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International Conference on Latent Variable Analysis and Signal Separation

LVA/ICA 2017: Latent Variable Analysis and Signal Separation pp 417–426Cite as

Kernel-Based NPLS for Continuous Trajectory Decoding from ECoG Data for BCI Applications

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10169))

Abstract

In this paper, nonlinearity is introduced to linear neural activity decoders to improve continuous hand trajectory prediction for Brain-Computer Interface systems. For decoding the high-dimensional data-tensor, a kernel regression was coupled with multilinear PLS (NPLS). Two ways to introduce nonlinearity were studied: a generalized linear model with kernel link function and kernel regression in the NPLS latent variables space (inside or outside the NPLS iterations). The efficiency of these approaches was tested on the publically available database of the simultaneous recordings of three-dimensional hand trajectories and epidural electrocorticogram (ECoG) signals of a Japanese macaque. Compared to linear methods, nonlinearity did not significantly improve the prediction accuracy but did significantly improve the smoothness of the prediction.

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References

  1. Donoghue, J.P.: Bridging the brain to the world: a perspective on neural interface systems. Neuron 60, 511–521 (2008)

    Article  Google Scholar 

  2. Daly, J.J., Wolpaw, J.R.: Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 7, 1032–1043 (2008)

    Article  Google Scholar 

  3. Wang, W., Collinger, J.L., Degenhart, A.D., Tyler-Kabara, E.C., Schwartz, A.B., Moran, D.W., Weber, D.J., Wodlinger, B., Vinjamuri, R.K., Ashmore, R.C., Kelly, J.W., Boninger, M.L.: An electrocorticographic brain interface in an individual with tetraplegia. PLoS ONE 8, e55344 (2013)

    Article  Google Scholar 

  4. Mestais, C., Charvet, G., Sauter-Starace, F., Foerster, M., Ratel, D., Benabid, A.L.: WIMAGINE®: wireless 64-channel ECoG recording implant for long term clinical applications. IEEE TNSRE 23, (2015)

    Google Scholar 

  5. Müller-Putz, G.R., Kaiser, V., Solis-Escalante, T., Pfurtscheller, G.: Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG. Med. Biol. Eng. Comput. 48, 229–233 (2010)

    Article  Google Scholar 

  6. Bro, R.: Multiway calidration. Multilinear PLS. J. Chemom. 10, 47–61 (1996)

    Article  Google Scholar 

  7. Zhao, Q., Zhou, G., Adali, T., Zhang, L., Cichocki, A.: Kernelization of tensor-based models for multiway data analysis: Processing of multidimensional structured data. IEEE Signal Process. Mag. 30, 137–148 (2013)

    Article  Google Scholar 

  8. Eliseyev, A., Aksenova, T.: Stable and artifact-resistant decoding of 3D hand trajectories from ECoG signals using the generalized additive model. J. Neural Eng. 11, 066005 (2014)

    Article  Google Scholar 

  9. Eliseyev, A., Aksenova, T.: Recursive N-way partial least squares for brain-computer interface. PLoS ONE 8, e69962 (2013)

    Article  Google Scholar 

  10. Zhao, Q., Caiafa, C.F., Mandic, D.P., Chao, Z.C., Nagasaka, Y., Fujii, N., Zhang, L., Cichocki, A.: Higher order partial least squares (HOPLS): a generalized multilinear regression method. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1660–1673 (2013)

    Article  Google Scholar 

  11. Gao, Y., Black, M.J., Bienenstock, E., Wu, W., Donoghue, J.P.: A quantitative comparison of linear and non-linear models of motor cortical activity for the encoding and decoding of arm motions. In: The Proceedings of First International IEEE EMBS Conference on Neural Engineering 2003, pp. 189–192. IEEE (2003)

    Google Scholar 

  12. Demir, S., Toktamis, Ö.: On the adaptive Nadaraya-Watson kernel regression estimators. Hacet. J. Math. Stat. 39, 429–437 (2010)

    MathSciNet  MATH  Google Scholar 

  13. Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction. Springer Publishing Company, Incorporated, Heidelberg (2007)

