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Review Article

High-level hands-free control of wheelchair – a review

Pages 46-64 | Received 04 Mar 2016, Accepted 05 Jul 2016, Published online: 08 Aug 2016

References

  • Brault MW. Americans with disabilities: 2010 [Internet]. Washington (DC): U.S. Census Bureau; 2012. (Current Population Reports; 70–131). [cited 2015 Jul 23] Available from: http://www.census.gov/prod/2012pubs/p70-131.pdf [https://www.nlm.nih.gov/services/Subject_Guides/healthstatistics/specialpopulations/americans with disabilities].
  • Tibble M. User’s guide to disability estimates and definitions. London: Department for Work and Pensions; 2004.
  • Bley F, Rous M, Canzler U, et al. Supervised navigation and manipulation for impaired wheelchair users. Paper presented at: IEEE International Conference on Systems, Man and Cybernetics 2004;3:2790–2796.
  • Bien Z, Chung M-J, Chang P-H, et al. Integration of a rehabilitation robotic system (kares ii) with human-friendly man-machine interaction units. Auton Robots 2004;16:165–191.
  • Inhyuk M, Myungjoon L, Jeicheong R, et al. Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces. Paper presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003 (IROS 2003). 2003;4:3453–3458.
  • Kang SPG, Rodnay M Tordon J, et al. A hand gesture based virtual interface for wheelchair control. Paper presented at: IEEE/ASME International Conference on Advanced Intelligent Mechatronics 2003;2:778–783.
  • Adachi Y, Kuno Y, Shimada N, et al. Intelligent wheelchair using visual information on human faces. Paper presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, Oct 1998. p. 354–359.
  • Ikushi Y, Katsuhiko S, Takenobu I. Development of headgesture interface for electric wheelchair. Paper presented at: Proceedings of the 1st International Convention on Rehabilitation Engineering & Assistive Technology, i-CREATe ’07 2007; New York, USA. p. 77–80.
  • Ikushi Y, Junichi T, Bisser R, et al. Stereo camera based non-contact non-constraining headgesture interface for electric wheelchairs. ICPR. 2006;4:740–745.
  • Yoshinori K, Teruhisa M, Nobutaka S, et al. Interactive gesture interface for intelligent wheelchairs. Paper presented at: IEEE International Conference on Multimedia and Expo (II); 2000; p. 789–792.
  • Matsumoto Y, Ino T. Ogasawara T. Development of intelligent wheelchair system with face and gaze based interface. Paper presented at: 10th IEEE International Workshop on Robot and Human Interactive Communication; 2001; p. 262–267.
  • Hu H, Jia P, Lu T, et al. Head gesture recognition for hands free control of an intelligent wheelchair. Industrial Robot. 2007;34:60–68.
  • Bergasa LM, Mazo M, Gardel A, et al. Commands generation by face movements applied to the guidance of a wheelchair for handicapped people. Paper presented at 15th International Conference on Pattern Recognition; vol. 4, 2000; p. 660–663.
  • Bergasa LM, Mazo M, Gardel A, et al. Guidance of a wheelchair for handicapped people by face tracking. Paper presented at: 7th IEEE International Conference on Emerging Technologies and Factory Automation; vol. 1, 1999; p. 105–111.
  • Pajkanovic A, Dokic B. Wheelchair control by head motion. Serbian J Electrical Eng. 2013;10:135–151.
  • Jia P, Hu HH, Lu T, et al. Head gesture recognition for hands-free control of an intelligent wheelchair. Industrial Robot. 2007;34:60–68.
  • Rechy-Ramirez EJ, Hu HK, McDonald-Maier H. Head movements based control of an intelligent wheelchair in an indoor environment. Paper presented at: IEEE International Conference on Robotics and Biomimetics; 2012.
  • Perez E, Soria C, López NM, et al. Vision-based interfaces applied to assistive robots. Int J Adv Robotic Syst. 2013:10.
  • Kawarazaki N, Stefanov D, Barragan Diaz AI. Toward gesture controlled wheelchair: a proof of concept study. Paper presented at: IEEE International Conference on Rehabilitation Robotics; 2013.
  • Kawarazaki N, Barragan Diaz AI. Gesture recognition system for wheelchair control using a depth sensor. Paper presented at: IEEE Symposium on Computational Intelligence in Rehabilitation and Assistive Technologies (CIRAT); 2013; p. 48–53.
