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[0] BASMAJIAN JV, Control and training of individual motor units.Science 141no Issue 440-1 (1963 Aug 2)

[0] Cheney PD, Fetz EE, Functional classes of primate corticomotoneuronal cells and their relation to active force.J Neurophysiol 44:4, 773-91 (1980 Oct)

[0] Jackson A, Mavoori J, Fetz EE, Correlations between the same motor cortex cells and arm muscles during a trained task, free behavior, and natural sleep in the macaque monkey.J Neurophysiol 97:1, 360-74 (2007 Jan)

[0] Radhakrishnan SM, Baker SN, Jackson A, Learning a novel myoelectric-controlled interface task.J Neurophysiol no Volume no Issue no Pages (2008 Jul 30)

[0] Clancy EA, Xia H, Christie A, Kamen G, Equalization filters for multiple-channel electromyogram arrays.J Neurosci Methods 165:1, 64-71 (2007 Sep 15)

[0] Townsend BR, Paninski L, Lemon RN, Linear encoding of muscle activity in primary motor cortex and cerebellum.J Neurophysiol 96:5, 2578-92 (2006 Nov)

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ref: -0 tags: myoelectric EMG recording TMR prosthetics date: 02-13-2017 20:43 gmt revision:0 [head]

PMID: Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

  • General idea: deconvolve a grid-recorded EMG signal to infer the spinal motorneron spikes, and use this to more accurately decode user intention.
  • EMG envelope is still fairly good...

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ref: Brown-2001.12 tags: EMG ECoG motor control human coherence dopamine oscillations date: 01-19-2012 21:41 gmt revision:5 [4] [3] [2] [1] [0] [head]

PMID-11765129[0] Cortical network resonance and motor activity in humans.

  • good review.
  • No coherence between ECoG and eMG below 12 Hz; frequency coherence around 18 Hz.
    • This seen only in high-resolution ECoG; lower resolution signals blurs the sharp peak.
  • Striking narrowband frequency of coherence.
  • ECoG - ECoG coherence not at same frequency as EMG-ECoG.
  • Marked task-dependence of these coherences, e.g. for wrist extension and flexion they observed similar EMG/ECoG coherences; for different tasks using the same muscles, different patterns of coherence.
  • Pyramidal cell discharge tends to be phase-locked to oscillations in the local field potential (Murthy and Fetz 1996)
    • All synchronization must ultimately be through spikes, as LFPs are not transmitted down the spinal cord.
  • Broadband coherence is pathological // they note it occurred during cortical myclonus (box 2)
  • Superficial chattering pyramidal cells (!!) firing bursts of frequency at 20 to 80 Hz, interconnected to produce spike doublets (Jefferys 1996).
  • Dopamine restores coherence between EMG and ECoG in a PD patient.

____References____

[0] Brown P, Marsden JF, Cortical network resonance and motor activity in humans.Neuroscientist 7:6, 518-27 (2001 Dec)

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ref: BASMAJIAN-1963.08 tags: original BMI M1 human EMG tuning operant control Basmajian date: 01-05-2012 00:49 gmt revision:6 [5] [4] [3] [2] [1] [0] [head]

PMID-13969854[0] Control and Training of Individual Motor Units

  • humans have the ability to control the firing rate of peripheral motor units with a high resolution.
  • "The quality of control over anterior horn cells may determine the rates of learning" yup!
  • "Some learn such esquisite control that they soon can produce rhythms of contraction in one unit, imitating drum rolls etc"
  • the youngest persons were among both the best and worst learners.
  • after about 30 minutes the subject was required to learn how to repress the first unit and to recruit another one.
    • motor unit = anterior horn cell, its axon, and all the muscle fibers on which the terminal branches of the axon end. max rate ~= 50hz.
    • motor units can be discriminated, much like cortical neurons, by their shape.
    • some patients could recruit 3-5 units altogether - from one bipolar electrode!
      • in playback mode (task: trigger the queried unit), several subjects had particular difficulty in recruiting the asked-for units. "They groped around in their conscious efforts to find them sometimes, it seemed, only succeded by accident"
    • some patients could recruit motor units in the absence of feedback, but they were unable to explain how they do it.
  • 0.025 (25um) nylon-insulated Karma alloy EMG recording wire.
  • feedback: auditory & visual (oscilloscope).
  • motor units have a maximum rate, above which overflow takes place and other units are recruited (in accord with the size principle).
  • "The controls (are) learned so quickly, are so esquisite, are so well retained after the feedbacks are eliminated that one must not dismiss them as tricks"

