Brain controlled robotic platform using steady state visual evoked potentials acquired by EEG

This paper describes a capstone design project by four undergraduate students (first four authors listed in alphabetical order by last name). A noninvasive brain computer interface (BCI) based on the steady state visual evoked potential (SSVEP) has been developed and utilized in controlling an iRobot platform remotely in real-time closed-loop fashion using video feedback from the robot's eye view to the operator over the internet. The operator selects commands by focusing gaze on one of four flickering checkerboards surrounding the video feedback window in order to navigate the robot as desired. The intended/desired control commands are sent to a laptop controlling the iRobot platform via remote wireless connection. Naive subjects are able to control and navigate the robot via the designed interface with minimal practice and classifier calibration. Typical command selection accuracy is over 95% within 4 seconds of the desired transition; most subjects are able to achieve such high accuracies with a 1 second delay.

[1]  Abraham Lempel,et al.  On Fast M-Sequence Transforms , 1998 .

[2]  G Pfurtscheller,et al.  Toward a hybrid brain–computer interface based on imagined movement and visual attention , 2010, Journal of neural engineering.

[3]  Karla Felix Navarro,et al.  A Comprehensive Survey of Brain Interface Technology Designs , 2007, Annals of Biomedical Engineering.

[4]  Rajesh P. N. Rao,et al.  Spectral Changes in Cortical Surface Potentials during Motor Movement , 2007, The Journal of Neuroscience.

[5]  Misha Pavel,et al.  A hybrid generative/discriminative method for EEG evoked potential detection , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[6]  M A Lebedev,et al.  A comparison of optimal MIMO linear and nonlinear models for brain–machine interfaces , 2006, Journal of neural engineering.

[7]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[8]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[9]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[10]  Yijun Wang,et al.  VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier] , 2009, IEEE Computational Intelligence Magazine.

[11]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

[12]  Reinhold Scherer,et al.  Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components , 2005, Journal of neural engineering.

[13]  O. Bai,et al.  A motor imagery-based online interactive brain-controlled switch: Paradigm development and preliminary test , 2010, Clinical Neurophysiology.

[14]  J.C. Sanchez,et al.  Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Kip A Ludwig,et al.  Naïve coadaptive cortical control , 2005, Journal of neural engineering.

[16]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[17]  John K. Chapin,et al.  Realtime Control of a Robot Arm Using Simultaneously Recorded Brain Neurons , 2000 .

[18]  B. Blankertz,et al.  A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue , 2010, PloS one.

[19]  S. Bunce,et al.  Functional brain imaging using near-infrared technology , 2007, IEEE Engineering in Medicine and Biology Magazine.

[20]  Geoffrey M. Boynton,et al.  Efficient Design of Event-Related fMRI Experiments Using M-Sequences , 2002, NeuroImage.

[21]  Shirley M Coyle,et al.  Brain–computer interface using a simplified functional near-infrared spectroscopy system , 2007, Journal of neural engineering.

[22]  Akihiro Ishikawa,et al.  Development of a new rehabilitation system based on a brain-computer interface using near-infrared spectroscopy. , 2010, Advances in experimental medicine and biology.

[23]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[24]  D.J. McFarland,et al.  The wadsworth BCI research and development program: at home with BCI , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[26]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[27]  Shangkai Gao,et al.  An online brain–computer interface using non-flashing visual evoked potentials , 2010, Journal of neural engineering.

[28]  Xiaorong Gao,et al.  An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.

[29]  B. Blankertz,et al.  (C)overt attention and visual speller design in an ERP-based brain-computer interface , 2010, Behavioral and Brain Functions.

[30]  T. J. Sullivan,et al.  A user-friendly SSVEP-based brain–computer interface using a time-domain classifier , 2010, Journal of neural engineering.

[31]  Solomon W. Golomb,et al.  Shift Register Sequences , 1981 .