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[0] Cabel DW, Cisek P, Scott SH, Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task.J Neurophysiol 86:4, 2102-8 (2001 Oct)

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ref: Ganguly-2009.07 tags: Ganguly Carmena 2009 stable neuroprosthetic BMI control learning kinarm date: 01-14-2012 21:07 gmt revision:4 [3] [2] [1] [0] [head]

PMID-19621062 Emergence of a stable cortical map for neuroprosthetic control.

  • Question: Are the neuronal adaptations evident in BMI control stable and stored like with skilled motor learning?
    • There is mixed evidence for stationary neuron -> behavior maps in motor cortex.
      • It remains unclear if the tuning relationship for M1 neurons are stable across time; if they are not stable, rather advanced adaptive algorithms will be required.
  • A stable representation did occur.
    • Small perturbations to the size of the neuronal ensemble or to the decoder could disrupt function.
    • Compare with {291} -- opposite result?
    • A second map could be learned after primary map was consolidated.
  • Used a Kinarm + Plexon, as usual.
    • Regressed linear decoder (Wiener filter) to shoulder and elbow angle.
  • Assessed waveform stability with PCA (+ amplitude) and ISI distribution (KS test).
  • Learning occurred over the course of 19 days; after about 8 days performance reached an asymptote.
    • Brain control trajectory to target became stereotyped over the course of training.
      • Stereotyped and curved -- they propose a balance of time to reach target and effort to enforce certain firing rate profiles.
    • Performance was good even at the beginning of a day -- hence motor maps could be recalled.
  • By analyzing neuron firing wrt idealized movement to target, the relationship between neuron & movement proved to be stable.
  • Tested to see if all neurons were required for accurate control by generating an online neuron dropping curve, in which a random # of units were omitted from the decoder.
    • Removal of 3 neurons (of 10 - 15) resulted in > 50% drop in accuracy.
  • Tried a shuffled decoder as well: this too could be learned in 3-8 days.
    • Shuffling was applied by permuting the neurons-to-lags mapping. Eg. the timecourse of the lags was not changed.
  • Also tried retraining the decoder (using manual control on a new day) -- performance dropped, then rapidly recovered when the original fixed decoder was reinstated.
    • This suggests that small but significant changes in the model weights (they do not analyze what) are sufficient for preventing an established cortical map from being transformed to a reliable control signal.
  • A fair bit of effort was put into making & correcting tuning curves, which is problematic as these are mostly determined by the decoder
    • Better idea would be to analyze the variance / noise properties wrt cursor trajectory?
  • Performance was about the same for smaller (10-15) and larger (41) unit ensembles.

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ref: Kim-2007.08 tags: Hyun Kim muscle activation method BMI model prediction kinarm impedance control date: 01-06-2012 00:19 gmt revision:1 [0] [head]

PMID-17694874[0] The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.

  • First BMI that successfully predicted interactions between the arm and a force field.
  • Previous BMIs are used to decode position, velocity, and acceleration, as each of these has been shown to be encoded in the motor cortex
  • Hyun talks about stiff tasks, like writing on paper vs . pliant tasks, like handling an egg; both require a mixture of force and position control.
  • Georgopoulous = velocity; Evarts = Force; Kalaska movement and force in an isometric task; [17-19] = joint dependence;
  • Todorov "On the role of primary motor cortex in arm movement control" [20] = muscle activation, which reproduces Georgouplous and Schwartz ("Direct cortical representation of drawing".
  • Kakei [19] "Muscle movement representations in the primary motor cortex" and Li [23] [1] show neurons correlate with both muscle activations and direction.
  • Argues that MAM is the best way to extract impedance information -- direct readout of impedance requires a supervised BMI to be trained on data where impedance is explicitly measured.
  • linear filter does not generalize to different force fields.
  • algorithm activity highly correlated with recorded EMG.
  • another interesting ref: [26] "Are complex control signals required for human arm movements?"


[0] Kim HK, Carmena JM, Biggs SJ, Hanson TL, Nicolelis MA, Srinivasan MA, The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.IEEE Trans Biomed Eng 54:8, 1520-9 (2007 Aug)
[1] Li CS, Padoa-Schioppa C, Bizzi E, Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field.Neuron 30:2, 593-607 (2001 May)

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ref: Fagg-2007.1 tags: BMI kinarm Hatsopoulos Moxon Miller FES date: 01-06-2012 00:17 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-17978021[0] Biomimetic Brain Machine Interfaces for the Control of Movement.