____References____

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ref: bookmarks-0 tags: EMG schematic amplifier prosthetic myopen date: 01-03-2012 23:08 gmt revision:3 [2] [1] [0] [head]

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ref: notes-0 tags: NXT EMG design myopen date: 01-03-2012 02:49 gmt revision:33 [32] [31] [30] [29] [28] [27] [head]

devices:

devices that can be turned off & on to save power (e.g. actually disconnected from power through a P channel MOSFET. must be careful to tristate all outputs before disabling, otherwise we'll get current through the ESD protection diodes )

  1. ethernet
  2. usb reset
  3. usb host power
  4. RS 232
  5. LCD (or at least the 40ma, 6V LED -- the LCD can be disabled in software, and it only consumes 2-3 ma anyway.)
  6. core voltage boost 0.8v to 1.2V
  7. AFE

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ref: Cheney-1980.1 tags: M1 kinematics dynamics tuning STA EMG Fetz date: 01-03-2012 02:30 gmt revision:3 [2] [1] [0] [head]

PMID-6253605[0] Functional classes of primate corticomotoneuronal cells and their relation to active force

  • monkeys made ramp and hold torque wrist movements/contractions.
  • corticomotoneuronal cells identified by clear postspike facilitation of rectified EMG activity.
  • all CM cells or PTNs were related to force - with a mixture/diversity of phasic, tonic, and ramp discharge rate profiles.
  • torque trajectory rather than velocity signal seems to be the primnary determinant of cell firing rate...
  • cells appear to be recruited at low force levels..with increasing rates as the torque increases.
  • high firing rates observed > 100!
    • and really low firing rate when there was no torque.

____References____

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ref: Merletti-2009.02 tags: surface EMG multielectrode recording technology italy date: 01-03-2012 01:07 gmt revision:2 [1] [0] [head]

PMID-19042063[0] Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art

  • good background & review of surface EMG (sEMG) - noise levels, electrodes, electronics. eg. Instrumentation amplifiers with an input resistance < 100MOhm are not recommended, and the lower the input capacitance, the better: the impedance of a 10pf capacitor at 100hz is 160MOhm.
  • Low and balanced input impedances are required to reduce asymmetric filtering of common-mode power-line noise.

____References____

[0] Merletti R, Botter A, Troiano A, Merlo E, Minetto MA, Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art.Clin Biomech (Bristol, Avon) 24:2, 122-34 (2009 Feb)

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ref: -0 tags: Scott M1 motor control pathlets filter EMG date: 12-22-2011 22:52 gmt revision:1 [0] [head]

PMID-19923243 Complex Spatiotemporal Tuning in Human Upper-Limb Muscles

  • Original idea: M1 neurons encode 'pathlets', sophisticated high-level movement trajectories, possibly through the action of both the musculoskeletal system and spinal cord circuitry.
  • Showed that muscle pathlets can be extracted from EMG data, relkiably and between patients, implying that M1 reflects 'filter-like' properties of the body, and not high level representations.

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ref: bookmark-0 tags: EMG SNR bits delsys differential amplifier bandwidth date: 12-07-2011 03:15 gmt revision:4 [3] [2] [1] [0] [head]

http://delsys.com/KnowledgeCenter/FAQ_EMGSensor.html

  • on a very good EMG recording the signal-to-noise is 65db ~= 11 bits
  • dynamic range of 5uv to 10mv.
  • differential measurement essential.
  • googling 'EMG bandwidth' yields something around 20-500hz. study of this question
  • delsys wireless EMG system & logger - uses WLAN to transmit the data (up to 16 channels) passband 20-450hz, has QVGA screen, 1GB removable storage.
  • also see "grasp recognition from myoelectric signals" images/474_1.pdf

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ref: work-0 tags: emg_dsp design part selection stage6 date: 09-22-2010 20:09 gmt revision:9 [8] [7] [6] [5] [4] [3] [head]

"Stage 6" part selection:

  • B527 to replace the BF537 -- big difference are more pins + USB OTG high-speed port. The previous deign used Maxim's MAX3421E, which seems to drop packets / have limited bandwidth (or perhaps my USB profile is incorrect?)
    • available in both 0.8mm and 0.5mm BGA. which? both are available from Digi-key. Coarser one is fine, will be easier to route.
    • Does not support mobile SDRAM nor DDR SDRAM; just the vanilla variety.
  • Continue to use the BF532 on the wireless devices (emg, neuro)
  • LAN8710 to replace the LAN83C185. Both can use the MII interface; the LAN83 is not recommended for new designs, though it is in the easier-to-debug TQFP package. Blackfin EZ-KIT for BF527 uses the LAN8710.
    • comes in 0.5mm pitch QFN-32 package.
    • 3.3V and 1.2V supply - can supply 1.2V externally.
  • SDRAM: MT48LC16M16A2BG-7E:D, digikey 557-1220-1-ND 16M x16, or 4M x 16 bit X 4 banks.
    • VFBGA-54 package.
    • 3.3v supply.
  • converter: AD7689 8 channel, 16-bit SAR ADC. has a built-in sequencer, which is sweet. (as well as a temperature sensor??!)
    • Package: 20LFCSP.
    • Seems we can run it at 4.0V, as in stage4.
  • Inst amp: MCP4208, available MSOP-8 (they call it 8-muMax). can use the same circuitry as in stage2 - just check the bandwidth; want 2khz maybe?
  • M25P16 flash, same as on the dev board.
    • Digikey M25P16-VMN6P-ND : 150mil width SOIC-8
  • USB: use the on-board high-speed controller. No need for OTG functionality; FCI USB connector is fine. Digikey 609-1039-ND.

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ref: Oskoei-2008.08 tags: EMG pattern analysis classification neural network date: 04-07-2009 21:10 gmt revision:2 [1] [0] [head]

  • EMG pattern analysis and classification by Neural Network
    • 1989!
    • short, simple paper. showed that 20 patterns can accurately be decoded with a backprop-trained neural network.
  • PMID-18632358 Support vector machine-based classification scheme for myoelectric control applied to upper limb.
    • myoelectric discrimination with SVM running on features in both the time and frequency domain.
    • a survace MES (myoelectric sensor) is formed via the superposition of individual action potentials generated by irregular discharges of active motor units in a muscle fiber. It's amplitude, variance, energy, and frequency vary depending on contration level.
    • Time domain features:
      • Mean absolute value (MAV)
      • root mean square (RMS)
      • waveform length (WL)
      • variance
      • zero crossings (ZC)
      • slope sign changes (SSC)
      • William amplitude.
    • Frequency domain features:
      • power spectrum
      • autoregressive coefficients order 2 and 6
      • mean signal frequency
      • median signal frequency
      • good performance with just RMS + AR2 for 50 or 100ms segments. Used a SVM with a RBF kernel.
      • looks like you can just get away with time-domain metrics!!

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ref: notes-0 tags: wireless nordic headstage bridge neurorecord pictures photo EMG myopen date: 03-12-2009 02:33 gmt revision:4 [3] [2] [1] [0] [head]

{287}
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ref: Cooke-1990.03 tags: motor organization triphasic control EMG date: 03-11-2009 21:42 gmt revision:12 [11] [10] [9] [8] [7] [6] [head]

the organization of the human triphasic EMG control sequence:

  • PMID-2329356[0] Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.
  • PMID-8989378[1]
  • PMID-1629754[2]
  • PMID-2230915[3]
  • PMID-2329365[4]
  • PMID-2769335[5]
  • PMID-2769334[6]
  • PMID-3622686[7] Trajectory control in targeted force impulses. I. Role of opposing muscles.
    • Our findings emphasize that neuronal commands to opposing muscles acting at a joint must be adapted to constraints imposed by the properties of the neuromuscular plant.
  • PMID-10085332[8] Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.
    • frictionless air-jet system, rapid movements, inertia perturbation via masses on the joints, surprise trials.
    • surprise trials were well predicted by an open-loop feedforward controller.
    • there was feedback compensation upon return-to-center: it is not all feedforward (of course!)

