Skip to main content
SpringerLink
Log in

Towards an EEG-based brain-computer interface for online robot control

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F-measure, and our results show that the algorithms can accurately classify the subjects’ thoughts into corresponding action commands.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Acampora G, Cook DJ, Rashidi P, Vasilakos AV (2013) A survey on ambient intelligence in healthcare. IEEE Proc. 101:2470–2494

    Article  Google Scholar 

  2. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kuber A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralysed. Nature 398:297–298

    Article  Google Scholar 

  3. Birbaumer N, Hinterberger T, Kubler A, Neumann N (2003) The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans Neural Syst Rehabil Eng 111(2):120–123

    Article  Google Scholar 

  4. Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, KR Muller (2008) The Berlin brain computer interface: accurate performance from first-session in BCI-nave subjects. IEEE Trans Biomed Eng 55(10):2452–2462

    Article  Google Scholar 

  5. Barry RJ, Clarke AR, McCarthy R, Selikowitz M (2002) Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in TOVA scores, behavioral ratings, and WISC-R performance. Appl Psychophysiol Biofeedback 20(1):83–99

    Google Scholar 

  6. Barry RJ, Clarke AR, McCarthy R, Selikowitz M (2002) EEG coherence in attention-deficit/hyperactivity disorder: A comparative study of two DSM-IV types. Clin Neurophysiol 113(4):579–85

    Article  Google Scholar 

  7. Buttfield A, Ferrez PW, Millan JR (2006) Towards a robust BCI: Error potentials and online learning. IEEE Trans Neural Syst Rehabil Eng 14(2):164–168

    Article  Google Scholar 

  8. Brain Controlled NXT Robot (2013). http://bci.vourvopoulos.com/

  9. Choi K, Cichocki A (2008) Control of a wheelchair by motor imagery in real time. Int Data Eng Autom Learn–IDEAL 5:330–337

    Google Scholar 

  10. Chen M, Gonzalez S, Vasilakos A, Cao H S, Leung VCM (2011) Body area networks: a survey. Mob Netw Appl 16:171–193

    Article  Google Scholar 

  11. Cernea D, Olech PS, Ebert A, Kerren A (2011) EEG-based measurement of subjective parameters in evaluations. HCI Int 2011–Posters Extended Abstr Commun Comput Inf Sci 174:279–283

    Article  Google Scholar 

  12. Duvinage M, Castermans T, Dutoit T, Petieau M, Hoellinger T, Saedeleer C, Seetharaman K, Cheron G (2012) A P300-based Quantitative Comparison between the Emotiv Epoc Headset and a Medical EEG Device. Biomedical Engineering/765: Telehealth/766: Assistive Technologies

  13. Develop for EPOC (2013). http://emotiv.com/epoc/develop.php

  14. Emotiv EPOC Software Development Kit (2013). http://www.emotiv.com/store/hardware/299/

  15. Emotiv Wiki (2013). http://emotiv.wikia.com/wiki/Emotiv_EPOC

  16. Friman O, Luth T, Volosyak I, Graser A (2007) Spelling with steady-state visual evoked potentials. In: 3rd international IEEE/EMBS conference on neural engineering. Kohala Coast, pp 354–357

  17. Fortino G, Fatta GD, Pathan M, Vasilakos AV (2014) Cloud-assisted body area networks: state-of-the-art and future challenges. Wirel Netw 20:1925–1938

    Article  Google Scholar 

  18. Hill NJ, Lal TN, Hinterberger T, Wilhelm B, Nijboer F, Mochty U, Widman G, Elger C, Scholkopf B, Kubler A, Birbaumer N (2006) Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. IEEE Trans Neural Syst Rehabil Eng 14(2):183–186

    Article  Google Scholar 

  19. Hayajneh T, Almashaqbeh G, Ullah S, Vasilakos AV (2014) A survey of wireless technologies coexistence in WBAN: analysis and open research issues. Wirel Netw 20(8):2165–2199

    Article  Google Scholar 

  20. Kalcher J, Flotzinger D, Neuper C, Gölly S, Pfurtscheller G (1996) Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns. Med Biol Eng Comput 34(5):382–388

    Article  Google Scholar 

  21. Kubler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, Mcfarland DJ, Birbaumer N, Wolpaw JR (2005) Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64 (10):1175–1177

    Article  Google Scholar 

  22. Leeb R, Lee F, Keinrath C, Scherer R, Bischof H, Pfurtscheller G (2008) Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans Neural Syst Rehabil Eng 15(4):473–482

    Article  Google Scholar 

  23. Lablor EC, Kelly SP, Finucane C, Burke R, Smith R, Reilly RB, McDarby G (2005) Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP J Appl Signal Process 19:3156–3164

    Article  MATH  Google Scholar 

  24. Lego Mindstorm NXT (2013). http://www.legomindstormsnxt.co.uk/lego-nxt.html

  25. Movassaghi S, Abolhasan M, Lipman J, Smith D, Jamalipour A (2014) Wireless body area networks: a survey. IEEE Commun Surveys Tuts 16:1658–1686

