A Brain Controlled Robotic Gait Trainer for Neurorehabilitation

Authors: Qazi Umer Jamil, Abubakr Siddique, Mubeen Ur Rehman, Nida Aziz, Mohsin I. Tiwana

Abstract:

This paper discusses a brain controlled robotic gait trainer for neurorehabilitation of Spinal Cord Injury (SCI) patients. Patients suffering from Spinal Cord Injuries (SCI) become unable to execute motion control of their lower proximities due to degeneration of spinal cord neurons. The presented approach can help SCI patients in neuro-rehabilitation training by directly translating patient motor imagery into walkers motion commands and thus bypassing spinal cord neurons completely. A non-invasive EEG based brain-computer interface is used for capturing patient neural activity. For signal processing and classification, an open source software (OpenVibe) is used. Classifiers categorize the patient motor imagery (MI) into a specific set of commands that are further translated into walker motion commands. The robotic walker also employs fall detection for ensuring safety of patient during gait training and can act as a support for SCI patients. The gait trainer is tested with subjects, and satisfactory results were achieved.

Keywords: Brain Computer Interface (BCI), gait trainer, Spinal Cord Injury (SCI), neurorehabilitation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2702711

References:

[1] Yip PK, Malaspina A. Spinal cord trauma and the molecular point of no return. Mol Neurodegener. 2012;7:6.
[2] http://www.who.int/mediacentre/news/releases/2013/spinal-cord-injury- 20131202/en/ (Last accessed: 05 June 2018).
[3] http://www.asia-spinalinjury.org/committees/prevention facts.php.
[4] Donovan, William H. ”Spinal cord injurypast, present, and future.” The Journal of Spinal Cord Medicine 30.2 (2007): 85.
[5] http://www.mayoclinic.org/diseases-conditions/spinal-cord-injury/basics/ treatment/con-20023837 (Last accessed: 05 June 2018).
[6] King, Christine E., et al. ”The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia.” Journal of neuroengineering and rehabilitation 12.1 (2015): 80.
[7] Birbaumer, Niels, and Leonardo G. Cohen. ”Braincomputer interfaces: communication and restoration of movement in paralysis.” The Journal of physiology 579.3 (2007): 621-636.
[8] Decety J, Gre‘zes J. Neural mechanisms subserving the perception of human actions. Trends Cogn Sci 1999;3:172-8.
[9] Cramer, Steven C., et al. ”Effects of motor imagery training after chronic, complete spinal cord injury.” Experimental brain research 177.2 (2007): 233-242.
[10] https://www.intel.com/content/www/us/en/support/articles/000005985/ mini-pcs/intel-compute-sticks.html (Last accessed: 05 June 2018).
[11] Ang, Kai Keng, and Cuntai Guan. ”Brain-computer interface in stroke rehabilitation.” (2013).
[12] Nicolas-Alonso, Luis Fernando, and Jaime Gomez-Gil. ”Brain computer interfaces, a review.” Sensors 12.2 (2012): 1211-1279.
[13] Usakli, Ali Bulent. ”Improvement of eeg signal acquisition: An electrical aspect for state of the art of front end.” Computational intelligence and neuroscience 2010 (2010): 12.
[14] https://www.emotiv.com/product/emotiv-epoc-14-channel-mobile-eeg/ tab-description (Last accessed: 05 June 2018).
[15] https://www.trans-cranial.com/local/manuals/10 20 pos man v1 0 pdf. pdf (Last accessed: 05 June 2018).
[16] http://openvibe.inria.fr/ (Last accessed: 05 June 2018).
[17] G. Pfurtscheller and C. Neuper. Motor imagery and direct braincomputer communication. proc. of the IEEE, 89(7):11231134, 2001.
[18] http://fourier.eng.hmc.edu/e84/lectures/ActiveFilters/node6.html (Last accessed: 05 June 2018).
[19] http://openvibe.inria.fr/documentation/1.0.1/Doc BoxAlgorithm Simple DSP.html (Last accessed: 05 June 2018).
[20] https://github.com/vrpn/vrpn/wiki (Last accessed: 05 June 2018).