  • Kang S-P, Katu Pitiya J. A hand gesture controlled semi-autonomous wheelchair. Paper presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems 2004; p. 3565–3570.
  • Posada-Gomez R, Sainchez-Medel LH, Alor Hernandez G. A hands gesture system of control for an intelligent wheelchair. Paper presented at: 4th International Conference on Electrical and Electronics Engineering, ICEEE/IEEE; 2007; p. 68–71.
  • Garg R, Shriram N, Gupta V, et al. A smart mobility solution for physically challenged. Paper presented at: IEEE/ICVES; 2009; p. 168–173.
  • Yi Z, Zhang J, Luo Y. A novel intelligent wheelchair control system based on hand gesture recognition. Paper presented at: Proceedings of IEEE/ICME International Conference on Complex Medical Engineering; 2011.
  • Arturo E, Anne S, Christian L. Multimodal control of a robotic wheelchair: using contextual information for usability improvement. Paper presented at: IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS); 2013; p. 4262–4267.
  • Lu T. A motion control method of intelligent wheelchair based on hand gesture recognition. Paper presented at: 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA); IEEE; 2013. p. 957–962.
  • Rivera LA, DeSouza GN. A power wheelchair controlled using hand gestures, a single sEMG sensor, and guided under-determined source signal separation. Paper presented at: The Fourth IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics; 2012.
  • Rehman S, Raytchev B, Yoda I, et al. Vibrotactile rendering of head gestures for controlling electric wheelchair. Paper presented at: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics; 2009; p. 413–417.
  • Tomari MRM, Kobayashi Y, Kuno Y. Development of smart wheelchair system for a user with severe motor impairment. Paper presented at: International Symposium on Robotics and Intelligent Sensors (IRIS 2012); 2012; p. 538–546.
  • Nishimori M, Saitoh T, Konishi R. Voice controlled intelligent wheelchair. Paper presented at: SICE Annual Conference; 2007.
  • Tahir M, Qadri S, Ahmed A. Voice controlled wheelchair using DSK TMS320C6711. Paper presented at: IEEE International Conference on Signal Acquisition and Processing; 2009.
  • Thiang DW. Speech recognition for controlling movement of the wheelchair. Paper presented at: Proceedings of the 2nd International Conference on Optics and Laser Applications; 2007.
  • Fezari M, Khati A, Attoui H. Embedded system based on multiprocessors to improve thecontrol of a motorisedwheelchair. Paper presented at: 4th International Conference on Design and Technology of Integrated Systems in Nanoscal Era; 2009; p. 167–170.
  • Pacnik G, Benkic K, Brecko B. Voice operated intelligent wheelchair – VOIC. Paper presented at: Proceedings of IEEE International Symposium on Industrial Electronics; 2005; p. 1221–1226.
  • Kathirvelan J, Anilkumar R, Alex ZC, et al. Development of low cost automatic wheelchair controlled by oral commands using standalone controlling system. Paper presented at: 2012 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC); IEEE; 2012. p. 1–4.
  • Pires Urbanonunes G. A wheelchair steered through voice commands and assisted by a reactive fuzzy-logic controller. J Intelligent Robotic Syst. 2002;34: 301–314.
  • Simpson RC, Levine SP. Voice control of a powered wheelchair. IEEE Trans Neural Syst Rehabil Eng 2002;10:122–125.
  • Murai A, Mizuguchi M, Nishimori M, et al. Voice activated wheelchair with collision avoidance using sensor information. Paper presented at: ICROS-SICE International Joint Conference; 2009.
  • Yi JZ, Tan YK, Ang ZR, et al. Microcontroller based voice-activated powered wheelchair control. Paper presented at: Proceedings of the Interenational. Conference on Rehabilitation Engineering and Assistive Technology; 2007; Singapore. p. 67–72.
  • Fezari M, Khati A-E. New speech processor and ultrasonic sensors based embedded system to improve the control of a motorised wheelchair. Paper presented at: Design and Test Workshop, 3rd International Conference; 2008; p. 345–349.
  • Banerjee C, Gupta H, Sushobhan K. Low cost speech and vision based wheelchair for physically challenged. Paper presented at: Proceedings of the International Conference on Computer and Automation Engineering, Singapore; vol. 1, 2010; p. 706–709.
