Development of Mind Control System for Humanoid Robot through a Brain Computer Interface

We develop a mind control system for humanoid robot through a brain-computer-interface (BCI), consisting of a 32 channel electroencephalograph (EEG), a humanoid robot, and a CCD camera. We present two types of humanoid robots in the BCI control system: KT-X PC robot with 20 degrees of freedom (DOFs) or NAO H25 robot with 25 DOFs. The CCD camera takes video clips of a subject or an instructor hand postures to identify mental activities when the subject is thinking "turning right, " "turning left, " or "walking forward." As an initial test, we implement three types of robot walking behaviors: turning right, turning left and walking forward, and report the neural signals correlated to these three mental activities.

[1]  J. Tanji,et al.  Neuronal activity in the primate supplementary, pre-supplementary and premotor cortex during externally and internally instructed sequential movements , 1994, Neuroscience Research.

[2]  M. Rosenzweig,et al.  Biological Psychology: An Introduction to Behavioral, Cognitive, and Clinical Neuroscience , 1996 .

[3]  T. Takenaka,et al.  The development of Honda humanoid robot , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

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

[5]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[7]  Hua O. Wang,et al.  Electroencephalogram-based control of a mobile robot , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[8]  Garcia Molina,et al.  Direct brain-computer communication through scalp recorded EEG signals , 2004 .

[9]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[10]  G. Rizzolatti,et al.  Neural Circuits Involved in the Recognition of Actions Performed by Nonconspecifics: An fMRI Study , 2004, Journal of Cognitive Neuroscience.

[11]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

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

[13]  Cuntai Guan,et al.  Controlling a wheelchair using a BCI with low information transfer rate , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[14]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[15]  Rajesh P. N. Rao,et al.  Control of a humanoid robot by a noninvasive brain–computer interface in humans , 2008, Journal of neural engineering.

[16]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[17]  Marco A. Meggiolaro,et al.  Activation of a mobile robot through a brain computer interface , 2010, 2010 IEEE International Conference on Robotics and Automation.

[18]  Danielle M. Gerhard,et al.  Biological Psychology: An Introduction to Behavioral, Cognitive, and Clinical Neuroscience , 2013, The Yale Journal of Biology and Medicine.