m8ta
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{331} | |||
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
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{1380} |
ref: -0
tags: myoelectric EMG recording TMR prosthetics
date: 02-13-2017 20:43 gmt
revision:0
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PMID: Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation
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{1067} | |||
PMID-11765129[0] Cortical network resonance and motor activity in humans.
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{330} | |||
PMID-13969854[0] Control and Training of Individual Motor Units
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{496} | |||
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{519} | |||
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 )
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{327} | |||
PMID-6253605[0] Functional classes of primate corticomotoneuronal cells and their relation to active force
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{744} |
ref: Merletti-2009.02
tags: surface EMG multielectrode recording technology italy
date: 01-03-2012 01:07 gmt
revision:2
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PMID-19042063[0] Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art
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{957} | |||
PMID-19923243 Complex Spatiotemporal Tuning in Human Upper-Limb Muscles
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{474} | |||
http://delsys.com/KnowledgeCenter/FAQ_EMGSensor.html
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{844} | |||
"Stage 6" part selection:
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{724} | |||
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{594} | |||
{287} | |||
the organization of the human triphasic EMG control sequence:
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{611} | |||
PMID-18667540[0] Learning a novel myoelectric-controlled interface task.
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{596} | |||
PMID-11081826 EMG activation patterns during force production in precision grip. III. Synchronisation of single motor units.
Dr. hepp-Raymond himself seems to be a prolific researcher, judging from his pubmed search results. e.g.:
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{588} | |||
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} | |||
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} | |||
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 ;) | |||
{495} | |||
Electromyography of Eating Apples: Influences of Cooking, Cutting, and Peeling
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{493} | |||
PMID-17614134[0] Equalization filters for multiple-channel electromyogram arrays.
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{324} | |||
PMID-16790591[0] Linear encoding of muscle activity in primary motor cortex and cerebellum
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