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Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network

  • Abdessalam Kifouche
  • Vincent Vigneron
  • Mohammad B. Shamsollahi
  • Abderrezak Guessoum
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)

Abstract

Brain-machines - also termed neural prostheses, could potentially increase substantially the quality of life for people suffering from motor disorders or even brain palsy. In this paper we investigate the non-stationary continuous decoding problem associated to the rat’s hand position. To this aim, intracortical data (also named ECoG for electrocorticogram) are processed in successive stages: spike detection, spike sorting, and intention extraction from the firing rate signal.

The two important questions to answer in our experiment are (i) is it realistic to link time events from the primary motor cortex with some time-delay mapping tool and are some inputs more suitable for this mapping (ii) shall we consider separated channels or a special representation based on multidimensional statistics. We propose our own answers to these questions and demonstrate that a nonlinear representation might be appropriate in a number of situations.

Keywords

BMI Time Delay Neural Network nonlinear regression spikes 

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References

  1. 1.
    Brockwell, E., Rojas, L., Kass, R.E.: Recursive bayesian decoding of motor cortical signals by particle filtering. Journal of Neurophysiology 91(4), 1899–1907 (2004)CrossRefGoogle Scholar
  2. 2.
    Brown, E.N., et al.: A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. Journal of Neuroscience 18(18), 7411–7425 (1998)Google Scholar
  3. 3.
    Gage, G.J.: Co-adaptive kalman filtering in a naïve rat cortical control task. In: IEEE Conference on Engineering in Medicine and Biology Conference, vol. 6, pp. 4367–4370 (2004)Google Scholar
  4. 4.
    Ghanbari, A., et al.: Neural spike sorting with a self-training semi-supervised support vector machine. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan, pp. 6007–6010 (2013)Google Scholar
  5. 5.
    Hestenes, M.R., Stiefel, E.: Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standard 49(6) (1952)Google Scholar
  6. 6.
    Dayen, P., Abbott, L.F.: Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems. MIT Press (2001)Google Scholar
  7. 7.
    Sameni, R., Vrins, F., Parmentier, F., Herail, F., Vigneron, V., Verleysen, M., Jutten, C., Shamsollahi, M.B.: Electrode selection for noninvasive fetal electrocardiogram extraction using mutual information criteria. In: MaxEnt2006 Proceedings - 26th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 872, pp. 97–104 (July 2006)Google Scholar
  8. 8.
    Van Staveren, G.W., et al.: Wave shape classification of spontaneaous neural activity in cortical cultures on micro-electrode arrays. In: Proceedings 24th Annual Conference of the EMBS/BMES Society, TX, USA, October 23-26, vol. 3, pp. 2010–2011 (2002)Google Scholar
  9. 9.
    Vigneron, V., Chen, H., Chen, Y.-T., Lai, H.-Y., Chen, Y.-Y.: Decomposition of EEG signals for multichannel neural activity analysis in animal experiments. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds.) LVA/ICA 2010. LNCS, vol. 6365, pp. 474–481. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Vrins, F., Lee, J.A., Verleysen, M., Vigneron, V., Jutten, C.: Improving independent component analysis performances by variable selection. In: NNSP 2003 proceedings - Neural Networks for Signal Processing, Toulouse, France, pp. 359–368 (September 2003)Google Scholar
  11. 11.
    Vrins, F., Vigneron, V., Jutten, C., Verleysen, M.: Abdominal electrodes analysis by statistical processing for fetal electrocardiogram extraction. In: Proceedings of the 2nd International Conference Biomedical Engineering, Innsbruc, Austria, pp. 244–250 (2004)Google Scholar
  12. 12.
    Wu, W.: Inferring hand motion from multi-cell recordings in motor cortex using a kalman filter. In: Workshop on Motor Control in Humans and Robots: On the Interplay of Real Brains and Artifical Devices, pp. 1–8 (2002)Google Scholar
  13. 13.
    Wu, W., et al.: Closed-loop neural control of cursor motion using a kalman filter. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 6, pp. 4126–4129 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Abdessalam Kifouche
    • 1
    • 2
    • 3
  • Vincent Vigneron
    • 1
  • Mohammad B. Shamsollahi
    • 4
  • Abderrezak Guessoum
    • 1
  1. 1.LATSIUniversity of BlidaAlgeria
  2. 2.IBISCUniversity of EvryFrance
  3. 3.University of GhardaiaAlgeria
  4. 4.BiSIPLSharif University of TechnologyTehranIran

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