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Unscented Kalman Filter for Brain-Machine Interfaces

Author

Listed:
  • Zheng Li
  • Joseph E O'Doherty
  • Timothy L Hanson
  • Mikhail A Lebedev
  • Craig S Henriquez
  • Miguel A L Nicolelis

Abstract

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation.

Suggested Citation

  • Zheng Li & Joseph E O'Doherty & Timothy L Hanson & Mikhail A Lebedev & Craig S Henriquez & Miguel A L Nicolelis, 2009. "Unscented Kalman Filter for Brain-Machine Interfaces," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-18, July.
  • Handle: RePEc:plo:pone00:0006243
    DOI: 10.1371/journal.pone.0006243
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    References listed on IDEAS

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    1. Johan Wessberg & Christopher R. Stambaugh & Jerald D. Kralik & Pamela D. Beck & Mark Laubach & John K. Chapin & Jung Kim & S. James Biggs & Mandayam A. Srinivasan & Miguel A. L. Nicolelis, 2000. "Real-time prediction of hand trajectory by ensembles of cortical neurons in primates," Nature, Nature, vol. 408(6810), pages 361-365, November.
    2. Miguel A. L. Nicolelis, 2001. "Actions from thoughts," Nature, Nature, vol. 409(6818), pages 403-407, January.
    3. Mijail D. Serruya & Nicholas G. Hatsopoulos & Liam Paninski & Matthew R. Fellows & John P. Donoghue, 2002. "Instant neural control of a movement signal," Nature, Nature, vol. 416(6877), pages 141-142, March.
    4. Meel Velliste & Sagi Perel & M. Chance Spalding & Andrew S. Whitford & Andrew B. Schwartz, 2008. "Cortical control of a prosthetic arm for self-feeding," Nature, Nature, vol. 453(7198), pages 1098-1101, June.
    5. Leigh R. Hochberg & Mijail D. Serruya & Gerhard M. Friehs & Jon A. Mukand & Maryam Saleh & Abraham H. Caplan & Almut Branner & David Chen & Richard D. Penn & John P. Donoghue, 2006. "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, Nature, vol. 442(7099), pages 164-171, July.
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    Cited by:

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    2. Ahn, Sungyong, 2016. "Becoming a network beyond boundaries: Brain-Machine Interfaces (BMIs) as the actor-networks after the internet of things," Technology in Society, Elsevier, vol. 47(C), pages 49-59.
    3. Wu Zhu & Jian-an Fang & Yang Tang & Wenbing Zhang & Wei Du, 2012. "Digital IIR Filters Design Using Differential Evolution Algorithm with a Controllable Probabilistic Population Size," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    4. Rodina, L. & Harris, L. & Ziervogel, G. & Wilson, J., 2024. "Resilience counter-currents: Water infrastructures, informality, and inequities in Cape Town, South Africa," World Development, Elsevier, vol. 180(C).
    5. Andrey Eliseyev & Tetiana Aksenova, 2016. "Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.

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