Skip to main content
Log in

EEG pattern decoding of rhythmic individual finger imaginary movements of one hand

  • Published:
Human Physiology Aims and scope Submit manuscript

Abstract

The results of four-class classification of the motor imagery EEG patterns corresponding to the right hand finger movements (little finger, thumb, index and middle fingers) of eight healthy subjects are presented in this study. The motor imagery of individual right-hand finger movements was executed by the subjects in a prescribed rhythm and the trials contained no external stimuli. Classification was performed by means of a specially developed two-level committee of classifiers on the basis of support vector machine and artificial neural networks at the first level and by generalizing an artificial neural network at the second level. The area under the EEG signal curve and the curve length calculated in a sliding time window for sites F 3, C 3, and Cz of the International 10?20 System were selected as the key features of signals from the sensorimotor and adjoining frontal cortical areas contralateral to the movements. The average accuracy of four-class singletrial classification for all subjects was 50 ± 7 [SD] (maximum, 58%) for the pair of sites F 3C 3 and 46 ± 11% [SD] (maximum 62%) for the pair of sites C 3Cz with a theoretical guessing level 25%.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wolpaw, J.R. and Wolpaw, E.W., Brain-Computer Interfaces: Principles and Practice, New York: Oxford Univ. Press, 2012.

    Book  Google Scholar 

  2. Blankertz, B., Tangermann, M., Vidaurre, C., et al., The berlin brain-computer interface: Non-medical uses of BCI technology, Front. Neurosci., 2010, vol. 4, art. 198. doi 10.3389/fnins.2010.00198.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Brunner, C., Birbaumer, N., Blankertz, B., et al., BNCI Horizon 2020: Towards a roadmap for the BCI community, Brain-Comput. Interfaces, 2015, vol. 2, no. 1. doi 10.1080/2326263X.2015.1008956.

    Google Scholar 

  4. Frolov, A.A., Biryukova, E.V., Bobrov, P.D., et al., Principles of neurorehabilitation based on the brain–computer interface and biologically adequate control of the exoskeleton, Hum. Physiol., 2013, vol. 39, no. 2, p. 196.

    Article  Google Scholar 

  5. Kaplan, A.Ya., Kochetova, A.G., Shishkin, S.L., et al., Experimental and theoretical foundations and practical implementation of the brain–computer interface technology, Byull. Sib. Med., 2013, vol. 12, no. 2, p. 21.

    Google Scholar 

  6. Schwartz, D.L. and Black, T., Inferences through imagined actions. Knowing by simulated doing, J. Exp. Psychol., 1999, vol. 25, no. 1, p. 116.

    Google Scholar 

  7. 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, vol. 20, no. 5, p. 720.

    Article  PubMed  Google Scholar 

  8. Middendorf, M., McMillan, G., Calhoun, G., and Jones, K.S., Brain-computer interfaces based on the steady-state visual-evoked response, IEEE Trans. Rehabil. Eng., 2000, vol. 8, no. 2, p. 211.

    Article  CAS  PubMed  Google Scholar 

  9. Ang, K.K., Guan, C., Chua, K.S., et al., Large clinical study on the ability of stroke patients in using EEGbased motor imagery brain-computer interface, Clin. EEG Neurosci., 2011, vol. 42, pp. 253.

    Article  PubMed  Google Scholar 

  10. de Charms, R.C., Maeda, F., Glover, G.H., et al., Control over brain activation and pain learned by using realtime functional MRI, Proc. Natl. Acad. Sci. U.S.A., 2005, vol. 102, no. 51, p. 18626.

    Article  Google Scholar 

  11. Sterman, M.B., Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning, Clin. Electroencephalogr., 2000, vol. 31, no. 1, p. 45.

    Article  CAS  PubMed  Google Scholar 

  12. Bekinschtein, T.A., Manes, F.F., Villarreal, M., et al., Functional imaging reveals movement preparatory activity in the vegetative state, Front. Hum. Neurosci., 2011, vol. 5, art. 5. doi 10.3389/fnhum.2011.00005

    Article  PubMed  PubMed Central  Google Scholar 

  13. Doud, A.J., Lucas, J.P., Pisansky, M.T., and He, B., Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface, PLoS One, 2011, vol. 6, no. 10. doi 10.1371/journal.pone.0026322

