Solar-Rechargeable Brain-Controlled Wheel-Chair for Paralytic Patients Using Emotiv Epoc+

Ahmad Muhammad Faruk, A. Bakry Hussein, Marwan A. Rashed, Sohair F. Rezeka, Mohamed El-Habrouk


This paper presents the mechanical design and construction of the solar power rechargeable brain controlled wheel-chair with signal acquisition, feature extraction, processing and control methods. It provides the research performed on building a relatively cheap solar rechargeable brain controlled wheel-chair. In the proposed system, the authors aim to augment the abilities of handicapped people such as moving from one place to another, standing up, as well as hands-free control through several artificial techniques. In order to accomplish this task, the proposed system reads and analyses the patient’s brain waves (EEG signals) and turns them into actions to control the proposed wheel chair for moving and standing.

The signals acquired from the EEG were used after filtration, feature extraction, and classification. Furthermore, the signals are passed to the control system of the wheel-chair which consists of motor drivers and linear actuators. An alternative Joystick input is also present in the proposed system for normal use of the wheel-chair. Processing and control are all handled by an Intel based computer and an Arduino Mega 2560-R3 board. The Integration of the system is based on a PID controller and complementary filters leading to high efficient wheel-chair operation.

The system improves the power efficiency by using two solar panels fitted to the rooftop of the wheel-chair in order to trickle charge the batteries of the wheel-chair when it is present under appropriate solar irradiance for the purpose of extending the operating time of the wheel-chair batteries. This led to almost an extra hour of usage as compared to over three hours of usage without the solar panel. The electrical and mechanical designs were all constrained by economical means as well as market availability. The overall cost of the system was around $2000.


Electroencephalogram (EEG); Brain Control Interface (BCI); Arduino Mega 2560-R3; Wheel-chair; Solar Powered; Paralytic Patients;

Full Text:



M. B. I. Reaz, M. S. Hussain and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and Applications”, Biological procedures online, January 18, 2006, issue 8, pp. 11–35.

J.J. Vidal, “Toward direct brain-computer communication”, Annual Review of Biophysics and Bioengineering, 1973, pp. 157–180.

Peter Baranyi, Adam Csapo, “Definition and Synergies of Cognitive Infocommunications”, Acta Polytechnica Hungarica, 2012, Vol. 9 issue No. 1, pp. 67-83.

Tan D., Nijholt A., “Brain-Computer Interfaces and Human-Computer Interaction.”, Human-Computer Interaction Series. Springer London, SN - 978-1-84996-272-8 UR, 2010.

John S. Barlow, “EMG artifact minimization during clinical EEG recordings by special analog filtering”, Electroencephalography and clinical Neurophysiology, Issue 58 (2), February 17, 1984.

Naveen.R. S and Anitha Julian, “Brain computer interface for wheel chair control”, IEEE – issue 31661, 4-6 July 2013.

Niels Birbaumer, Ander Ramos Murguialdaya, and Leonardo Cohend, “Brain–computer interface in paralysis”, Current Opinion in Neurology, 2008, issue 21(6), pp 635-638.

Nijboer F, Sellers E, Mellinger J, “A brain-computer interface in patients with amyotrophic lateral sclerosis.”, Clinical Neurophysiology, 2008, issue 119, pp. 1909–1916.

Birbaumer N, Cohen L., “Brain-computer-interfaces (BCI):communicationand restoration of movement in paralysis.”, J Physios 2007; issue 579.3, pp. 621–636.

Lebedev MA, Nicolelis MA., “Brain machine interfaces: past, present and future.”, Trends euroscience, 2006; issue 29, pp. 536–546.

Sitaram R, Zhang H, Guan C, “Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface”, Neuroimage 2007; issue 34, pp. 1416–1427.

Caria A, Veit R, Sitaram R, “Regulation of anterior insular cortex activity using real-time fMRI.”, Neuroimage 2007; Issue 35, pp. 1238–1246.

Riehle A, Vaadia E, “Motor cortex in voluntary movements. A distributed system for distributed functions”, Boca Raton: CRC Press; 2005.

Felton EA, Wilson JA, Williams JC, Garell PC., “Electrocorticographically controlled brain–computer

interfaces using motor and sensory imagery in patients with temporary subdural electrode implants”, J Neurosurgery 2007; issue 106, pp. 495–500.

Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil “Brain Computer Interfaces, a Review”, sensors (basel), Published: 31 January 2012, issue 12(2), pp. 1211-1279.

A. Eliseyev, C. Moro, T. Costecalde, N. Torres, S. Gharbi, C. Mestais, A. L. Benabid, T. Aksenova, “Iterative N-way PLS for self-paced BCI in freely moving animals.”, Journal of Neural Engineering, issue 8, 2011.

D. Öngür, J.L. Price; “The Organization of Networks within the Orbital and Medial Prefrontal Cortex of Rats, Monkeys and Humans”, Cerebral Cortex, Volume 10, Issue 3, 1 March 2000, pp. 206–219.

Baum, Michele (6 September 2008). "Monkey Uses Brain Power to Feed Itself with Robotic Arm". Pitt Chronicle. Retrieved 6 July 2009.

Gaurav Sinha, Rahul Shahi, Mani Shankar., “Human Computer Interaction”, published by IEEE, Nov. 2010, 19-21.

Polikov, Vadim S., Patrick A. Tresco, and William M. Reichert, "Response of brain tissue to chronically implanted neural electrodes", Journal of neuroscience methods, 2005.08.015, issue 148 (1), pp. 1-18.

Gulati, Tanuj; Won, Seok Joon; Ramanathan, Dhakshin S.; Wong, Chelsea C.; Bodepudi, Anitha; Swanson, Raymond A.; Ganguly, Karunesh, "Robust Neuroprosthetic Control from the Stroke Perilesional Cortex", the Journal of Neuroscience, June 2015, Issue 35 (22), pp. 8653-8661.

Serruya MD, Donoghue JP. “Chapter III: Design Principles of a Neuromotor Prosthetic Device”, Neuroprosthetics: Theory and Practice, ed. Kenneth W. Horch, Gurpreet S. Dhillon. Imperial College Press, 2003, pp. 1158-1196.

Yanagisawa, Takafumi, "Electrocorticograpic Control of Prosthetic Arm in Paralyzed Patients", American Neurological Association, march 2011, issue 71(3), pp.353-361.

Pei, X., "Decoding Vowels and Consonants in Spoken and Imagined Words Using Electrocorticographic Signals in Humans", Journal of Neural Engineering, August 2011, issue 8 (4): 046028

Melissa González, Lochi Yu. “Auditory imagery classification with a non-invasive BCI”. Central American and Panama Convention (concapan xxxvi), IEEE, 2016, issue 36.

Tonio Ball,Markus Kern,Isabella Mutschler,Ad Aertsen,Andreas Schulze-Bonhage, “Signal quality of simultaneously recorded invasive and non-invasive EEG”. Neuroimage, Elsevier, 1 July 2009, issue 46(3), pp. 708-716., “Dassault Systemes SolidWorks Corporation” copyright 2017., “Arduino – Arduino Mega 2560 - REV3”, copyright 2017., “Arduino”, copyright 2017 arduino.

Martin A. Green, Keith Emery, Yoshihiro Hishikawa, Wilhelm, Warta, Ewan D. Dunlop, “Solar cell efficiency”.

Chetan Singh Solanki, “solar photovoltaic technology and systems: A Manual for Technicians, Trainers and Engineers,” PHI Learning, New Delhi, 2013.

Ulanoff, L. Elon Musk and SolarCity unveil ‘world’s most efficient’ solar panel, Mashable, 2 October 2015, accessed 28 June 2016.

I. Boldea, S.A. Nasar, “Linear electric actuators and generators,” IEEE, (18-21 May, 1997), Milwaukee, Wisonsin, USA.

Robert L. Norton, “Machine design, an integrated approach,” Pearson, Massachusetts, fifth edition, 2014.

John J. Grainger and William D. Stevenson, “Power System Analysis,” McGraw-Hill, New York, 2011.

James Larminie and Johns Lowry, “Electric Vehicle Technology Explained,” John Wiley & Sons, New Jersey, 2012.

Proflex; “Crystalline silicon terrestrial photovoltaic (PV) modules – Design qualification and type approval Reference”, International Standard IEC61215.

Michael Boxwell, “Solar Electricity Handbook: A Simple, Practical Guide to Solar Energy - Designing and Installing Photovoltaic Solar Electric Systems,” Greenstream Publishing, Coventry, 2010.