  • Chen R-X, Chen L-G, Chen L. System design consideration for digital wheelchair controller. IEEE Transac Industrial Electron. 2000;47:898–907.
  • Sasou A. Acoustic head orientation estimation applied to powered wheelchair control. Paper presented at: ICST, ROBOCOMM; 2009.
  • Al-Haddad AA, Sudirman R, Omar C. Guiding wheelchair motion based on EOG signals using tangent bug algorithm. Paper presented at: Third International Conference on Computational Intelligence, Modelling & Simulation; 2011.
  • Purwanto D, Mardiyanto R, Arai K. Electric wheelchair control with gaze direction and eye blinking. Artif Life Robotics. 2009;14:397–400.
  • Barea R, Boquete L, Mazo M, et al. System for assisted mobility using eye movements based on electrooculography. IEEE Transac Neural Syst Rehabil Eng. 2002;10:209–218.
  • Banerjee A, Datta S, Das P, et al. Electrooculogram based online control signal generation for wheelchair. Paper presented at: 2012 International Symposium on Electronic System Design (ISED); IEEE; 2012. p. 251–255.
  • Arai K, Mardiyanto R. A prototype of electric wheelchair controlled by eye-only for paralyzed user. J Robotics Mechatronics 2011;23:66.
  • R, Barea Lucian B, Mazo M, et al. E.O.G. guidance of a wheelchair using neural networks, Paper presented at: Proceedings of the 15th International Conference on Pattern Recognition 2000;4:668–671.
  • Lin C-S, Ho C, Chen W, et al. Powered wheelchair controlled by eye-tracking system. Optica Applicata. 2006;36:401–412.
  • Barea R, Boquete L, Bergasa LM, et al. Electrooculograpic guidance of a wheelchair using eye movement’s codification. Int J Robotics Res 2003;22:641–652.
  • Al-Haddad A, Sudirman R, Omar C, et al. Wheelchair motion control guide using eye gaze and blinks based on point bug algorithm. Paper presented at: 3rd International Conference on Intelligent Systems Modelling and Simulation 2012; p. 37–42.
  • Al-Haddad A, Sudirman R, Omar C. Gaze at desired destination, and wheelchair will navigate towards it. new technique to guide wheelchair motion based on EOG signals. Paper presented at: 1st International Conference on Informatics and Computational Intelligence; 2011; p. 126–131.
  • Al-Haddad R, Sudirman C, Omar, et al. Wheelchair motion control guide using eye gaze and blinks based on bug algorithms. Paper presented at: IEEE EMBS International Conference on Biomedical Engineering and Sciences; 2012; p. 398-403.
  • Wijesoma WS, Wee KS, Wee OC, et al. EOG based control of mobile assistive platforms for the severely disabled. Paper presented at: IEEE International Conference on Robotics and Biomimetics; 2005.
  • Felzer T, Freisleben B. HaWCoS: the hands-free wheelchair controlsystem. Paper presented at: Proceedings of the ACM SIGCAPH Conference on Assistive Technologies; 2002; Edinburgh, Scotland: ACM Press; p. 127–134.
  • Han JS, Bien ZZ, Kim DJ, et al. Human–machine interface for wheelchair control with EMG and its evaluation. Paper presented at: Proceedings of the 25th IEEE International Conference on Engineering in Medicine and Biology Society; September 2003; Cancun, Mexico. p. 1602–1605.
  • Moon I, Lee M, Chu J, et al. Wearable EMG-based HCI for electricpoweredwheelchair users with motor disabilities. Paper presented at: Proceedings of theIEEE International Conference on Robotics and Automation; April 2005; Barcelona, Spain. p. 2660–2665.
  • Oonishi Y, Sehoon O, Hori Y. A new control method for power-assisted wheelchair based on the surface myoelectric signal. IEEE Trans Ind Electron. 2010;57:3191–3196.
  • Choi K, Sato M, Koike Y. A new, human-centered wheelchair system controlled by the EMG signal. Paper presented at: Proceedings of the WCCI; 2006; p. 4664–4671.
  • Firoozabadi SMP, Oskoei MA, Hu H. A Human–Computer Interface based on Forehead Multi-channel Bio-signals to control a virtual wheelchair. Paper presented at: Proceedings of the 14th Iranian Conference on Biomedical Engineering (ICBME), Citeseer; 2008; p. 272–277.
