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Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb

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Abstract

A Brain–Computer Interface (BCI) is a device that transforms brain signals, which are intentionally modulated by a user, into control commands. BCIs based on motor imagery (MI) and steady-state visual evoked potentials (SSVEP) can partially restore motor control in spinal cord injured patients. To determine whether these BCIs can be combined for grasp and elbow function control independently, we investigated a control method where the beta rebound after brisk feet MI is used to control the grasp function, and a two-class SSVEP-BCI the elbow function of a 2 degrees-of-freedom artificial upper limb. Subjective preferences for the BCI control were assessed with a questionnaire. The results of the initial evaluation of the system suggests that this is feasible.

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References

  1. Allison BZ, Brunner C, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G (2010) Toward a hybrid brain-computer interface based on imagined movement and visual attention. J Neural Eng 7:026007

    Article  CAS  Google Scholar 

  2. Bin G, Gao X, Yan Z, Hong B, Gao S (2009) An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J Neural Eng 6:1–6

    Article  Google Scholar 

  3. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, Perelmouter J, Taub E, Flor H (1999) A spelling device for the paralysed. Nature 98:297–298

    Article  Google Scholar 

  4. Cheng M, Gao X, Gao S, Xu D (2002) Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Neural Syst Rehab Eng 49:1181–1186

    Google Scholar 

  5. Davison AC, Hinkley DV (1997) Bootstrap methods and their application. Cambridge University Press, London

  6. Donchin E, Spencer KM, Wijesinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Neural Syst Rehab Eng 8:174–179

    Article  CAS  Google Scholar 

  7. Gao X, Xu D, Cheng M, Gao S (2003) A BCI-based environmental controller for the motion-disabled. IEEE Trans Neural Syst Rehab Eng 11:137–140

    Article  Google Scholar 

  8. Graimann B, Huggins JE, Levine SP, Pfurtscheller G (2002) Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG datas. Clin Neurophysiol 113:43–47

    Article  PubMed  CAS  Google Scholar 

  9. Hjorth B (1975) An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroencephalogr Clin Neurophysiol 39:526–530

    Article  PubMed  CAS  Google Scholar 

  10. Horki P, Neuper C, Müller-Putz GR (2010) Asynchronous steady-state visual evoked potential based BCI: control of a 2 DoF artificial upper limb. Biomed Tech 55(6):367–374

    Article  Google Scholar 

  11. Hotelling H (1936) Relations between two sets of variates. Biometrika 28:321–377

    Google Scholar 

  12. Kübler A, Neumann N, Wilhelm B, Hinterberger T, Birbaumer N (2004) Predictability of brain-computer communication. J Psychophysiol 18(2):121–129

    Google Scholar 

  13. Lin Z, Zhang C, Wu W, Gao X (2007) Frequency recognition based on canonical correlation analysis for ssvep-based bcis. IEEE Trans Biomed Eng 54(6):1172–1176

    Article  PubMed  Google Scholar 

  14. Makeig S, Debener S, Onton J, Delorme A. (2004) Mining event-related brain dynamics. Trends Cogn Sci 8:204–210

    Article  PubMed  Google Scholar 

  15. McMillan GR, Calhoun GL, Middendorf MS, Schnurer JH, Ingle DF, Nasman VT (1995) Direct brain interface utilizing self-regulation of steady-state visual evoked response (SSVER). In: Proceedings of the RESNA 18th annual conference (RESNA)

  16. Middendorf M, McMillan G, Calhoun G, Jones KS (2000) Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehab Eng 8:211–214

    Article  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  18. Müller-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 2:1–8

    Article  Google Scholar 

  19. Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R (2005) EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci Lett 382:169–174

    Article  PubMed  Google Scholar 

  20. Müller-Putz GR, Eder E, Wriessnegger SC, Pfurtscheller G (2008) Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI. J Neurosci Meth, 168:174–181