Martin Sokol, Didac Mallorquin Colina, Nikos Konstantinidis, Ahmed Berrada, “MPPT Tracker S.M.K.B. Edition: European Project Semester thesis,” Ecole Nationale d'Ingénieurs de Tarbes, Tarbes, 2010.

Mark Hankins, “Stand-alone Solar Electric Systems: The Earthscan Expert Handbook for Planning, Design and Installation”. Earthscan, London, 2010.

Rehan Jamil, “Maximum Power Point Tracker (MPPT) Based Photovoltaic (PV) Water Pumping System Using AC and DC Motors”. GRIN Verlag, München, 2014.

Ernst Niedermeyer, F. H. Lopes da Silva, “Electroencephalography: Basic Principles, Clinical Applications, and Related Fields,” Lippincott Williams & Wilkins, Pennsylvania, 2005.

Kylie J Barnett, “Colour knowledge: The role of the right hemisphere in colour processing and object colour knowledge. Laterality,” National Center for biotechnology information and U.S. National library of medicine, Maryland, 2008., Neurosky, 2012., Neurosky, 2012., Emotiv, 2011

Saleh Ibrahim Alzahrani, “p300 wave detection using emotiv epoc+ headset: effects of matrix size, flash duration, and colors,” ProQuest, Michigan, Fall 2016.

Kamel Nidal, Aamir Saeed Malik, “EEG/ERP Analysis: Methods and Applications,” CRC Press, Boca Raton, 2014.

Maurizio Di Paolo Emilio, “Embedded Systems Design for High-Speed Data Acquisition and Control,” Springer, New York, 2014

Saeid Sanei, “Adaptive processing of brain signal,” John Wiley & Sons, New Jersey, 2013.

Ivo K¨athner, Selina C Wriessnegger, Gernot R M¨uller-Putz, Andrea K¨ubler, and Sebastian Halder, “Effects of mental workload and fatigue on the p300, alpha and thetaband power during operation of an ERP (P300) brain–computer interface,” National Center for biotechnology information and U.S. National library of medicine, 2014.

SC Kleih, F Nijboer, S Halder, and A Kubler, “Motivation modulates the p300 amplitude during brain–computer interface use”, Clinical Neurophysiology, 121 (7), pp. 1023–1031, 2010.

H´el`ene Otzenberger, Daniel Gounot, and JR Foucher, “Optimisation of a post-processing method to remove the pulse artifact from eeg data recorded during fmri: an application to p300 recordings during e-fmri”, Neuroscience research, 57 (2), pp. 230–239, 2007

Pedro Campos,Nicholas Graham, Joaquim Jorge, Nuno Nunes, Philippe Palanque, Marco Winckler, “Human-Computer Interaction ”, New York: Springer, 2011.

Emotiv Epoc+ Manual:, Emotiv systems, 2014.

Pritom Chowdhury, S. S. Kibria Shakim, Md Risul Karim,Md Khalilur Rhaman "Cognitive efficiency in robot control by Emotiv EPOC" Informatics, Electronics & Vision (ICIEV) 2014 International Conference, 23-24 May 2014.

E.T. McAdams, J. Jossinet, R. Subramanian, R.G.E. McCauley "Characterization of gold electrodes in phosphate buffered saline solution by impedance and noise measurements for biological applications", Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE, 30 Aug.-3 Sept. 2006.

Debener S, Minow F, Emkes R, Gandras K, de Vos M,” How about taking a low-cost, small, and wireless EEG for a walk?” Psychophysiology, 2012, 49(11):1617–1621.

Rabiner, L. R.; Gold, B. “Theory and application of digital signal processing”. Englewood Cliffs, N.J., Prentice-Hall, Inc., 1975. 777 p

Gabriel Pires, Urbano Nunes, and Miguel Castelo-Branco, “Statistical spatial filtering for a p300-based bci: tests in ablebodied, and patients with cerebral palsy and amyotrophic lateral sclerosis”, Journal of neuroscience methods, 195 (2), pp. 270–281, 2011.

I. Selesnick, “Introduction to Sparsity in Signal Processing, Connexions”, Web site:, May 27, 2012.

Dean J Krusienski, Eric W Sellers, François Cabestaing, Sabri Bayoudh, Dennis J McFarland, Theresa M Vaughan and Jonathan R Wolpaw. “A comparison of classification techniques for the P300 Speller” Journal of Neural Engineering, Volume 3, Number 4, Published 26 October 2006.

