Skip to main content
SpringerLink
Log in

A Tactile-based Brain Computer Interface P300 Paradigm Using Vibration Frequency and Spatial Location

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Purpose

Visual based brain-computer interface (BCI) has been widely investigated for the severely disabled patients. However, this modality is not applicable to the persons who have lost their visual function. This study aims to develop an alternative BCI using tactile stimuli only.

Methods

An event-related potential (ERP) based BCI applying vibrotactile stimuli was proposed. It featured a combination of frequency and spatial information as a cue for binary choices. Two identical mechanical vibrotactile tactors were tied to each index finger of the two hands of a subject, to provide vibrotactile stimuli at various frequencies and constitute a three-stimulus oddball paradigm with one attended stimulus, one ignored stimulus and one disturbance stimulus. Ten healthy subjects participated in the experiments, and the classification of P300 signals was conducted by a stepwise linear discriminate analysis algorithm.

Results

Significant P300 components were evoked at approximately 360 ms after the onset of the target stimuli for each subject, and an average classification accuracy of 79% was achieved.

Conclusions

This work demonstrates the feasibility of employing vibrotactile stimuli to build an ERP based BCI. The patients without a functional visual system could benefit from the presented paradigm.

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

Similar content being viewed by others

References

  1. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791. https://doi.org/10.1016/S1388-2457(02)00057-3.

    Article  Google Scholar 

  2. Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography & Clinical Neurophysiology, 70(6), 510–523. https://doi.org/10.1016/0013-4694(88)90149-6.

    Article  Google Scholar 

  3. Schreuder, M., Blankertz, B., & Tangermann, M. (2010). A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS ONE, 5(4), e9813. https://doi.org/10.1371/journal.pone.0009813.

    Article  Google Scholar 

  4. Brunner, P., Joshi, S., Briskin, S., Wolpaw, J. R., Bischof, H., & Schalk, G. (2010). Does the 'P300' speller depend on eye gaze? Journal of Neural Engineering, 7(5), 056013. https://doi.org/10.1088/1741-2560/7/5/056013.

    Article  Google Scholar 

  5. Lewis, M., & Rushanan, S. (2007). The role of physical therapy and occupational therapy in the treatment of amyotrophic lateral sclerosis. NeuroRehabilitation, 22(6), 451–461.

    Article  Google Scholar 

  6. Murguialday, A. R., Hill, J., Bensch, M., Martens, S., Halder, S., Nijboer, F., et al. (2011). Transition from the locked in to the completely locked-in state: a physiological analysis. Clinical Neurophysiology, 122(5), 925–933. https://doi.org/10.1016/j.clinph.2010.08.019.

    Article  Google Scholar 

  7. Müller-Putz, G. R., Scherer, R., Neuper, C., & Pfurtscheller, G. (2006). Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(1), 30–37. https://doi.org/10.1109/tnsre.2005.863842.

    Article  Google Scholar 

  8. Brouwer, A.-M., & van Erp, J. B. F. (2010). A tactile P300 brain-computer interface. Frontiers in Neuroscience, 4, 19. https://doi.org/10.3389/fnins.2010.00019.

    Article  Google Scholar 

  9. Severens, M., Van der Waal, M., Farquhar, J., & Desain, P. (2014). Comparing tactile and visual gaze-independent brain-computer interfaces in patients with amyotrophic lateral sclerosis and healthy users. Clinical Neurophysiology, 125(11), 2297–2304. https://doi.org/10.1016/j.clinph.2014.03.005.

    Article  Google Scholar 

  10. van der Waal, M., Severens, M., Geuze, J., & Desain, P. (2012). Introducing the tactile speller: an ERP-based brain-computer interface for communication. Journal of Neural Engineering, 9(4), 045002. https://doi.org/10.1088/1741-2560/9/4/045002.

    Article  Google Scholar 

  11. Guger, C., Spataro, R., Allison, B. Z., Heilinger, A., Ortner, R., Cho, W., et al. (2017). Complete Locked-in and Locked-in Patients: Command Following Assessment and Communication with Vibro-Tactile P300 and Motor Imagery Brain-Computer Interface Tools. Frontiers in neuroscience, 11, 251. https://doi.org/10.3389/fnins.2017.00251.

    Article  Google Scholar 

  12. Herweg, A., Gutzeit, J., Kleih, S., & Kübler, A. (2016). Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biological psychology, 121(Pt A), 117–124. https://doi.org/10.1016/j.biopsycho.2016.10.006.

    Article  Google Scholar 

  13. Kaufmann, T., Herweg, A., & Kübler, A. (2014). Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials. Journal of neuroengineering and rehabilitation, 11, 7. https://doi.org/10.1186/1743-0003-11-7.

