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[0] Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs SJ, Srinivasan MA, Nicolelis MA, Real-time prediction of hand trajectory by ensembles of cortical neurons in primates.Nature 408:6810, 361-5 (2000 Nov 16)

[0] Brockwell AE, Rojas AL, Kass RE, Recursive bayesian decoding of motor cortical signals by particle filtering.J Neurophysiol 91:4, 1899-907 (2004 Apr)

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ref: Wessberg-2000.11 tags: wessberg nicolelis BMI wiener date: 01-08-2012 02:53 gmt revision:4 [3] [2] [1] [0] [head]

PMID-11099043[0] Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

  • both linear and nonlinear methods
  • 3d robotic control through the internet gee-whiz!
  • 2 owl monkeys.
  • Showed neuron-dropping curves.
    • Analysis revealed fewer PMd neurons would be required to achieve 90% accuracy (480 PMD, approximately 660 M1, 1200 ipsilateral).
  • Used non-overtrained food picking behavior.

____References____

{989}
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ref: Li-2009.07 tags: Zheng Odoherty Nicolelis unscented kalman wiener filter date: 01-07-2012 23:57 gmt revision:1 [0] [head]

PMID-19603074[0] Unscented Kalman filter for brain-machine interfaces.

  • Includes quadratic neuron tuning curves.
  • Includes n-1 past states for augmented state prediction.
  • Population vector .. has < 0 SNR.
  • Works best with only 1 future tap .. ?
  • Pursuit and center-out tasks.

____References____

[0] Li Z, O'Doherty JE, Hanson TL, Lebedev MA, Henriquez CS, Nicolelis MA, Unscented Kalman filter for brain-machine interfaces.PLoS One 4:7, e6243 (2009 Jul 15)

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ref: thesis-0 tags: clementine Kalman wiener tlh24 date: 01-06-2012 03:08 gmt revision:3 [2] [1] [0] [head]

040507. wiener pred. same deal as {262}

kalman fit/pred.

