How to use python or Brainflow to smooth uvrms in GUI?
Gong
London
Could someone provide a tutorial, because the exported csv data needs to be analyzed using Matlab, and it is easier to analyze the signal after it is smoothed.
Comments
Another question is that how I convert the refresh rate of Openbci's uvrms from 250hz to 125hz.
Gong, hi.
Have you considered a tutorial on DSP Digital Signal Processing. Many exist in many different computer language libraries, including Matlab.
https://www.google.com/search?q=matlab+dsp+tutorial
https://www.google.com/search?q=python+dsp+tutorial
https://www.google.com/search?q=dsp+tutorial
'Smoothing' as you are referring to it, usually means adjusting your bandpass filter to have a lower high limit. For example if you changed your bandpass from (.5 Hz - 50 Hz) to (.5 - 40) or (.5 - 30), many of the higher frequency EEG components would disappear.
If you are just referring to the DISPLAY in the GUI of the time series, you can adjust filter parameters here:
https://docs.openbci.com/Software/OpenBCISoftware/GUIWidgets/#filters
But note, GUI always exports the recording in RAW format, unfiltered.
The sample rate of the 8 channel Cyton is 250 Hz. All DSP libraries have a 'resample' function.
https://www.google.com/search?q=matlab+resample+time+series
William
The channel 5 noise burst you show in your screen shot, is an artifact, likely created by movement or EMG muscle tension. Adjusting the upper limit of your bandpass will NOT remove EMG or EOG artifacts.
You might instead detect artfacts by a heuristic group of thresholds of your own design. In the example of channel 5 noise burst, if the average uV was below 20 uV, then suddenly jumps up to many times that, you could clip out that segment of noise burst. Until the signal returns again to the range below 20 uV.