain signals. In this work, Emotiv Epoc+ Device, which is 14 channel headsets, is used. We first tried to distinguish between the left and right hand movements. Then, four main directions of right hand swipe(movement) are identified by brain signals. But accurate recognition of hand gestures is not occurring. Existence of muscular signals with higher amplitude than brain signals, cause brain signal extraction and analysis problems. Accurate measurement of brain signals is impossible without appropriate sensor sensitivity. In this work, different approaches have been made for estimating hand movement. Finally, in order to achieve adequate accuracy for control objectives, by inspiring P300 approach and using face muscles signals, more than 95 percent prosperity achieved in specifying movement command. Moreover, Intention to movement in P300 approach is being replaced by eye blinking detection at the time of observing specific stimulus for movement. Therefore, movement can be detected in four directions and three different speeds. Besides, this method could be extended for other movements. By extracting signal features like maximum and minimum amplitude, exact blinking moment specified for each person and by comparing this with stimulus appearance time, intended command determined. In comparison with brain signals, using blinking signal which is muscular signal, cause fewer difficulties in analyzing and extraction.
|2017||M.Sc.||Dynamical Systems Analysis and Control|