[Eeglablist] Down sample issue

[Makoto Miyakoshi]

Dear Jumana,

I don't know much about this process, but it looks like it upsample first,
filter, then downsample to the desired frequency in the following way
(hence the function name!) upfirdn() is a function called from within
resample().

Makoto



>> help upfirdn
 upfirdn  Upsample, apply a specified FIR filter, and downsample a signal.
    upfirdn(X,H,P,Q) is a cascade of three systems applied to input signal
X:
          (1) Upsampling by P (zero insertion).  P defaults to 1 if not
              specified.
          (2) FIR filtering with the filter specified by the impulse
response
              given in H.
          (3) Downsampling by Q (throwing away samples).  Q defaults to 1
if not
              specified.
    upfirdn uses an efficient polyphase implementation.

    Usually X and H are vectors, and the output is a (signal) vector.
    upfirdn permits matrix arguments under the following rules:
    If X is a matrix and H is a vector, each column of X is filtered
through H.
    If X is a vector and H is a matrix, each column of H is used to filter
X.
    If X and H are both matrices with the same number of columns, then the
i-th
       column of H is used to filter the i-th column of X.

    Specifically, these rules are carried out as follows.  Note that the
length
    of the output is Ly = ceil( ((Lx-1)*P + Lh)/Q ) where Lx = length(X)
and
    Lh = length(H).

       Input Signal X    Input Filter H    Output Signal Y   Notes
       -----------------------------------------------------------------
    1) length Lx vector  length Lh vector  length Ly vector  Usual case.
    2) Lx-by-Nx matrix   length Lh vector  Ly-by-Nx matrix   Each column of
X
                                                             is filtered by
H.
    3) length Lx vector  Lh-by-Nh matrix   Ly-by-Nh matrix   Each column of
H is
                                                             used to filter
X.
    4) Lx-by-N matrix    Lh-by-N matrix    Ly-by-N matrix    i-th column of
H is
                                                             used to filter
i-th
                                                             column of X.

    For an easy-to-use alternative to upfirdn, which does not require you
to
    supply a filter or compensate for the signal delay introduced by
filtering,
    use RESAMPLE.

    EXAMPLE: Sample-rate conversion by a factor of 147/160. It is used to
             % downconvert from 48kHz to 44.1kHz.
             L = 147; M = 160;                   % Interpolation/decimation
factors.
             Lp = 24;                            % Filter length of each
phase
             N = Lp*L-1;                         % Filter Order
             h = fir1(N,1/M,kaiser(N+1,7.8562));
             h = L*h; % Passband gain = L
             Fs = 48e3;                          % Original sampling
frequency: 48kHz
             n = 0:10239;                        % 10240 samples, 0.213
seconds long
             x  = sin(2*pi*1e3/Fs*n);            % Original signal,
sinusoid at 1kHz
             y = upfirdn(x,h,L,M);               % 9430 samples, still
0.213 seconds

             % Overlay original (48kHz) with resampled signal (44.1kHz) in
red.
             stem(n(1:49)/Fs,x(1:49)); hold on
             stem(n(1:45)/(Fs*L/M),y(12:56),'r','filled');
             xlabel('Time (sec)');ylabel('Signal value');

    See also resample, interp, decimate, fir1, intfilt.

    Reference page in Help browser
       doc upfirdn

On Fri, Dec 9, 2016 at 12:50 AM, Ahmad, Jumana <jumana.ahmad at kcl.ac.uk>
wrote:

> Dear All,
> The EEGlab function let me down sample from 1000 to 256, but as 256 isn't
> a multiple into 1000 I wonder how it's possible. HP filtered at 1.5. This
> is what I did for the purpose of ICA (was trying to equate sampling rates
> of data sets collected across sites) and will apply the weights to the
> 0.1Hz HP filtered and 1000Hz sampling rate data. Thank you.
> Best wishes,
> Jumana
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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