  • Xu X, Zhang Y, Luo Y, et al. Robust bio-signal based control of an intelligent wheelchair. Robotics. 2013;2:187–197.
  • Tamura H, Murata T, Yamashita Y, et al. Development of the electric wheelchair hands-free semi-automatic control system using the surface-electromyogram of facial muscles. Artif Life Rob. 2012;17:300–305.
  • Kim KH, Kim HK, Kim JS, et al. A biosignal-based human interface controlling a power-wheelchair for people with motor disabilities. ETRI J. 2006;28:111–114.
  • Chong L, Hong W. The design of wheelchair based on SEMG control. IEEE Transc. 2008;24:721–724.
  • Silva AN, Morère Y, Naves ELM, et al. Virtual electric wheelchair controlled by electromyographic signals. Paper presented at: IEEE Biosignals and Biorobotics Conference (BRC); 2013.
  • Oonishi Y, Oh S, Hori Y. New control method for power-assisted wheelchair based on upper extremity movement using surface myoelectric signal. Paper presented at: 10th International workshop on Advanced Motion Control; 2008; p. 498–503.
  • Yi Z, Lingling D, Yuan L, et al. Design of a surface EMG based human–machine interface for an intelligent wheelchair. Paper presented at: 10th International Conference on Electronic Measurement & Instruments (ICEMI); 2011; p. 132–136.
  • Ohkubo H, Shimono T. Motion control of mobile robot by using myoelectric signals based on functionallydifferent effective muscle theory. Paper presented at: IEEE International conference on Mechatronics; 2013; p. 786–93.
  • Rebsamen B, Burdet E, Teo CL, et al. A brain control wheelchair with a p300 based BCI and a path following controller. Paper presented at: Proceedings of 1st IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics; 2006; Pisa, Italy.
  • Rebsamen B, Guan C, Zhang H, et al. A brain-controlled wheelchair to navigate in familiar environments. IEEE Trans Neural Syst Rehabil Eng. 2010;18:590–598.
  • Rebsamen B, Burdet E, Guan C, et al. Controlling a wheelchair indoors using thought. IEEE Intell Syst Mag. 2007;22:18–24.
  • Tanaka K, Matsunaga K, Wang HO. Electroencephalogram-based control of an electric wheelchair. IEEE Trans Robotics. 2005;21:762–766.
  • Iturrate I, Antelis J, Kubler A, et al. Non-invasive brain Actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans Robot. 2009;25:614–627.
  • Choi K, Cichocki A. Control of a wheelchair by motor imagery in real time. Paper presented at: Proceedings of the 9th International Conference onIntelligent Data Engineering and Automated Learning; 2008; New York: Springer; p. 330–337.
  • Choi K. Control of a vehicle with EEG signals in real-time and system evaluation. Eur J Appl Physiol. 2011;112:755–766.
  • Carlson T, Millan J. Brain–controlled wheelchairs: a robotic Architecture. IEEE Robot Automat Mag. 2013;20:65–73.
  • Del RMJJ, Galan F, Vanhooydonck D, et al. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Proc IEEE Eng Med Biol Soc Conf. 2009;2009:3361–3364.
  • Pires G, Castelo-Branco M, Nunes U. Visual P300-based BCI to steer a wheelchair: a Bayesian approach. Proc IEEE Eng. 2008;2008:658–661.
  • Leeb R, Friedman D, Muller-Putz G, et al. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci. 2007;2007:79642.
  • Vanacker G, del R Millán J, Lew E, et al. Context-based filtering for assisted brain-actuated wheelchair driving. Comput Intell Neurosci. 2007;2007:25130.
  • Dasgupta S, Fanton M, Pham J, et al. Brain controlled robotic platform using steady state visual evoked potentials acquired by EEG. Paper presented at: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers; IEEE; 2010. p. 1371–1374.
  • Prueckl R, Guger C. Controlling a robot with a brain–computer interface based on steady state visual evoked potentials. Paper presented at: Proceedings of the International Joint Conference on Neural Networks; 2010.
  • Ortner R, Guger C, Prueckl R, et al. SSVEP based brain–computer interface for robot control. Paper presented at: Proceedings of the 12th International Conference on Computers Helping People Special Needs 2010; p. 85–90.
