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Hi?Phillip,?
Thank you for the information.
1. During the band-pass filter before applying Hilbert, some level of
smoothing happens. That is why I want to know my exact temporal
resolution to report in the paper. In MNE examples, I saw the code
"w_size = n_cycles / ((fmax + fmin) / 2.) # in seconds
<Page Redirection;
to calculate the infer window size but I am not sure whether this is
the way to calculate the temporal resolution?according to the number
of cycles?and the frequency of interests (fmin, fmax).
That example doesn't use the HIlbert transform .... and that example
chunks the data up into windows, but wants the same number of cycles,
regardless of frequency band, in each window and so has to have
variable-length windows.
So that's not related to the filtering per se. The number of complete
oscillatory cycles for a given frequency f is dependent on the length of
the time window. This is the fundamental trade-off. For the example,
they just take the middle/average frequency of a given frequency bracket.
The Hilbert transformation preserves the temporal resolution of the
phase information at the cost of the frequency resolution: the Hilbert
transform 'averages' across all the frequencies present in the signal
and gives a single phase value for that. So the work-around is filtering
in advance, which is where the time-frequency tradeoff comes out ....
2. Thanks for the links. However, my question was more about the way
they implement the filter in eegfilt?routine in MATLAB (a two-way
least-square FIR?filter with maximally steep roll-offs and an
extremely narrow transition band).
Again, look at the documentation for full details about how the
different filter options are implemented in MNE and how to specify the
design you want.
It's been a long time since I've done any analysis in MATLAB, but I
suspect eegfilt isn't a MATLAB-builtin but rather implemented in EEGLAB,
ERPLAB or FieldTrip. Specifying that is more relevant than MATLAB --
that would be like asking about Python's "Epochs" object. 
I know e.g.
that there were some problems with EEGLAB's old FIR filters that have
largely been addressed.
For a good comparison of the different packages back (at least as they
were in 2015 and unfortunately not including MNE), see
Widmann, A.; Schr?ger, E. & Maess, B. Digital filter design for
electrophysiological data ? a practical approach? Journal of
Neuroscience Methods, Cutting-edge EEG Methods, 2015 , 250 , 34-46
(but again see the current documentation for the most up-to-date info)
That paper is also a nice introduction to filtering in a way that's
quire relevant for your question. The narrow transition band will you
longer filters, which will in some sense decrease your temporal
resolution in a way that's you can perhaps best summarize with the
impulse response function.
Best,
Phillip
I would be thankful if you could help me with the above questions.
Thanks,
-Mary
?
The advantage to the Hilbert transform is that it more or less
preserves the original temporal resolution but completely loses
any frequency resolution (hence the need to filter beforehand,
which does in a certain sense impact temporal resolution).
Regarding your second question, a quick search yields a number of
results:
Search - MNE 1.6.0 documentation
but especially
mne.io.Raw — MNE 1.6.0 documentation
Page Redirection
Phillip
???External Email - Use Caution???
Hi all,?
I am analyzing my EEG signals through time-frequency methods and
interested in the pre-stimulus alpha and theta band activity. The
time resolution is important for my analysis. I have two very
basic questions:
? ? ? ? ? ??1.? I am wondering if there is any way to calculate
the temporal precision/resolution after applying the
Hilbert?transform to get the power??
? ? ? ? ? ? 2. Also, is there any function similar to *eegfilt*
in Matlab?in MNE??
Thank you,?
-Mary
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