  • images/482_1.pdf
  • describe structured models that include arm information & 'plant' dynamics.
    • current methods ignore the dynamics of the musculoskeletal system. Want to mimic natural arm movement.
    • To this end used a kinarm with a paralyzed monkey.
  • obtained real-time prediction of joint force, torque, and EMG
    • Concerning quality of prediction: they use fraction of movement variance that can be accounted for (FVAF) which, though google does not seem to know much about it, is probably the same as R^2. but it does not look that great:
      • 0.61 - 0.65 for torque prediction
      • 0.70 - 0.75 for EMG prediction once again, the limitation is the recording technology.
  • tested coupling predictions to the freehand FES system - see this crazy news brief
  • want to incorporate somatosensory feedback into the BMI.
  • they reference a paper from 2008 - huh? The document claims to be written/published in 2007.


[0] Fagg AH, Hatsopoulos NG, de Lafuente V, Moxon KA, Nemati S, Rebesco JM, Romo R, Solla SA, Reimer J, Tkach D, Pohlmeyer EA, Miller LE, Biomimetic brain machine interfaces for the control of movement.J Neurosci 27:44, 11842-6 (2007 Oct 31)

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ref: notes-2007 tags: clementine BMI robot kinarm timarm 032807 date: 01-06-2012 00:07 gmt revision:14 [13] [12] [11] [10] [9] [8] [head]

  1. http://m8ta.com/tim/clementine.MOV -- opens with totem, MJPG compressor.
  2. http://m8ta.com/tim/timarm_servocontroller.JPG
  3. http://m8ta.com/tim/images/spikeInformation_shuffled.jpg
    1. shuffled information distribution -- high significance level ;)
  4. kinarm.
    1. http://www.hardcarve.com/tim/kinarm.JPG
    2. http://www.hardcarve.com/tim/kinarm2.JPG
    3. http://www.hardcarve.com/tim/kinarm3.JPG
  5. robot svg or timarm png
    1. http://www.hardcarve.com/tim/timarm/timarm_side.jpg
    2. http://m8ta.com/tim/robotPulleyDetail.png
  6. bmi predictions clem 032807
      1. x & y predictions
      1. x & y predictions
      1. z velocity predictions - pretty darn good, snr 2
    1. Movie of the day: http://m8ta.com/tim/clem032807_3dBMI.MPG
      1. cells for that day - 40 in all

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ref: work-0 tags: kinarm problem mathML date: 11-03-2010 16:05 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

Historical notes from using the Kinarm... this only seems to render properly in firefox / mozilla.

To apply cartesian force fields to the arm, the original kinarm PLCC (whatever that stands for) converted joint velocities to cartesian veolocities using the jacobian matrix. All well and good. The equation for endpoint location of the kinarm is:

x^=[l 1sin(θ sho)+l 2sin(θ sho+θ elb) l 1cos(θ sho)+l 2cos(θ sho+θ elb)] \hat{x} = { \left[ \array{ l_1 sin(\theta_{sho}) + l_2 sin(\theta_{sho} + \theta_{elb} ) \\ l_1 cos(\theta_{sho}) + l_2 cos(\theta_{sho} + \theta_{elb} ) } \right] }

L_1 = 0.115 meters, l_2 = 0.195 meters in our case. The jacobian of this function is: J=[l 1sin(θ sho)l 2sin(θ sho+θ elb) l 2sin(θ elb) l 1cos(θ sho)+l 2cos(θ sho+θ elb) l 2cos(θ elb)] J = { \left[ \array{ - l_1 sin(\theta_{sho}) - l_2 sin(\theta_{sho} + \theta_{elb} ) && - l_2 sin(\theta_{elb}) \\ l_1 cos(\theta_{sho}) + l_2 cos(\theta_{sho} + \theta_{elb} ) && l_2 cos(\theta_{elb}) } \right] } v^=Jθ^ \hat{v} = J \cdot \hat{\theta} etc. and (I think!) F^=Jτ^ \hat{F} = J \cdot \hat{\tau} where tau is the shoulder and elbow torques and F is the cartesian force. The flow of the PLCC is then:

  1. convert joint angluar velocities to cartesian velocities
  2. cartesian velocities to cartesian forces by a symmetric matrix A which effects simple viscious and curl fields.
F^=Av^ \hat{F} = A \cdot \hat{v}
  1. cartesian forces to joint torques via the inverse of the jacobian.
But, and I may be wrong here, rather than inverting the jacobian, the PLCC simply takes the transform. The inverse of the jacobian and the transpose are not even close to equal. viz (from mathworld):

J=[a b c d] J = { \left[ \array{ a & b \\ c & d } \right] }

J 1=1adbc[d b c a][a c b d]=J T J^{-1} = \frac{ 1}{a d - b c} { \left[ \array{d &-b \\ -c & a} \right] } \ne { \left[ \array{a & c \\ b & d} \right] } = J^{T}

substitute to see if the matrices look similar ...

|J|[l 2cos(θ elb) l 2sin(θ elb) l 1cos(θ sho)l 2cos(θ sho+θ elb) l 1sin(θ sho)l 2sin(θ sho+θ elb)][l 1sin(θ sho)l 2sin(θ sho+θ elb) l 1cos(θ sho)+l 2cos(θ sho+θ elb) l 2sin(θ elb) l 2cos(θ elb)]{\vert J \vert} \cdot { \left[ \array{ l_2 cos(\theta_{elb}) && l_2 sin(\theta_{elb}) \\ - l_1 cos(\theta_{sho}) - l_2 cos(\theta_{sho} + \theta_{elb} ) && - l_1 sin(\theta_{sho}) - l_2 sin(\theta_{sho} + \theta_{elb} ) } \right] } \ne { \left[ \array{ - l_1 sin(\theta_{sho}) - l_2 sin(\theta_{sho} + \theta_{elb} ) && l_1 cos(\theta_{sho}) + l_2 cos(\theta_{sho} + \theta_{elb} ) \\ - l_2 sin(\theta_{elb}) && l_2 cos(\theta_{elb}) } \right] }


|J|=l 1l 2sin(θ sho)cos(θ elb)l 2 2sin(θ sho+θ elb)cos(θ elb)+l 1l 2cos(θ sho)sin(θ elb)l 2 2cos(θ sho+θ elb)sin(θ elb) {\vert J \vert} = { - l_1 l_2 sin(\theta_sho) cos(\theta_elb) - l_2^2 sin(\theta_{sho} + \theta_{elb} ) cos(\theta_elb) + - l_1 l_2 cos(\theta_sho) sin(\theta_elb) - l_2^2 cos(\theta_{sho} + \theta_{elb} ) sin(\theta_elb) }

I'm surprised that we got something even like curl and viscous forces - the matrices are not similar. This explains why the forces seemed odd and poorly scaled, and why the constants for the viscious and curl fields were so small (the units should have been N/(cm/s) - 1 newton is a reasonable force, and the monkey moves at around 10cm/sec, so the constant should have been 1/10 or so. Instead, we usually put in a value of 0.0005 ! For typical values of the shoulder and elbow angles, the determinant of the matrix is 200 (the kinarm PLCC works in centimeters, not meters), so the transpose has entries ~ 200 x too big. Foolishly we compensated by making the constant (or entries in A) 200 times to small. i.e. 1/10 * 1/200 = 0.0005 :(

The end result is that a density-plot of the space spanned by the cartesian force and velocity is not very clean, as you can see in the picture below. The horizontal line is, of course, when the forces were turned off. A linear relationship between force and velocity should be manifested by a line in these plots - however, there are only suggestions of lines. The null field should have a negative - slope line in upper left and lower right; the curl field should have a positive sloped line in the upper right and negative in the lower left (or vice-vercia).


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ref: Cabel-2001.1 tags: Stephen Scott Kinarm motor control date: 04-04-2007 21:51 gmt revision:0 [head]

PMID-11600665[] Neural Activity in Primary Motor Cortex Related to Mechanical Loads Applied to the Shoulder and Elbow During a Postural Task

  • experiment w/ the kinarm. w/ Stephen Scott.
  • roughly equal numbers of neuons responsive to mechanical loads on shoulder, elbow, and both.
  • neural activity is also strongly influenced by the specific motor patterns used to perform a given task.


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ref: notes-0 tags: SQL kinarm count date: 0-0-2007 0:0 revision:0 [head]

SELECT file, COUNT(file) FROM info2 WHERE unit>1 AND maxinfo/infoshuf > 10 AND analog < 5 GROUP BY file ORDER BY COUNT(file) DESC

to count the number of files matching the criteria.. and get aggregate frequentist statistics.