    Article  Google Scholar 

  26. Martinez P, Bakardjian H, Cichocki A (2006) Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm. Comput Intell Neurosci 2007(2007):1–9

    Google Scholar 

  27. Muller-Putz GR, Pfurtscheller G (2008) Control of an electrical prosthesis with an SSVEP-based BCI. IEEE Trans Biomed Eng 55(1):361–364

    Article  Google Scholar 

  28. Muller-Putz GR, Scherer R, Brauneis C, Pfurtscheller G (2005) Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng 2(4):123–130

    Article  Google Scholar 

  29. Martin AR, Sankar T, Lipsman N, Lozano A M (2012) Brain-Machine Interfaces for Motor Control: A Guide for Neuroscience Clinicians. Can J Neurol Sci 39:11–22

    Article  Google Scholar 

  30. Neuper C, Muller-Putz GR, Kubler A, Birbaumer N, Pfurtscheller G (2003) Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol 114(3):399–409

    Article  Google Scholar 

  31. Nijholt A, Tan D, Pfurtscheller G, Brunner C, Millan JR, Allison BZ, Graimann B, Popescu F, Blankertz B, Muller KR (2008) Brain-computer interface for intelligent systems. IEEE Intell Syst 23(3):72–79

    Article  Google Scholar 

  32. Pfurtscheller G, Neuper C (2001) Motor imagery and direct brain-computer communication. IEEE Proc 89(7):1123–1134

    Article  Google Scholar 

  33. Piccione F, Giorgi F, Tonin P, Priftis K, Giove S, Silvoni S, Palmas G, Beverina F (2006) P300-based brain-computer interface: reliability and performance in healthy and paralysed participants. Clin Neurophysiol 117(3):531–537

    Article  Google Scholar 

  34. Pedrycz W, Vasilakos AV (1999) Linguistic Models and Linguistic Modeling. IEEE Trans Syst, Man, Cybern. B, Cybern 29:745–757

    Article  Google Scholar 

  35. Scherer R, Muller-Putz GR, Neuper C, Graimann B, Pfurtscheller G (2004) An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans Biomed Eng 51(6):979–984

    Article  Google Scholar 

  36. Sugiarto I, Allison BZ, Graser A (2009) Optimization strategy for SSVEP-based BCI in spelling program application. In: ICCET’08 international conference on computer engineering and technology, pp 223–226

  37. Salameh HB, Shu T, Krunz M (2007) Adaptive crosslayer MAC design for improved energy-efficiencey in multi-channel wireless sensor networks. Ad Hoc Net 5:844–854

    Article  Google Scholar 

  38. Trejo LJ, Rosipal R, Matthews B (2006) Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Trans Neural Syst Rehabil Eng 14(2):225–229

    Article  Google Scholar 

  39. Ullah S, Vasilakos AV, Chao HC, Suzuki J (2014) Cloud-assisted wireless body area networks. Inf Sci 284:81–83

    Article  Google Scholar 

  40. Vaughan TM, McFarland DJ, Schalk G, Sarnacki WA, Krusienski DJ, Seller EW, Wolpow JR (2006) The Wadsworth BCI research and development program: at home with BCI. IEEE Trans Neural Syst Rehabil Eng 14(2):229–233

    Article  Google Scholar 

  41. VAROL E (2010) Raw EEG data classification and applications using SVM. Tese de doutorado. Istanbul Technical University. Electrical-electronics Engineering Faculty

  42. Vourvopoulos A, Liarokapis F (2011) Brain-controlled NXT Robot: Tele-operating a robot through brain electrical activity. 2011 Third International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES)

  43. Yao JT, Vasilakos AV, Pedrycz W (2013) Granular Computing: Perspectives and Challenges. IEEE Tran Cybern 43(6):1977–1989

    Article  Google Scholar 

  44. Zhang ZY, Wang HG, Vasilakos AV, Fang H (2012) ECG-cryptography and authentication in body area networks. IEEE Trans Inf Technol Biomed 16(6):1070–1078

    Article  Google Scholar 

Download references

Acknowledgments

The work is supported in part by the National Natural Science Foundation of China Grant 61402380, U.S. National Science Foundation Grants CNS-1253506 (CAREER) and CNS-1250180, the Fundamental Research Funds for the Central Universities Grant XDJK2015B030, the State Ethnic Affairs Commission of China Grant 14GZZ012, and the Science and Technology Foundation of Guizhou Grant LH20147386.

Author information

Authors and Affiliations

  1. College of Computer and Information Sciences, Southwest University, Chongqing, 400715, China

    Yantao Li

  2. Department of Computer Science, College of William and Mary, Williamsburg, VA, 23187, USA

    Gang Zhou & Daniel Graham

  3. The MITRE Corporation, McLean, VA, USA

    Andrew Holtzhauer

Authors
  1. Yantao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Graham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew Holtzhauer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yantao Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Zhou, G., Graham, D. et al. Towards an EEG-based brain-computer interface for online robot control. Multimed Tools Appl 75, 7999–8017 (2016). https://doi.org/10.1007/s11042-015-2717-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2717-z

Keywords

Search

Navigation