The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

Neuronal Spike Train Decoding Methods for the Brain-Machine Interface Using Nonlinear Mapping

비선형매핑 기반 뇌-기계 인터페이스를 위한 신경신호 spike train 디코딩 방법

  • Published : 2005.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Brain-machine interface (BMI) based on neuronal spike trains is regarded as one of the most promising means to restore basic body functions of severely paralyzed patients. The spike train decoding algorithm, which extracts underlying information of neuronal signals, is essential for the BMI. Previous studies report that a linear filter is effective for this purpose and there is no noteworthy gain from the use of nonlinear mapping algorithms, in spite of the fact that neuronal encoding process is obviously nonlinear. We designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR), and show that the nonlinear algorithms are superior in general. The MLP often showed unsatisfactory performance especially when it is carelessly trained. The nonlinear SVR showed the highest performance. This may be due to the superiority of the SVR in training and generalization. The advantage of using nonlinear algorithms were more profound for the cases when there are false-positive/negative errors in spike trains.

Keywords

References

  1. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, T. M. Vaughan, 'Brain-computer interfaces for communication and control', Clin. Neurophys., Vol. 113, pp. 767-791, 2002 https://doi.org/10.1016/S1388-2457(02)00057-3
  2. J. D. Donoghue, 'Connecting cortex to machines: recent advances in brain interfaces', Nature Neurosci. ence, Vol. 5, pp. 1085-1088, 2002 https://doi.org/10.1038/nn947
  3. M. Mojarradi, D. Binkley, B. Blalock, R. Andersen, N. Ulshoefer, T. Johnson, L. D. Castillo, 'A miniaturized neuroprosthesis suitable for implantation into the brain', IEEE Trans. Neural Sys. Rehab. Eng., Vol. 11, pp. 38-42, 2003 https://doi.org/10.1109/TNSRE.2003.810431
  4. M. A. L. Nicolelis, 'Actions from thoughts', Nature, Vol. 409, pp. 403-407, 2001 https://doi.org/10.1038/35053191
  5. J. Wessberg, C. R. Stambaugh, J. D. Kralik, P. D. Beck, M. Laubach, J. K. Chapin, J. Kim, S. J. Biggs, M. A. Srinivasan, M. A. L. Nicolelis, 'Real-time prediction of hand trajectory by ensembles of cortical neurons in primates', Nature, Vol. 408, pp. 361-365, 2000 https://doi.org/10.1038/35042582
  6. K. A. Moxon, N. M. Kalkhoran, M. Markert, M. A. Sambito, J. L. McKenzie, J. T. Webster, 'Nanostructured surface modification of ceramic-based microelectrodes to enhance biocompatibility for a direct brain-machine interface', IEEE Trans. Biomed. Eng., Vol. 51, pp. 881-889, 2004 https://doi.org/10.1109/TBME.2004.827465
  7. M. Serruya, N. Hatsopoulos, M. Fellows, L. Paninski, J. Donoghue, 'Robustness of neuroprosthetic decoding algorithms', Biol. Cybern., Vol. 88, pp. 219-228, 2003 https://doi.org/10.1007/s00422-002-0374-6
  8. J. Wessberg, M. A. L. Nicolelis, 'Optimizing a linear algorithm for real-time robotic control using choronic cortical ensemble recordings in monkeys', J. Cogn. Neurosci., Vol. 16, pp. 1022-1035, 2004 https://doi.org/10.1162/0898929041502652
  9. D. K. Warland, P. Reinagel, M. Meister, 'Decoding visual information from a population of retinal ganglion cells', J. Neurophysiol., Vol. 78, pp. 2336-2350, 1997
  10. M. Pardo, G. Sberveglieri, 'Remarks on the use of multilayer perceptrons for the analysis of chemical sensor array data', IEEE Sensors J., Vol. 4, pp. 355-363, 2004 https://doi.org/10.1109/JSEN.2004.827207
  11. S. Haykin, Neural Networks: A comprehensive foundation, New Jersey: Prentice Hall, 1999
  12. S. Amari, N. Murata, K. R. Muller, M. Finke, H. Yang, 'Statistical theory of overtraining', Adv. Neural. Inf. Proc. Sys., Vol. 8, pp. 176-182, 1996
  13. A. Smola, B. Schlkopf, 'A tutorial on support vector regression', Statistics and Computing, Vol. 14, pp. 199-222, 2004 https://doi.org/10.1023/B:STCO.0000035301.49549.88
  14. L. Hoegaerts, J. A. K. Suykens, J. Vandewalle, B. De Moor, 'Subset based least squares subspace regression in RKHS', Neurocomputing, Vol. 63, pp. 293-323, 2005 https://doi.org/10.1016/j.neucom.2004.04.013
  15. A. P. Georgopoulos, A. B. Schwartz, R. E. Kettner, 'Neuronal Population Coding of Movement Direction', Science , Vol. 233, pp. 1416-1419, 1986 https://doi.org/10.1126/science.3749885
  16. C. Koch, Biophysics of Computation, New York: Oxford University Press, 1999
  17. C. Kernere, K. V. Shenoy, T. H. Meng, 'Model-based neural decoding of reaching movements: a maximum likelihood approach', IEEE Trans. Biomed. Eng., Vol. 51, pp. 925-932, 2004 https://doi.org/10.1109/TBME.2004.826675
  18. M. Serruya, N. Hatsopoulos, M. Fellows, L. Paninski, J. Donoghue, 'Robustness of neuroprosthetic decoding algorithms', Biol. Cybern., Vol. 88, pp. 219-228, 2003 https://doi.org/10.1007/s00422-002-0374-6
  19. D. S. Won, P. D. Wolf, 'A simulation study of information transmission by multi -unit microelectrode recordings', Network: Comput. Neural. Syst., Vol. 15, pp. 29-44, 2004 https://doi.org/10.1088/0954-898X/15/1/003
  20. K. H. Kim, S. J. Kim, 'A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio', IEEE Trans. Biomed. Eng., Vol. 50, pp. 999-1011, 2003 https://doi.org/10.1109/TBME.2003.814523
  21. K. H. Kim, S. J. Kim, 'Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio', IEEE Trans. Biomed. Eng., Vol. 50, pp. 421-431, 2003 https://doi.org/10.1109/TBME.2003.809503
  22. Y. Ben-Shaul, E. Stark, I. Asher, R. Drori, Z. Nadasdy, M. Abeles, 'Dynamical organization of directional tuning in the primate premotor and primary motor cortex', J. Neurophysiol., Vol. 89, pp. 1136-1142, 2003 https://doi.org/10.1152/jn.00364.2002