User Tools

Site Tools


analysis:course-w16:week6

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
analysis:course-w16:week6 [2016/02/05 14:41]
mvdm [Example application of filtering for event detection]
analysis:course-w16:week6 [2018/07/07 10:19] (current)
Line 263: Line 263:
 As is often the case, the output from ''​filtfilt()''​ actually has a steeper rolloff than that from ''​filter()''​. This is because we are effectively filtering twice, an effect that can be approximated by increasing order of the filter (if you were to filter it only once). ''​filtfilt()''​ tends to be more robust, but it is always a good idea to check your filter on white noise if you have not used it before. As is often the case, the output from ''​filtfilt()''​ actually has a steeper rolloff than that from ''​filter()''​. This is because we are effectively filtering twice, an effect that can be approximated by increasing order of the filter (if you were to filter it only once). ''​filtfilt()''​ tends to be more robust, but it is always a good idea to check your filter on white noise if you have not used it before.
  
-☛ (test your knowledge) [[analysis:​course-w16:​week4|Module 4]] introduced the importance of using an anti-aliasing filter when (sub)sampling,​ and recommended using the ''​decimate()''​ function because it has exactly such a filter built-in (as opposed to ''​subsample()''​ which does not). However, as you have seen in this module, filtering can produce phase shifts, which could lead to serious artifacts when e.g. relating spike or event times to field potential phases. Find out if the anti-aliasing filter in ''​decimate()''​ produces phase shifts.+☛ (test your knowledge) [[analysis:​course-w16:​week4|Module 4]] introduced the importance of using an anti-aliasing filter when (sub)sampling,​ and recommended using the ''​decimate()''​ function because it has exactly such a filter built-in (as opposed to ''​downsample()''​ which does not). However, as you have seen in this module, filtering can produce phase shifts, which could lead to serious artifacts when e.g. relating spike or event times to field potential phases. Find out if the anti-aliasing filter in ''​decimate()''​ produces phase shifts.
  
 ==== Some typical neuroscience applications ==== ==== Some typical neuroscience applications ====
analysis/course-w16/week6.1454701315.txt.gz · Last modified: 2018/07/07 10:19 (external edit)