Research of Mobile Robot Control System Based on SSVEP Brain Computer Interaction

Brain-computer Interface is a new technology to communicate with the outside world without dependence on the peripheral nerves and muscle tissues. It has provided a brand-new interaction way for those suffering from neuromuscular diseases. Using the brain-computer interaction method based on steady state visual evoked potential, 3 instructions are designed to control the robot's motion. In this paper, band-pass filters and independent component analysis are used for artifacts reduction, the power spectral density of the signals is extracted with sliding windows to train the neural network classifier. The experimental results show that the system can complete various experimental tasks with high control precision.

[1]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[2]  D. Regan Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine , 1989 .

[3]  Guodong Yin,et al.  Motion control of a four-wheel-independent-drive electric vehicle by motor imagery EEG based BCI system , 2017, 2017 36th Chinese Control Conference (CCC).

[4]  Surej Mouli,et al.  Hybrid BCI utilising SSVEP and P300 event markers for reliable and improved classification using LED stimuli , 2017, 2017 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).

[5]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  D. Regan Some early uses of evoked brain responses in investigations of human visual function , 2009, Vision Research.

[7]  M. Nuwer Quantitative EEG: II. Frequency Analysis and Topographic Mapping in Clinical Settings , 1988, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[8]  Shangkai Gao,et al.  A practical VEP-based brain-computer interface. , 2006, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  Valentín Chapter 4. , 1998, Annals of the ICRP.

[10]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[11]  F. Afdideh,et al.  Real-time monitoring of military sentinel sleepiness using a novel SSVEP-based BCI system , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[12]  L.J. Trejo,et al.  Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.