per-unit and channel aggregate SNR summary

    unit     chan       lag       snr       behav var
    1.0000   69.0000    1.0000    1.1159    2.0000
    1.0000   58.0000    1.0000    1.1074    6.0000
    2.0000   44.0000    2.0000    1.1040    2.0000
    2.0000   44.0000    1.0000    1.0953    2.0000
    2.0000   93.0000    1.0000    1.0868    3.0000
    2.0000   64.0000         0    1.0728    3.0000
    1.0000   69.0000    2.0000    1.0698    2.0000
    1.0000   32.0000         0    1.0684    3.0000
    2.0000   44.0000         0    1.0634    8.0000
    1.0000   58.0000         0    1.0613    6.0000
    1.0000   33.0000    1.0000    1.0594    1.0000
    2.0000   93.0000    3.0000    1.0523    3.0000
    1.0000   63.0000         0    1.0507    3.0000
    1.0000   67.0000    1.0000    1.0490    5.0000
    1.0000   47.0000         0    1.0489    3.0000
    1.0000   12.0000    4.0000    1.0472    3.0000
    2.0000   93.0000    2.0000    1.0460    3.0000
    1.0000   24.0000         0    1.0459    3.0000
    1.0000   42.0000    1.0000    1.0447    6.0000
    1.0000   24.0000    1.0000    1.0440    3.0000
    1.0000   69.0000    3.0000    1.0431    2.0000
    2.0000   60.0000         0    1.0429    5.0000
    1.0000   61.0000         0    1.0410    4.0000
    1.0000   12.0000    1.0000    1.0400    1.0000
    1.0000   32.0000    3.0000    1.0395    3.0000
    1.0000    8.0000    1.0000    1.0387    1.0000
    1.0000   33.0000         0    1.0386   11.0000
    1.0000         0    1.0000    1.0383    4.0000
    2.0000   77.0000    2.0000    1.0383    1.0000
    1.0000   47.0000    1.0000    1.0382    3.0000
    2.0000   60.0000    1.0000    1.0376   10.0000
    2.0000   77.0000    1.0000    1.0375    1.0000
    1.0000   28.0000    1.0000    1.0374    1.0000
    1.0000   69.0000    5.0000    1.0359    3.0000
    1.0000   42.0000         0    1.0358    3.0000
    1.0000    8.0000         0    1.0357    3.0000
    1.0000   63.0000    3.0000    1.0357    3.0000
    2.0000   68.0000    1.0000    1.0348    1.0000
    1.0000   51.0000         0    1.0343    3.0000
    1.0000   30.0000    1.0000    1.0341    1.0000
    1.0000   24.0000    2.0000    1.0341    3.0000
    2.0000   93.0000    5.0000    1.0340    3.0000
    1.0000   63.0000    4.0000    1.0338    3.0000
    1.0000   63.0000    2.0000    1.0337    3.0000
    1.0000   12.0000    2.0000    1.0329    1.0000
    2.0000   23.0000    1.0000    1.0325    1.0000
    1.0000   46.0000    1.0000    1.0324    2.0000
    1.0000   28.0000         0    1.0323    1.0000
    2.0000   93.0000    4.0000    1.0321    3.0000
    1.0000   58.0000    3.0000    1.0316    6.0000
    1.0000   47.0000    2.0000    1.0314    6.0000
    1.0000   48.0000         0    1.0311    4.0000
    1.0000   12.0000    3.0000    1.0310    3.0000
    1.0000   12.0000         0    1.0309    3.0000
    1.0000   48.0000    1.0000    1.0303   11.0000
    1.0000   28.0000    2.0000    1.0300    1.0000
    2.0000   60.0000    2.0000    1.0294   10.0000
    1.0000   46.0000         0    1.0293    8.0000
    1.0000   49.0000         0    1.0291    3.0000
    1.0000   24.0000    3.0000    1.0286    1.0000
    2.0000   77.0000    3.0000    1.0282    3.0000
    1.0000    8.0000    2.0000    1.0282    1.0000
    2.0000   15.0000    1.0000    1.0281    3.0000
    2.0000   68.0000    2.0000    1.0278    1.0000
    2.0000   23.0000         0    1.0273    1.0000
    1.0000  112.0000    1.0000    1.0261    7.0000
    1.0000   69.0000    4.0000    1.0258    3.0000
    2.0000   92.0000    3.0000    1.0244    3.0000
    2.0000   42.0000    1.0000    1.0244   11.0000
    1.0000   58.0000    2.0000    1.0238    3.0000
    1.0000   61.0000    1.0000    1.0234    7.0000
    1.0000   32.0000    4.0000    1.0232    3.0000
    1.0000   33.0000    2.0000    1.0231    1.0000
    1.0000   30.0000    4.0000    1.0231    3.0000
    1.0000   46.0000    2.0000    1.0227    2.0000
    1.0000   30.0000    3.0000    1.0226    3.0000
    1.0000   45.0000         0    1.0225    3.0000
    1.0000   60.0000         0    1.0225    3.0000
    2.0000   84.0000    5.0000    1.0222    3.0000
    1.0000   32.0000    1.0000    1.0221    1.0000
    1.0000   24.0000    4.0000    1.0220    1.0000
    1.0000   28.0000    3.0000    1.0219    1.0000
    1.0000   64.0000    1.0000    1.0216    4.0000
    2.0000   84.0000    1.0000    1.0215    3.0000
    1.0000   30.0000         0    1.0212    3.0000
    2.0000   77.0000    5.0000    1.0211    3.0000
    1.0000   63.0000    1.0000    1.0210    3.0000
    1.0000   33.0000    4.0000    1.0209    1.0000
    1.0000    7.0000    1.0000    1.0209    3.0000
    2.0000   35.0000         0    1.0202    3.0000

{176}
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ref: Brockwell-2004.04 tags: particle_filter Brockwell BMI 2004 wiener filter population_vector MCMC date: 02-05-2007 18:54 gmt revision:1 [0] [head]

PMID-15010499[0] Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering

  • It seems that particle filtering is 3-5 times more efficient / accurate than optimal linear control, and 7-10 times more efficient than the population vector method.
  • synthetic data: inhomogeneous poisson point process, 400 bins of 30ms width = 12 seconds, random walk model.
  • monkey data: 258 neurons recorded in independent experiments in the ventral premotor cortex. monkey performed a 3D center-out task followed by an ellipse tracing task.
  • Bayesian methods work optimally when their models/assumptions hold for the data being analyzed.
  • Bayesian filters in the past were computationally inefficient; particle filtering was developed as a method to address this problem.
  • tested the particle filter in a simulated study and a single-unit monkey recording ellipse-tracing experiment. (data from Rena and Schwartz 2003)
  • there is a lot of math in the latter half of the paper describing their results. The tracings look really good, and I guess this is from the quality of the single-unit recordings.
  • appendix details the 'innovative methodology ;)

____References____