    Google Scholar 

  14. Morash, V., Bai, O., Furlani, S., et al., Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries, Clin. Neurophysiol., 2008, vol. 119, no. 11, p. 2570.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Shenoy, P., Miller, K., Ojemann, J., and Rao, R., Finger movement classification for an electrocorticographic BCI, IEEE/EMBS Neural Eng. Conf., 2007, p. 192. doi 10.1109/CNE.2007.369644

    Google Scholar 

  16. Wang, V., Degenhart, A.D., Collinger, J.L., et al., Human motor cortical activity recorded with microECoG electrodes during individual finger movements, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2009, p. 586. doi 10.1109/IEMBS.2009.5333704

    Google Scholar 

  17. Onaran, I., Ince, N.F., and Cetin, A.E., Classification of multichannel ECoG related to individual finger movements with redundant spatial projections, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2011, p. 5424. doi 10.1109/IEMBS.2011.6091341

    Google Scholar 

  18. Wissel, T., Pfeiffer, T., Frysch, R., et al., Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography, J. Neural Eng., 2013, vol. 10, no. 5, art. 056020. doi 10.1088/1741-2560/10/5/056020

    Article  PubMed  PubMed Central  Google Scholar 

  19. Xiao, R. and Ding, L., Evaluation of EEG features in decoding individual finger movements from one hand, Comput. Math. Methods Med., 2013, art. 243257, p. 243.

    Google Scholar 

  20. Quandt, F., Reichert, C., Hinrichs, H., et al., Single trial discrimination of individual finger movements on one hand: a combined MEG and EEG study, Neuroimage, 2012, vol. 59, no. 4, p. 3316.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sonkin, K.M., Stankevich, L.A., Khomenko, Yu.G., et al., Classification of electroencephalographic patterns of imagined and real movements by one hand fingers using the support vectors method, Tikhookean. Med. Zh., 2014, vol. 2, pp. 30.

    Google Scholar 

  22. Sonkin, K.M., Stankevich, L.A., Khomenko, J.G., et al., Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand, Artif. Intell. Med., 2015, vol. 63, no. 2, p. 107.

    Article  PubMed  Google Scholar 

  23. Porro, C.A., Francescato, M.P., Cettolo, V., et al., Primary motor and sensory cortex activation during motor performance and motor imagery. A functional magnetic resonance study, J. Neurosci., 1996, vol. 16, no. 23, p. 7688.

    CAS  PubMed  Google Scholar 

  24. Porro, C.A., Cettolo, V., Francescato, M.P., and Baraldi, P., Ipsilateral involvement of primary motor cortex during motor imagery, Eur. J. Neurosci., 2000, vol. 12, no. 8, p. 3059.

    Article  CAS  PubMed  Google Scholar 

  25. Roth, M., Decety, J., Raybaudi, M., et al., Possible involvement of primary motor cortex in mentally simulated movement. A functional magnetic resonance imaging study, NeuroReport, 1996, vol. 17, no. 7, p. 1280.

    Article  Google Scholar 

  26. Pfurtscheller, G. and Neuper, C., Motor imagery activates primary sensorimotor area in humans, Neurosci. Lett., 1997, vol. 239, pp. 65.

    Article  CAS  PubMed  Google Scholar 

  27. Leocani, L., Toro, C., Manganotti, P., et al., Eventrelated coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements, EEG Clin. Neurophysiol., 1997, vol. 104, no. 3, p. 199.

    CAS  Google Scholar 

  28. Neuper, C., Scherer, R., Reiner, M., and Pfurtscheller, G., Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG, Cognit. Brain Res., 2005, vol. 25, no. 3, p. 668.

    Article  Google Scholar 

  29. Decety, J. and Michel, F., Comparative analysis of actual and mental movement times in two graphic tasks, Brain Cognit., 1989, vol. 11, pp. 87.

    Article  CAS  Google Scholar 

  30. Sirigu, A., Duhamel, J.R., Cohen, L., et al., The mental representation of hand movements after parietal cortex damage, Science, 1996, vol. 273, no. 5281, p. 1564.

    Article  CAS  PubMed  Google Scholar 

  31. Lemos, M.S. and Fisch, B.J., The weighted average reference montage, EEG Clin. Neurophysiol., 1991, vol. 79, no. 5, p. 361.