    Article  Google Scholar 

  14. Kaufmann, T., Holz, E. M., & Kübler, A. (2013). Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state. Frontiers in neuroscience, 7, 129. https://doi.org/10.3389/fnins.2013.00129.

    Article  Google Scholar 

  15. Lugo, Z. R., Rodriguez, J., Lechner, A., Ortner, R., Gantner, I. S., Laureys, S., et al. (2014). A vibrotactile p300-based brain-computer interface for consciousness detection and communication. Clinical EEG and neuroscience, 45(1), 14–21. https://doi.org/10.1177/1550059413505533.

    Article  Google Scholar 

  16. Hohne, J., Schreuder, M., Blankertz, B., & Tangermann, M. (2011). A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System. Frontiers in neuroscience, 5, 99. https://doi.org/10.3389/fnins.2011.00099.

    Article  Google Scholar 

  17. Halder, S., Takano, K., Ora, H., Onishi, A., Utsumi, K., & Kansaku, K. (2016). An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary. Frontiers in neuroscience, 10, 446. https://doi.org/10.3389/fnins.2016.00446.

    Article  Google Scholar 

  18. Silvoni, S., Konicar, L., Prats-Sedano, M. A., Garcia-Cossio, E., Genna, C., Volpato, C., et al. (2016). Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface. Clinical Neurophysiology, 127(1), 936–945. https://doi.org/10.1016/j.clinph.2015.06.029.

    Article  Google Scholar 

  19. Choi, I., Bond, K., Krusiensk, D., & Nam, C. S. (2015). Comparison of Stimulation Patterns to Elicit Steady-State Somatosensory Evoked Potentials (SSSEPs): Implications for Hybrid and SSSEP-Based BCIs. In 2015 EEE International Conference on Systems, Man, and Cybernetics, 9–12, 3122–3127. https://doi.org/10.1109/SMC.2015.542.

    Article  Google Scholar 

  20. Halder, S., Rea, M., Andreoni, R., Nijboer, F., Hammer, E. M., Kleih, S. C., et al. (2010). An auditory oddball brain-computer interface for binary choices. Clinical Neurophysiology, 121(4), 516–523. https://doi.org/10.1016/j.clinph.2009.11.087.

    Article  Google Scholar 

  21. Committee, E. P. N. (1991). American Electroencephalographic Society guidelines for standard electrode position nomenclature. Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society, 8(2), 200–202.

    Article  Google Scholar 

  22. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009.

    Article  Google Scholar 

  23. Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., et al. (2006). A comparison of classification techniques for the P300 Speller. Journal of Neural Engineering, 3(4), 299–305. https://doi.org/10.1088/1741-2560/3/4/007.

    Article  Google Scholar 

  24. Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008). Toward enhanced P300 speller performance. Journal of neuroscience methods, 167(1), 15–21. https://doi.org/10.1016/j.jneumeth.2007.07.017.

    Article  Google Scholar 

  25. Serby, H., Yom-Tov, E., & Inbar, G. F. (2005). An improved P300-based brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(1), 89–98. https://doi.org/10.1109/TNSRE.2004.841878.

    Article  Google Scholar 

  26. Cavrini, F., Bianchi, L., Quitadamo, L. R., & Saggio, G. (2016). A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface. Comput Intell Neurosci, 2016, 1–9. https://doi.org/10.1155/2016/9845980.

    Article  Google Scholar 

  27. Pires, G., & Nunes, U. (2009). A Brain Computer Interface methodology based on a visual P300 paradigm. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 10–15, 4193–4198. https://doi.org/10.1109/IROS.2009.5354384.

    Article  Google Scholar 

  28. Nakajima, Y., & Imamura, N. (2000). Relationships between attention effects and intensity effects on the cognitive N140 and P300 components of somatosensory ERPs. Clinical Neurophysiology, 111(10), 1711–1718. https://doi.org/10.1016/s1388-2457(00)00383-7.

    Article  Google Scholar 

  29. Pang, C. Y., & Mueller, M. M. (2014). Test–retest reliability of concurrently recorded steady-state and somatosensory evoked potentials in somatosensory sustained spatial attention. Biological psychology, 100, 86–96. https://doi.org/10.1016/j.biopsycho.2014.05.009.

    Article  Google Scholar 

  30. Reuter, E.-M., Voelcker-Rehage, C., Vieluf, S., Winneke, A. H., & Godde, B. (2014). Extensive occupational finger use delays age effects in tactile perception-an ERP study. Attention, perception & psychophysics, 76(4), 1160–1175. https://doi.org/10.3758/s13414-014-0634-2.

    Article  Google Scholar 

  31. Hori, J., & Okada, N. (2017). Classification of tactile event-related potential elicited by Braille display for brain–computer interface. Biocybernetics and Biomedical Engineering, 37(1), 135–142. https://doi.org/10.1016/j.bbe.2016.10.007.