  • Prueckl R, Guger C. A brain–computer interface based on steady state visual evoked potentials for controlling a robot. Paper presented at: Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computing Ambient Intelligence 2009; p. 690–697.
  • Barbosa AOG, Achanccaray DR, Meggiolaro MA. Activation of a mobile robot through a brain computer interface. Paper presented at: 2010 IEEE International Conference on Robotics and Automation (ICRA); IEEE; 2010. p. 4815–4821.
  • Li Y, Pan J, Wang F, et al. A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE Trans Biomed Eng. 2013;60:3156–3166.
  • Long J, Li Y, Wang H, et al. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng. 2012;20:720–729.
  • Cho SY, Vinod AP, Cheng KWE. Towards a brain–computer interface based control for next generation electric wheelchairs. Paper presented at: Proceedings of the 3rd International Conference on Power Electronics Systems and Applications; 2009; Hong Kong.
  • Tsui CSL, Gan JQ, Roberts SJ. A self-paced brain–computer interface for controlling a robot simulator: an online event labeling paradigm and an extended Kalman filter based algorithm for online training. Med Biol Eng Comput. 2009;47:257–265.
  • Tsui CSL, Gan JQ. Asynchronous BCI control of a robot simulator with supervised online training. Paper presented at: Proceedings 8th International Conference on Intelligence Data on Engineering Automated Learning; 2007; p. 125–134.
  • Bento VA, Cunha JP, Silva FM. Towards a human–robot interface based on the electrical activity of the brain. Paper presented at: IEEE-RAS 8th International Conference on Humanoid Robots; 2008; Daejeon, Korea. p. 85–90.
  • Craig DA, Nguyen HT, Burchey HA. Two channel EEG thought pattern classifier. Paper presented at: Proceedings of the 28th IEEE EMBS Annual International Conference; 2006.
  • Craig DA, Nguyen HT Adaptive EEG thought pattern classifier for advanced wheelchair control. Paper presented at: Proceedings of the 29th Annual International Conference of the IEEE EMBS; 2007; p. 2544–2547.
  • Carrino F, Dumoulin J, Mugellini E, et al. A self-paced BCI system to control an electric Wheelchair: evaluation of a commercial, low-cost EEG device. Paper presented at: Biosignals and Biorobotics Conference (BRC); 2012.
  • Valsan G, Grychtol B, Lakany H, et al. The strathclyde brain computer interface. Paper presented at: 31st Annual International Conference of the IEEE EMBS; 2009.
  • Kim K-T, Carlson T, Lee S-W. Design of a robotic wheelchair with a motor imagery based brain-computer interface. Paper presented at: International Winter Workshop on Brain-Computer Interface (BCI); 2013; p. 18–20.
  • Chai R, Ling SH, Hunter GP, et al. Mental non-motor imagery tasks classifications of brain computer interface for wheelchair commands using genetic algorithm-based neural network. Paper presented at: WCCI 2012 IEEE World Congress on Computational Intelligence; 2012 June; p. 10–15.
  • Chai R, Ling SH, Hunter GP, et al. Toward fewer EEG channels and better feature extractor ofnon-motor imagery mental tasks classification for a wheelchair thought controller. Paper presented at: 34th Annual International Conference of the IEEE EMBS; 2012.
  • Torres Muller SM, Bastos-Filho TF, Sarcinelli-Filho M. Using a SSVEP-BCI to command a robotic wheelchair. Paper presented at: IEEE International Symposium on Industrial Electronics; 2011 June; p. 957–962.
  • Muller ST, Celeste WC, Bastos-Filho TF, et al. Brain-computer interface based on visual evoked potentials to command autonomous robotic wheelchair. J Med Biol Eng. 2010;30:407–415.
  • Jia W, Huang D, Luo X, et al. Electroencephalography (EEG)-based instinctive brain-control of a quadruped locomotion robot. Paper presented at: 34th Annual International Conference of the IEEE EMBS; 2012.
  • Reshmi G, Amal A. Design of a BCI system for piloting a wheelchair using five class MI based EEG. Paper presented at: Third International Conference on Advances in Computing and Communications; 2013; p. 25–28.