____References____

[0] Cooke JD, Brown SH, Movement-related phasic muscle activation. II. Generation and functional role of the triphasic pattern.J Neurophysiol 63:3, 465-72 (1990 Mar)[1] Almeida GL, Hong DA, Corcos D, Gottlieb GL, Organizing principles for voluntary movement: extending single-joint rules.J Neurophysiol 74:4, 1374-81 (1995 Oct)[2] Gottlieb GL, Latash ML, Corcos DM, Liubinskas TJ, Agarwal GC, Organizing principles for single joint movements: V. Agonist-antagonist interactions.J Neurophysiol 67:6, 1417-27 (1992 Jun)[3] Corcos DM, Agarwal GC, Flaherty BP, Gottlieb GL, Organizing principles for single-joint movements. IV. Implications for isometric contractions.J Neurophysiol 64:3, 1033-42 (1990 Sep)[4] Gottlieb GL, Corcos DM, Agarwal GC, Latash ML, Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.J Neurophysiol 63:3, 625-36 (1990 Mar)[5] Corcos DM, Gottlieb GL, Agarwal GC, Organizing principles for single-joint movements. II. A speed-sensitive strategy.J Neurophysiol 62:2, 358-68 (1989 Aug)[6] Gottlieb GL, Corcos DM, Agarwal GC, Organizing principles for single-joint movements. I. A speed-insensitive strategy.J Neurophysiol 62:2, 342-57 (1989 Aug)[7] Ghez C, Gordon J, Trajectory control in targeted force impulses. I. Role of opposing muscles.Exp Brain Res 67:2, 225-40 (1987)[8] Sainburg RL, Ghez C, Kalakanis D, Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms.J Neurophysiol 81:3, 1045-56 (1999 Mar)[9] Seidler RD, Noll DC, Chintalapati P, Bilateral basal ganglia activation associated with sensorimotor adaptation.Exp Brain Res 175:3, 544-55 (2006 Nov)

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ref: Jackson-2007.01 tags: Fetz neurochil sleep motor control BMI free behavior EMG date: 03-06-2009 20:56 gmt revision:3 [2] [1] [0] [head]

PMID-17021028[0] Correlations Between the Same Motor Cortex Cells and Arm Muscles During a Trained Task, Free Behavior, and Natural Sleep in the Macaque Monkey

  • used their implanted "neurochip" recorder that recorded both EMG and neural activity. The neurochip buffers data and transmits via IR offline. It doesn't have all that much flash onboard - 16Mb.
    • used teflon-insulated 50um tungsten wires.
  • confirmed that there is a strong causal relationship, constant over the course of weeks, between motor cortex units and EMG activity.
    • some causal relationships between neural firing and EMG varied dependent on the task. Additive / multiplicative encoding?
  • this relationship was different at night, during REM sleep, though (?)
  • point out, as Todorov did, that Stereotyped motion imposes correlation between movement parameters, which could lead to spurrious relationships being mistaken for neural coding.
    • Experiments with naturalistic movement are essential for understanding innate, untrained neural control.
  • references {597} Suner et al 2005 as a previous study of long term cortical recordings. (utah probe)
  • during sleep, M1 cells exhibited a cyclical patter on quiescence followed by periods of elevated activity;
    • the cycle lasted 40-60 minutes;
    • EMG activity was seen at entrance and exit to the elevated activity period.
    • during periods of highest cortical activity, muscle activity was completely suppressed.
    • peak firing rates were above 100hz! (mean: 12-16hz).

____References____

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ref: Radhakrishnan-2008.1 tags: EMG BMI Jackson motor control learning date: 10-03-2008 16:45 gmt revision:0 [head]

PMID-18667540[0] Learning a novel myoelectric-controlled interface task.

  • EMG-controlled 2D cursor control task with variable output mapping.
  • Subjects could learn non-intuitive output transforms to a high level of performance,
  • Subjects preferred, and learned better, if hand as opposed to arm muscles were used.

____References____

{596}
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ref: Huesler-2000.1 tags: EMG synchronization Hepp-Raymond grip finger force isometric date: 09-07-2008 17:26 gmt revision:3 [2] [1] [0] [head]

PMID-11081826 EMG activation patterns during force production in precision grip. III. Synchronisation of single motor units.