    Article  Google Scholar 

  32. Breitwieser, C., Pokorny, C., & Muller-Putz, G. R. (2016). A hybrid three-class brain-computer interface system utilizing SSSEPs and transient ERPs. Journal of Neural Engineering, 13(6), 066015. https://doi.org/10.1088/1741-2560/13/6/066015.

    Article  Google Scholar 

  33. Kübler, A., & Birbaumer, N. (2008). Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? Clinical Neurophysiology, 119(11), 2658–2666. https://doi.org/10.1016/j.clinph.2008.06.019.

    Article  Google Scholar 

  34. Kaufmann, T., & Kübler, A. (2014). Beyond maximum speed–a novel two-stimulus paradigm for brain-computer interfaces based on event-related potentials (P300-BCI). Journal of Neural Engineering, 11(5), 056004. https://doi.org/10.1088/1741-2560/11/5/056004.

    Article  Google Scholar 

  35. Gonsalvez, C. J., Barry, R. J., Rushby, J. A., & Polich, J. (2007). Target-to-target interval, intensity, and P300 from an auditory single-stimulus task. Psychophysiology, 44(2), 245–250. https://doi.org/10.1111/j.1469-8986.2007.00495.x.

    Article  Google Scholar 

  36. Polich, J. (2007). Updating P300: an integrative theory of P3a and P3b. Clinical Neurophysiology, 118(10), 2128–2148. https://doi.org/10.1016/j.clinph.2007.04.019.

    Article  Google Scholar 

  37. Brouwer, A.-M., van Erp, J. B. F., Aloise, F., & Cincotti, F. (2010). Tactile, Visual, and Bimodal P300s: Could Bimodal P300s Boost BCI Performance? SRX Neuroscience, 2010, 1–9. https://doi.org/10.3814/2010/967027.

    Article  Google Scholar 

  38. Kayser, C., Petkov, C. I., Augath, M., & Logothetis, N. K. (2005). Integration of touch and sound in auditory cortex. Neuron, 48(2), 373–384. https://doi.org/10.1016/j.neuron.2005.09.018.

    Article  Google Scholar 

  39. Touge, T., Gonzalez, D., Wu, J., Deguchi, K., Tsukaguchi, M., Shimamura, M., et al. (2008). The interaction between somatosensory and auditory cognitive processing assessed with event-related potentials. Journal of clinical neurophysiolog, 25(2), 90–97. https://doi.org/10.1097/WNP.0b013e31816a8ffa.

    Article  Google Scholar 

  40. Yin, E., Zeyl, T., Saab, R., Hu, D., Zhou, Z., & Chau, T. (2016). An Auditory-Tactile Visual Saccade-Independent P300 Brain-Computer Interface. International journal of neural systems, 26(1), 1650001. https://doi.org/10.1142/s0129065716500015.

    Article  Google Scholar 

  41. Sellers, E. W., & Donchin, E. (2006). A P300-based brain-computer interface: initial tests by ALS patients. Clinical Neurophysiology, 117(3), 538–548. https://doi.org/10.1016/j.clinph.2005.06.027.

    Article  Google Scholar 

  42. Vučković, A., Wallace, L., & Allan, D. B. (2015). Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. Journal of neurologic physical therapy, 39(1), 3–14. https://doi.org/10.1097/npt.0000000000000063.

    Article  Google Scholar 

  43. Yi, W., Qiu, S., Wang, K., Qi, H., Zhao, X., He, F., et al. (2017). Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP. Journal of Neural Engineering, 14(2), 026002. https://doi.org/10.1088/1741-2552/aa5559.

    Article  Google Scholar 

  44. Simon, N., Kathner, I., Ruf, C. A., Pasqualotto, E., Kubler, A., & Halder, S. (2014). An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user. Front Hum Neurosci, 8, 1039. https://doi.org/10.3389/fnhum.2014.01039.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key Research and Development Program of China (2016YFE0128700).

Author information

Authors and Affiliations

  1. The State Key Laboratory of Reliability and Intelligence of Electrical Equipment and the Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, China

    Xiangke Han, Jianye Niu & Shijie Guo

  2. School of Mechanical Engineering, Anyang Institute of Technology, Anyang, China

    Xiangke Han

Authors
  1. Xiangke Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianye Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shijie Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shijie Guo.

Ethics declarations

Conflicts of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, X., Niu, J. & Guo, S. A Tactile-based Brain Computer Interface P300 Paradigm Using Vibration Frequency and Spatial Location. J. Med. Biol. Eng. 40, 773–782 (2020). https://doi.org/10.1007/s40846-020-00535-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-020-00535-6

Keywords

Search

Navigation