D.J. Krusienski,E.W, Sellers,D.J, McFarland,T.M, Vaughan,J.R, Wolpaw, “Toward enhanced P300 speller performance” Journal of Neuroscience Methods, 15 January 2008.

Kun Li, Ravi Sankar, Yael Arbel, Emanuel Donchin, “Single trial independent component analysis for P300 BCI system” EMBC Annual International Conference of the IEEE, 3-6 Sept. 2009.

M. Kaper, P. Meinicke, U. Grossekathoefer. “BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm” IEEE, 24 May 2004.

S M Abdullah-Al-Mamun, “A novel algorithm for Emotiv EPOC spellers based on Emokey”, Clinical Neurophsyology, November 2014.

Yann Renard, Fabien Lotte, Guillaume Gibert, Marco Congedo “OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain–Computer Interfaces in Real and Virtual

Environments” Presence: Teleoperators and Virtual Environments, Volume 19, Issue 1, February 2010, p.35-53.

H. Mirghasemi, R. Fazel-Rezai, M. B. Shamsollahi, “Analysis of P300 Classifiers in Brain Computer Interface Speller” IEEE, 30 Aug.-3 Sept. 2006.

L. Vega-Escobar, A. E. Castro-Ospina, L. Duque-Muñoz, "Feature extraction schemes for BCI systems", 20th Symposium on Signal Processing Images and Computer Vision (STSIVA) 2015, pp. 1-6, 2015.

A Chamanzar, M Shabany, A Malekmohammadi, S Mohammadinejad “Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform”, Published in: IEEE Transactions on Biomedical Circuits and Systems Volume: 99, June 2017.

Fabien Lotte, Marco Congedo, Anatole Lecuyer, Fabrice Lamarche, Bruno Arnaldi, “A review of classification algorithms for EEG-based brain computer interfaces” Journal of Neural Engineering, IOP Publishing, 2007, Issue 4, pp.24.

Preeti Sharma, Santvana Vats, “Brain computer interface” UG, Department of Electronics and Communication Engineering, Raj Kumar Goel Institute of Technology for Women, UP, India.

A. Materka, M. Byczuk, “Using Comb Filter to Enhance SSVEP for BCI Applications” MEDSIP, 2006, IET 3rd International Conference.

Na Lu, Tengfei Li, Xiaodong Ren, Hongyu Miao, “A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines”, IEEE Transactions on Neural Systems and Rehabilitation Engineering,VOLUME: 25, Issue: 6, June 2017.

Begg, Rezaul, “Neural Networks in Healthcare: Potential and Challenges: Potential”, Idea Group Publishing, 2006, ISBN 1591408504.

Md. Shakhawat Hossain, Simanto Saha, Md. Ahasan Habib, Abdullah Al Noman, Takia Sharfuddin, Khawza I. Ahmed, “Application of wavelet-based maximum entropy on the mean in channel optimization for BCI”, Medical Engineering, Health Informatics and Technology (MediTec), 2016 International Conference.

Emotiv Tools: XavierEmoKey from Emotiv official website, Emotiv-Xavier-Tools-XavierEmoKey, Emotiv systems.

C. Guger, A. Schlogl, C. Neuper, D. Walterspacher, T. Strein, G. Pfurtscheller, “Rapid prototyping of an EEG-based braincomputer interface (BCI)”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Volume: 9, Issue: 1, March 2001.

Yann Renard, Fabien Lotte, Guillaume Gibert, Marco Congedo, Emmanuel Maby, Vincent Delannoy, Olivier Bertrand, Anatole Lécuyer, “OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain–Computer Interfaces in Real and Virtual Environments”, Presence Volume: 19, Issue: 1, Feb. 1 2010.

D. W. K. Ng, Y. W. Soh, S. Y. Goh, "Development of an autonomous BCI wheelchair", Computational Intelligence in Brain ComputerInterfaces (CIBCI) 2014 IEEE Symposium, pp. 1-4, Dec 2014.

R. S. Naveen, A. Julian, "Brain computing interface for wheel chair control", Fourth International Conference on Computing Communications and Networking Technologies (ICCCNT), pp. 1-5, 2013.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.