Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

A Brain Computer Interface (BCI) is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command navigation of the robot. This prototype works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93 percent.

[1]  G. R. Muller,et al.  Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment , 2003, Clinical Neurophysiology.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  Dragan F. Dimitrov,et al.  Cortical Representation of Ipsilateral Arm Movements in Monkey and Man , 2009, The Journal of Neuroscience.

[5]  Karin Rothschild,et al.  Mastering Matlab 5 A Comprehensive Tutorial And Reference , 2016 .

[6]  Hynek Wichterle,et al.  Induced Pluripotent Stem Cells Generated from Patients with ALS Can Be Differentiated into Motor Neurons , 2008, Science.

[7]  Bernhard Graimann,et al.  A comparison of common spatial patterns with complex band power features in a four-class BCI experiment , 2006, IEEE Transactions on Biomedical Engineering.

[8]  G. Oriolo,et al.  Non-invasive brain–computer interface system: Towards its application as assistive technology , 2008, Brain Research Bulletin.

[9]  Alicia M. Gibb,et al.  NEW MEDIA ART, DESIGN, AND THE ARDUINO MICROCONTROLLER: A MALLEABLE TOOL , 2010 .

[10]  Soo-Young Lee,et al.  Brain–computer interface using fMRI: spatial navigation by thoughts , 2004, Neuroreport.

[11]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

[12]  David Ewins,et al.  The Emotiv EPOC neuroheadset: an inexpensive method of controlling assistive technologies using facial expressions and thoughts? , 2011 .

[13]  A. Graser,et al.  Brain-Computer Interface for high-level control of rehabilitation robotic systems , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[14]  Vladimir Bostanov,et al.  BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram , 2004, IEEE Transactions on Biomedical Engineering.

[15]  B. Allison,et al.  The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[16]  Michael Margolis,et al.  Make an Arduino-Controlled Robot , 2012 .

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

[18]  Thierry Dutoit,et al.  Performance of the Emotiv Epoc headset for P300-based applications , 2013, Biomedical engineering online.

[19]  M.M. Moore,et al.  Real-world applications for brain-computer interface technology , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

[21]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

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