raw data loaded using loadraw.m

[Reinhard]

Hi, All,

We tried to input the raw data, and got a voltage v.s. time plot in each channel. The 1500000 in time axis corresponds to 1 min.

1. The unit of the voltage seems strange as shown below, should it be mV or V?  

2. The actual noise displayed in the NR has a time window of 250ms as shown below. Could we change the time window in NR? 

3. Could we change the sampling frequency in the NR?

4. How to compare the raw data generated using loadraw.m in matlab with the display in NR? The raw data we loaded seems quite different from that displayed in NR.

[Jon Erickson]

​Been a while since I looked at raw data, but here are my recollections

​

 

1. The unit of the voltage seems strange as shown below, should it be mV or V?  

​Pretty sure this is in units of actual voltage recorded at NI system.  You need to refer this back to input by dividing by gain stages.  There are 2 of them - 1 for MCS amp (typically gain = 1200) and one for NI cards (depends on your setting for ADC gain that you set in NR, this info is in the header file)​

 ​

3. Could we change the sampling frequency in the NR?

​Yes, click the proper tab and look for the box that says sampling frequency.  Enter any value you wish (I used to use 15 kHz)​

 

4. How to compare the raw data generated using loadraw.m in matlab with the display in NR? The raw data we loaded seems quite different from that displayed in NR.

​How so?  If you are loading 1 min of data in matlab it will look very different than the 250 ms snippets in NR b/c timescales are vastly different.​

 

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[Jon Newman]

have a look at this:

https://sites.google.com/site/neurorighter/user-manual/recording-neural-signals

and this:

https://sites.google.com/site/neurorighter/user-manual/defining-hardware-settings

To clarify (or maybe obfustcate) John's answer to question 1, the units of recorded signals depend on the gain settings that you enter into Neurorighter. To get recordings that are in the units of volts at the recordings site, you must account for (1) the gain of any analog amplification up to the ADCs and (2) the digital 'gain' of the ADCs themselves.

(2) Is taken care of automatically when you set the digital gain in NR. See the above links for how to do this Note: A large digital gain simply uses a smaller internal reference so that the 16-bits of resolution are spread over a smaller voltage range. This means that electrical stimuli will much more effectively saturate your ADCs for much longer time periods, making things like artifact subtraction impossible. 

(1) Needs to be compensated for by setting the total analog gain of everything that happens before the ADC in the '

The answer to question 2 is no, the display window is 250 ms fixed.

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Jonathan Newman

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[Jon Newman]

(1) Needs to be compensated for by setting the total analog gain of everything that happens before the ADC in the 'System' Dialog -> Neural input tab.

[Reinhard]

Hi, Jon,

Thank you so much for the illustration. However I am still very confused. So, if my Dig.Gain (raw) is setted to 2, which is the defaulted value, and Gain of our amp is 1100, the actual voltage generated by neuronal activity= the voltage recorded in raw data file/2200, right? And why I need to set this Dig.Gain? Is this a necessary?

Now I also show my rawdata in 250ms timescale window (second figure below) using matlab, but it still looks quite different from the screen display in NR and an increase trend could be observed, which was not shown in the NR display. The y-axis, which is the voltage axis, is only from -8e-4 to 2e-4, which according to my understanding could be corresponded to the real signal or noise of 0.36uV (-8e-4/2200) to 0.09uV (2e-4/2200). This is an obvious underestimation of the noise. Could you please help me on this?

[Jon Newman]

Has this been resolved? 

[Reinhard]

Hi Jon,

Not yet. We are still facing the problem of matching the rawdata readout with the software screen display. Did you level the signal every 250ms when displayed on the Neurorighter software screen display so that the noise would not go up or down monotonically? Besides, is it possible for you to send us some rawdata files (not the spike file), which could be with noise only or with spikes detected, to check our matlab processing step? 

Thank you very much. 

Has this been resolved? 

 

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[Jon Newman]

What type of file did you record (selected via the 'select recording stream' dialog as explained on this page https://sites.google.com/site/neurorighter/user-manual/recording-neural-signals)? If it's a raw recording (.raw) then it will not include any of neurorighters digital filtering. It looks like this is the case. There is nothing wrong with the data it just does not have any filtering. The filtered signal, whether recorded or not, is what is displayed in the boxes. To record filtered signals you need to select them from the recording settings dialog. Please fully read the page above before attempting to do this.

 

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[Reinhard]

Hi, Jon

Thanks a lot. We tried .spkflt and got the expected signal/noise just like what was displayed in NR. However, we still have 2 questions.

1. What's the difference between .rawspk and .spk? 

2. Should SALPA be selected as we are only recording the signal without any stimulus?

 

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[Riley Zeller-Townson]

1. NeuroRighter gives you the options to detect spikes at different stages of the digital filtering.  rawspk are the spikes that are detected on the raw data.  salpaspk are the spikes detected on data that has gone through the salpa filter, and spk contains spikes that have gone through both salpa and the bandpass filter.  

2. Even if you are not stimulating, the SALPA filter produces two noticeable effects.  The first is that it acts as a nonlinear highpass filter, effectively removing the DC component of your signal (which is often nice to have, but not always).  The second is that if you have a channel that has very, very, very large noise on it (such as the kind that occurs when an amplifier becomes unstable), SALPA will act to blank out any periods where noise is super high.  

Hope that helps!

 

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Riley Zeller-Townson

PhD student, Potter lab

Laboratory for Neuroengineering

Georgia Tech/Emory University

Atlanta, GA, USA

[Reinhard]

Hi, Riley

Thanks for your information. That's very helpful!

Regards,

Qinwei

 

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