    Article  CAS  Google Scholar 

  32. Perrin, F., Pernier, J., Bertrand, O., and Echallier, J.F., Spherical splines for scalp potential and current density mapping Corrigenda EEG 02274, EEG Clin. Neurophysiol. 1990. vol. 76. p. 565, EEG Clin. Neurophysiol., 1989, vol. 72, no. 2, p. 184.

    Article  CAS  Google Scholar 

  33. Tenke, C.E. and Kayser, J., Generator localization by current source density (CSD): implications of volume conduction and field closure at intracranial and scalp resolutions, Clin. Neurophysiol., 2012, vol. 123, no. 12, p. 2328.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kayser, J. and Tenke, C., Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: a tutorial review, Int. J. Psychophysiol., 2015, vol. 97, no. 3, p. 189.

    Article  PubMed  Google Scholar 

  35. Ponomarev, V.A., Mueller, A., Candrian, G., et al., Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults, Clin. Neurophysiol., 2014, vol. 125, no. 1, p. 83.

    Article  PubMed  Google Scholar 

  36. Herwig, U., Satrapi, P., and Schönfeldt-Lecuona, C., Broadmann area using the international 10-20 EEG system for positioning of transcranial magnetic stimulation, Brain Topogr., 2003, vol. 16, no. 2, p. 95.

    Article  PubMed  Google Scholar 

  37. Choi, K., Electroencephalography (EEG) based neurofeedback training for brain-computer interface (BCI), Exp. Brain Res., 2013, vol. 231, no. 3, p. 351.

    Article  PubMed  Google Scholar 

  38. Ivanova, M.P., Korkovye mekhanizmy proizvol’nykh dvizhenii u cheloveka (Cortical Mechanisms of Human Voluntary Movement), Moscow: Nauka, 1991.

    Google Scholar 

  39. Tallon-Baudry, C. and Bertrand, O., Oscillatory gamma activity in humans and its role in object representation, Trends Cognit. Sci., 1999, vol. 3, no. 4, p. 151.

    Article  Google Scholar 

  40. Lotte, F., Congedo, M., Lecuyer, A., et al., Review of classification algorithms for EEG-based brain-computer interfaces, J. Neural Eng., 2007, vol. 4, no. 2, p. 1.

    Article  Google Scholar 

  41. Cortes, C. and Vapnik, V.N., Support-vector networks, Mach. Learn., 1995, vol. 20, no. 3, p. 273.

    Google Scholar 

  42. Shawe-Taylor, J. and Cristianini, N., Kernell Methods for Pattern Analysis, Cambridge Univ. Press, 2004.

    Google Scholar 

  43. Chang, C.C. and Lin, C.J., LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol., 2011, vol. 2, no. 3, art. 27. doi 10.1145/1961189. 1961199

    Google Scholar 

  44. Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Stat., 1979, vol. 7, no. 1, p. 1.

    Article  Google Scholar 

  45. Parsons, L.M., Integrating cognitive psychology, neurology and neuroimaging, Acta Psychol., 2001, vol. 107, nos. 1–3, p. 155.

    Article  CAS  Google Scholar 

  46. Pfurtscheller, G. and Lopes da Silva, F.H., Eventrelated EEG/EMG synchronization and desynchronization. Basic principles, Clin. Neurophysiol., 1999, vol. 110, no. 11, p. 1842.

    Article  CAS  PubMed  Google Scholar 

  47. Kuo, C.C., Luu, P., Morgan, K.K., et al., Localizing movement-related primary sensorimotor cortices with multi-band EEG frequency changes and functional MRI, PLoS One, 2014, vol. 9, no. 11. doi 10.1371/journal.pone.0112103

    Google Scholar 

  48. Müller-Putz, G.R., Scherer, R., Brunner, C., et al., Better than random? A closer look on BCI results, Int. J. Bioelectromagnet., 2008, vol. 10, no. 1, p. 52.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. A. Stankevich.

Additional information

Original Russian Text © L.A. Stankevich, K.M. Sonkin, N.V. Shemyakina, Zh.V. Nagornova, J.G. Khomenko, D.S. Perets, A.V. Koval, 2016, published in Fiziologiya Cheloveka, 2016, Vol. 42, No. 1, pp. 40–51.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stankevich, L.A., Sonkin, K.M., Shemyakina, N.V. et al. EEG pattern decoding of rhythmic individual finger imaginary movements of one hand. Hum Physiol 42, 32–42 (2016). https://doi.org/10.1134/S0362119716010175

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0362119716010175

Keywords

Navigation