    Article  Google Scholar 

  21. Müller-Putz GR, Kaiser V, Solis-Escalante T, Pfurtscheller G (2010) Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG. Med Biol Eng Comput

  22. Müller-Putz GR, Scherer R, Pfurtscheller G, Neuper C (2010) Temporal coding of brain patterns for direct limb control in humans. Front Neurosci/Neuroprost

  23. Ortner R, Allison BZ, Korisek G, Gaggl G, Pfurtscheller G (2011) An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia. IEEE Trans Neural Syst Rehab Eng, 19(1):1–5

    Article  Google Scholar 

  24. Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857

    Article  PubMed  CAS  Google Scholar 

  25. Pfurtscheller G, Solis-Escalante T (2009) Could the beta rebound in the EEG be suitable to realize a “brain switch”? Clin Neurophysiol 120:24–29

    Article  PubMed  CAS  Google Scholar 

  26. Pfurtscheller G, Neuper C, Guger C, Harkam W, Ramoser H, Schlögl A, Obermaier B, Pregenzer M (2000) Current trends in Graz brain-computer interface (BCI) research. IEEE Trans Rehab Eng 8:216–219

    Article  CAS  Google Scholar 

  27. Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Rupp R (2003) “Thought”-control of functional electrical stimulation to restore handgrasp in a patient with tetraplegia. Neurosci Lett 351:33–36

    Article  PubMed  CAS  Google Scholar 

  28. Pfurtscheller G, Allison BZ, Brunner C, Bauernfeind G, Solis-Escalante T, Scherer R, Zander TO, Müller-Putz G, Neuper C, Birbaumer N (2010) The hybrid BCI. Front Neurosci 4:30

    PubMed  Google Scholar 

  29. Pfurtscheller G, Solis-Escalante T, Ortner R, Linortner P, Müller-Putz GR (2010) Self-paced operation of an SSVEP-based orthosis with and without an imagery-based “brain switch”: a feasibility study towards a hybrid BCI. IEEE Trans Neural Syst Rehab Eng 18:409–414

    Article  Google Scholar 

  30. Pudil P, Novovicová J, Kittler J (1994) Floating search methods in feature selection. Patt Rec Lett 15(11):1119–1125

    Article  Google Scholar 

  31. Regan D (1989) Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, New York

    Google Scholar 

  32. Solis-Escalante T, Müller-Putz GR, Brunner C, Kaiser V, Pfurtscheller G (2010) Analysis of sensorimotor rhythms for the implementation of a brain switch for healthy subjects. Biomed Sig Proc Con 5:15–20

    Article  Google Scholar 

  33. Vilimek R, Zander TO (2009) Universal access in human-computer interaction. Intelligent and ubiquitous interaction environments, chapter BC (eye): combining eye-gaze input with brain-computer interaction. Springer, Berlin/Heidelberg, pp 593–602

  34. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791

    Article  PubMed  Google Scholar 

  35. Zhang D, Maye A, Gao X, Hong B, Engel AK, Gao S (2010) An independent brain-computer interface using covert non-spatial visual selective attention. J Neural Eng 7:016010

    Article  Google Scholar 

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Acknowledgments

This work was supported by EU COST Action BM0601 (Neuromath) and Wings for Life - Spinal Cord Research Foundation (WFL-SE-016/09). We are indebted to V. Kaiser and L. Deuse for assistance in data recording.

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Authors and Affiliations

  1. Laboratory of Brain–Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, Graz, Austria

    Petar Horki, Teodoro Solis-Escalante, Christa Neuper & Gernot Müller-Putz

Authors
  1. Petar Horki
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  2. Teodoro Solis-Escalante
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  3. Christa Neuper
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  4. Gernot Müller-Putz
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Correspondence to Petar Horki.

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Horki, P., Solis-Escalante, T., Neuper, C. et al. Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb. Med Biol Eng Comput 49, 567–577 (2011). https://doi.org/10.1007/s11517-011-0750-2

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  • DOI: https://doi.org/10.1007/s11517-011-0750-2

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