    Book  MATH  Google Scholar 

  14. Geladi, P., Kowalski, B.R.: Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1–17 (1986)

    Article  Google Scholar 

  15. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009)

    Article  Google Scholar 

  16. Shimoda, K., Nagasaka, Y., Chao, Z.C., Fujii, N.: Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques. J. Neural Eng. 9, 036015 (2012)

    Article  Google Scholar 

  17. Chao, Z.C., Nagasaka, Y., Fujii, N.: Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front. Neuroengineering 3, 3 (2010)

    Google Scholar 

  18. Hazrati, M.K., Erfanian, A.: An online EEG-based brain–computer interface for controlling hand grasp using an adaptive probabilistic neural network. Med. Eng. Phys. 32, 730–739 (2010)

    Article  Google Scholar 

  19. Fatourechi, M., Bashashati, A., Ward, R.K., Birch, G.E.: A hybrid genetic algorithm approach for improving the performance of the LF-ASD brain computer interface. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP 2005), vol. 5, pp. v/345–v/348. IEEE (2005)

    Google Scholar 

  20. Mörtsell, M., Gulliksson, M.: An overview of some non-linear techniques in chemometrics. FSCN, Mitthögskolan (2001)

    Google Scholar 

  21. Schlogl, A., Kronegg, J., Huggins, J.E., Mason, S.G.: 19 evaluation criteria for BCI Research. Toward brain-computer interfacing (2007)

    Google Scholar 

  22. Nagasaka, Y., Shimoda, K., Fujii, N.: Multidimensional recording (MDR) and data sharing: an ecological open research and educational platform for neuroscience. PLoS ONE 6, e22561 (2011)

    Article  Google Scholar 

  23. Eliseyev, A., Aksenova, T.: Penalized multi-way partial least squares for smooth trajectory decoding from electrocorticographic (ECoG) recording. PLoS ONE 11, e0154878 (2016)

    Article  Google Scholar 

  24. Koyama, S., Chase, S.M., Whitford, A.S., Velliste, M., Schwartz, A.B., Kass, R.E.: Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control. J. Comput. Neurosci. 29, 73–87 (2010)

    Article  Google Scholar 

  25. Gelb, A.: Applied optimal estimation. MIT press (1974)

    Google Scholar 

  26. B. Morinière, Verney, A., Abroug, N., Garrec, P., Perrot, Y.: EMY: a dual arm exoskeleton dedicated to the evaluation of Brain Machine Interface in clinical trials. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5333–5338 (2015)

    Google Scholar 

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Acknowledgments

This work was supported in part by grants from the French National Research Agency (ANR-Carnot Institute), Fondation Motrice, Fondation Nanosciences, Fondation de l’Avenir, and Fondation Philanthropique Edmond J. Safra. The authors are grateful to all members of the CEA-LETI-CLINATEC, and especially to Prof. A.-L. Benabid.

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Authors and Affiliations

  1. Université Grenoble Alpes, 38000, Grenoble, France

    Sarah Engel, Tetiana Aksenova & Andrey Eliseyev

  2. CEA, LETI, CLINATEC, MINATEC Campus, 38000, Grenoble, France

    Sarah Engel, Tetiana Aksenova & Andrey Eliseyev

Authors
  1. Sarah Engel
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  2. Tetiana Aksenova
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  3. Andrey Eliseyev
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Corresponding author

Correspondence to Andrey Eliseyev .

Editor information

Editors and Affiliations

  1. Institute of Information Theory and Automation, Prague, Czech Republic

    Petr Tichavský

  2. Sharif University of Technology, Tehran, Iran

    Massoud Babaie-Zadeh

  3. Grenoble-Alpes University, Grenoble, France

    Olivier J.J. Michel

  4. Toulon University, Toulon, France

    Nadège Thirion-Moreau

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Engel, S., Aksenova, T., Eliseyev, A. (2017). Kernel-Based NPLS for Continuous Trajectory Decoding from ECoG Data for BCI Applications. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_39

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  • DOI: https://doi.org/10.1007/978-3-319-53547-0_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53546-3

  • Online ISBN: 978-3-319-53547-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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