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apply_multifilt_eeglab.m
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apply_multifilt_eeglab.m
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function [ALLEEG, EEG, CURRENTSET, b] = apply_multifilt_eeglab(...
ALLEEG, EEG, CURRENTSET, cutoff_freqs, dataset_names, retrieve_set)
% This is a wrapper around the function filter_data_eeglab(...).
%
% Usage:
% [ALLEEG, EEG, CURRENTSET] = apply_multifilt_eeglab(ALLEEG, EEG, CURRENTSET, cutoff_freqs)
% [ALLEEG, EEG, CURRENTSET, b] = apply_multifilt_eeglab(ALLEEG, EEG, CURRENTSET, cutoff_freqs)
%
% [...] = apply_multifilt_eeglab(ALLEEG, EEG, CURRENTSET, cutoff_freqs, dataset_names)
% [...] = apply_multifilt_eeglab(ALLEEG, EEG, CURRENTSET, cutoff_freqs, [], retrieve_set)
% [...] = apply_multifilt_eeglab(ALLEEG, EEG, CURRENTSET, cutoff_freqs, dataset_names, retrieve_set)
%
% Input Parameters:
% ALLEEG, EEG, and CURRENTSET are Required EEGLAB variables.
%
% cutoff_freqs : Double Array of the cut-off frequencies (in Hz).
% Define '0' as the first value for Low-Pass
% Filtering. Define '0' as the last element for
% High-pass filtering. All other pairs will be
% considered as band-pass filtering.
%
% Other Parameters: (optional, but recommended)
% dataset_names : Array or Set of names (character-set/array). The
% default values will be 'filter_f1_f2_data', where
% f1 and f2 are the filtered frequencies.
% retrieve_set : Integer value for the set to be retrieved for
% filtering (default '1').
%
% Outputs:
% ALLEEG, EEG, and CURRENTSET are necessary EEGLAB variables.
% b : (optional) Set of filter coefficient matrices.
%
temp = reshape(unique(cutoff_freqs, 'stable'), 1, max(size(cutoff_freqs)));
if (cutoff_freqs(end) == 0)
cutoff_freqs = [temp, 0];
end, clear temp;
switch(nargin)
case {4}
dataset_names = [];
retrieve_set = 1;
case {5}
retrieve_set = 1;
case {6}
if rem(retrieve_set, 1) > 0 || retrieve_set == 0
error('''retrieve_set'' should have a positive integer value');
end
otherwise
error('Incorrect or missing input arguments');
end
if ~isempty(dataset_names)...
&& length(cutoff_freqs) - 1 ~= length(dataset_names)
error('Mismatched dataset_names');
end
% Loop over the iterations of Datasets
b = [];
for el = 1:length(cutoff_freqs)-1
% Retrieve dataset_name
if isempty(dataset_names)
dataset_name = sprintf('filter_%0.2f_%0.2f_data',...
cutoff_freqs(el), cutoff_freqs(el+1));
else
dataset_name = dataset_names{el};
end
% Retrieve the Original Dataset
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, CURRENTSET,...
'retrieve', retrieve_set);
% Filter the Data and Save it as a New Set
[ALLEEG, EEG, CURRENTSET, b_el] = filter_data_eeglab(...
ALLEEG, EEG, CURRENTSET,...
cutoff_freqs(el), cutoff_freqs(el+1),...
dataset_name);
b = [b, {b_el}];
end
end