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analysis:course-w16:week4 [2016/01/26 10:07]
eirvine [Reconstructing a signal from sampled data (optional)]
analysis:course-w16:week4 [2016/02/03 17:22]
mvdm [Reconstructing a signal from sampled data (optional)]
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 <code matlab> <code matlab>
 % sample at 12 Hz with different method % sample at 12 Hz with different method
-time1d ​= decimate(time1, dt);+tvec1d ​= decimate(tvec1, dt);
 signal2d = decimate(signal1,​dt);​ signal2d = decimate(signal1,​dt);​
  
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 You should obtain something like: You should obtain something like:
  
-{{ :​analysis:​course:​week3_fig1.png?​nolink&​600 |}}+{{ :​analysis:​course-w16:spline_recover.png?​nolink&​600 |}}
  
 Notice how the spline-interpolated sampled signal is a pretty good approximation to the original. In cases where you care about detecting the values and/or locations of signal peaks, such as during spike sorting, performing spline interpolation can often improve accuracy substantially! Notice how the spline-interpolated sampled signal is a pretty good approximation to the original. In cases where you care about detecting the values and/or locations of signal peaks, such as during spike sorting, performing spline interpolation can often improve accuracy substantially!
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 ==== Detailed examination of Neuralynx time series data ==== ==== Detailed examination of Neuralynx time series data ====
  
-This section will look in some detail at how raw time series data is stored by the Neuralynx system. Even if you do not use this system in your own work, the lessions ​that can be learned from looking at what can go wrong at the raw data level already are universal!+This section will look in some detail at how raw time series data is stored by the Neuralynx system. Even if you do not use this system in your own work, the lessons ​that can be learned from looking at what can go wrong at the raw data level already are universal!
  
 To get into the guts of actual Neuralynx data, we will not use the sanitized wrapper provided by ''​LoadCSC()''​ but instead use the loading function provided by Neuralynx. Using cell mode in a sandbox file as usual, ''​cd''​ into the ''​R016-2012-10-08''​ data folder you downloaded previously in Week 1. Then deploy the Neuralynx loader: To get into the guts of actual Neuralynx data, we will not use the sanitized wrapper provided by ''​LoadCSC()''​ but instead use the loading function provided by Neuralynx. Using cell mode in a sandbox file as usual, ''​cd''​ into the ''​R016-2012-10-08''​ data folder you downloaded previously in Week 1. Then deploy the Neuralynx loader:
analysis/course-w16/week4.txt ยท Last modified: 2018/07/07 10:19 (external edit)