  • synchronization observed in 78% of intrinsic finger muscles (within the hand itself) and 45% of extrinsic finger muscles.
    • force increase was not necessarily correlated to increased synchronization; rather, high synchronization occurred at low force production.
  • instrinsic muscles have higher force sensitivity & higher recruitment thresholds.
  • other articles in the series:
    • PMID-7615027 EMG activation patterns during force production in precision grip. I. Contribution of 15 finger muscles to isometric force.
    • PMID-7615028 EMG activation patterns during force production in precision grip. II. Muscular synergies in the spatial and temporal domain.

Dr. hepp-Raymond himself seems to be a prolific researcher, judging from his pubmed search results. e.g.:

  • PMID-18272868 Absence of gamma-range corticomuscular coherence during dynamic force in a deafferented patient.
    • quote: proprioceptive information is mandatory in the genesis of gamma-band CMC (corticomuscular coherence) during the generation and control of dynamic forces.

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ref: notes-0 tags: linear discriminant analysis LDA EMG date: 07-30-2008 20:56 gmt revision:2 [1] [0] [head]

images/588_1.pdf -- Good lecture on LDA. Below, simple LDA implementation in matlab based on the same:

% data matrix in this case is 36 x 16, 
% with 4 examples of each of 9 classes along the rows, 
% and the axes of the measurement (here the AR coef) 
% along the columns. 
Sw = zeros(16, 16); % within-class scatter covariance matrix. 
means = zeros(9,16); 
for k = 0:8
	m = data(1+k*4:4+k*4, :); % change for different counts / class
	Sw = Sw + cov( m ); % sum the 
	means(k+1, :) = mean( m ); %means of the individual classes
end
% compute the class-independent transform, 
% e.g. one transform applied to all points
% to project them into one plane. 
Sw = Sw ./ 9; % 9 classes
criterion = inv(Sw) * cov(means); 
[eigvec2, eigval2] = eig(criterion);

See {587} for results on EMG data.

{587}
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ref: -0 tags: LDA PCA autoregressive EMG date: 07-29-2008 23:35 gmt revision:3 [2] [1] [0] [head]

Below, emg classification by computing the autoregressive coefficients and feeding them into linear discriminant analysis (LDA). LDA code from here; data in myopen svn. Nine classes of movement in the data, 4 repetitions of each. The input data is 16-dimensional: 4 AR coefficients per 4 channels. This is consistent with Blair Lock's thesis.

For reference, here is an imagesc() of the raw coefficients (the 4 different color bands correspond to the 4 different channels):

{586}
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ref: -0 tags: myopen EMG recordings NLMS noise date: 07-29-2008 18:32 gmt revision:2 [1] [0] [head]

Myopen amplifiers & analog/digital filters & NLMS are working properly! Below, a recording from my deltiod as I held my arm up: (only one EMG channel active, ground was my knee))

Yellow traces are raw inputs from ADC, blue are the output from the IIR / adaptive filters; hence, you only see 8 of the 16 channels. Read from bottom to top (need a -1 in some opengl matrix somewhere...) Below, the system with no input except for free wires attached to one channel (and picking up ambient noise). For this channel, NLMS could not remove the square wave - too many harmonics - but for all other channels the algorthim properly removes 60hz interference :)

Now, let me clean this EEG paste off my shoulder & leg ;)

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ref: bookmark-0 tags: EMG apples Japan food date: 11-12-2007 17:51 gmt revision:1 [0] [head]

Electromyography of Eating Apples: Influences of Cooking, Cutting, and Peeling

  • good lord, this is retarded research!

{493}
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ref: Clancy-2007.09 tags: EMG channel equalization filter date: 11-11-2007 05:04 gmt revision:0 [head]

PMID-17614134[0] Equalization filters for multiple-channel electromyogram arrays.

  • idea: use digital filtering to equalize (as in communication systems) each electrode in a large array, and then use this to drive the common-mode (digital) rejection.

____References____

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ref: Townsend-2006.11 tags: EMG muscle activity dentate M1 cerebellum date: 04-09-2007 00:52 gmt revision:0 [head]

PMID-16790591[0] Linear encoding of muscle activity in primary motor cortex and cerebellum

  • precision grip task.
  • we showed that cells in both M1 and dentate encode muscle activity in a linear fashion
  • Neural activity in M1 was significantly more correlated with both EMG and kinematic signals than was activity in dentate nucleus
  • spike history effects added no information (probably due to the limited bandwidth of the output)

____References____