  • Carra M, Balbinot A. Evaluation of sensorimotor rhythms to control a wheelchair. Paper presented at: Biosignal and Biorobotics Conference (BRC); 2013.
  • Jayabhavani GN, Raajan NR, Rubini R. Brain mobile interfacing (BMI) system embedded with wheelchair. Paper presented at: Proceedings of IEEE Conference on Information and Communication Technologies; 2013; p. 1129–1133.
  • Huang D, Qian K, Fei D-Y, et al. Electroencephalography (EEG)-based brain–computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans Neural Syst Rehabil Eng 2012;20:379–388.
  • Hema CR, Paulraj MP, Yaacob AH, et al. Single trial motor imagery classification for a four state brain machine interface. Paper presented at: 5th International Colloquium on Signal Processing & Its Applications (CSPA); 2009.
  • Hema CR, Paulraj MP, Yaacob S, et al. Mu and beta EEG rhythm based design of a four state brain machine interface for a wheelchair. Paper presented at: Proceedings of the International Conference on Control, Automation, Communication Energy Conservation; 2009; Tamil Nadu, India. p. 401–404.
  • Hema CR, Paulraj MP. Control brain machine interface for a power wheelchair. Paper presented at: Proceedings of the 5th Kuala Lumpur International Conference on Biomedical Engineering; 2011; p. 287–291.
  • Hema CR, Paulraj MP, Yaacob S, et al. Robot chair control using an asynchronous brain machine interface. Paper presented at: 6th International Colloquium on Signal Processing & Its Applications (CSPA); 2010.
  • Benevides AB, Bastos TF, Filho MS. Proposal of brain-computer interface architecture to command a robotic wheelchair. Paper presented at: IEEE International Symposiun on Industrial Electronics (ISIE); 2011; p. 2249–2254.
  • Shin B-G, Kim T, Jo S. Non-invasive brain signal interface for a wheelchair navigation. Paper presented at: 2010 International Conference on Control Automation and Systems (ICCAS); IEEE; 2010. p. 2257–2260.
  • Puanhvuan D, Wongsawat YC. Semi-automatic P300-based brain-controlled wheelchair. Paper presented at: Proceedings of International Conference on Complex Medical Engineering (ICME); 2012; p. 455–460.
  • Kaysa WA, Suprijanto Widyotriatmo A. Design of Brain–computer interface platform for semi real-time commanding electrical wheelchair simulator movement. Paper presented at: 3rd International Conference on Instrumentation Control and Automation (ICA); 2013; p. 28–30.
  • Kaneswaran K, Arshak K, Burke E, et al. Towards a brain controlled assistive technology for powered mobility. Paper presented at: Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC); 2010 Aug 31–2010 Sep 4; p. 4176–4180.
  • Hassan MA, Ali AF, Eladawy MI. Classification of the imagination of the left and right hand movements using EEG. Paper presented at: Biomedical Engineering Conference; 2008; p. 18–20.
  • Lee PL, Chang HC, Hsieh TY, et al. A brain-wave-actuated small robot car using ensemble empirical mode decomposition-based approach. IEEE Trans Syst Man Cybern A. 2012;40:1053–1064.
  • Gandhi V, Prasad G, Coyle D, et al. A novel paradigm for multiple target selection using a two class brain computer interface. Paper presented at: Signals and System Conference (ISSC); 2009.
  • Wang Y, Hong B, Gao X, et al. Implementation of a brain–computer interface based on three states of motor imagery. Paper presented at: Proceedings of IEEE Engineering in Medicine and Biology Society; 2007; p. 5059–5062.
  • Kaufmann T, Herweg A, Kübler A. Toward brain–computer interface based wheelchair control utilizing tactually-evoked event-related potentials. J NeuroEng Rehabil. 2014;11:7. DOI: 10.1186/1743-0003-11-7.
  • Bastos-Filho TF, Cheein FA, Müller SMT, et al. Towards a new modality-independent interface for a robotic wheelchair. IEEE Trans Neural Syst Rehabil Eng 2014;22:567–584.
  • Millán J, del R, F, Galán D, et al. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Paper presented at: Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2009; p. 3361–3364.
  • Mandel C, Lüth T, Laue T, et al. Navigating a smart wheelchair with a brain–computer interface interpreting steady-state visual evoked potentials. Paper presented at: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems; IEEE; 2009. p. 1118–1125.

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