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load discontinuous Neuralynx data as epochs #1155

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33 changes: 29 additions & 4 deletions fileio/private/read_neuralynx_ds.m
Original file line number Diff line number Diff line change
Expand Up @@ -157,17 +157,42 @@
end
end % if ftype_ncs

% take into account the pause/stop feature + epoch data if discontinuous recording
ncs = read_neuralynx_ncs(fname{1});
idxPauses = find(ncs.NumValidSamp < recordsize);
nValidSamples = ncs.NumValidSamp(idxPauses);
nPauses = length(idxPauses);
isPause = false(nPauses, 1);
for i = 1:nPauses
if idxPauses(i) == ncs.NRecords
isPause(i) = true;
else
timeGap = double(diff(ncs.TimeStamp(idxPauses(i):idxPauses(i)+1)));
if timeGap > 3 * mode(diff(ncs.TimeStamp)) % threshold to be tuned
isPause(i) = true;
end
end
end
idxPauses = idxPauses(isPause);
nValidSamples = nValidSamples(isPause);
nTrials = length(idxPauses);
trl = zeros(nTrials, 3);
trl(:, 2) = ((idxPauses-1)*recordsize + nValidSamples)';
trl(1, 1) = 1;
trl(2:end, 1) = (idxPauses(1:end-1)*recordsize+1)';

% construct the header that applies to all channels combined
hdr.nChans = length(label);
hdr.label = label;
hdr.filename = fname;
hdr.nTrials = 1; % it is continuous
hdr.nTrials = nTrials;
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this is fine if all trials have the same length.

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please note that hdr.nSamples*hdr.nTrials should correspond to the total number of samples in the file.

hdr.Fs = SamplingFrequency(1);
hdr.nSamplesPre = 0; % it is continuous

hdr.nSamplesPre = 0;
hdr.trl = trl;
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you cannot add fields to the hdr structure.


if ~isempty(ftype_ncs)
% these elements are only relevant for continuously sampled channels
hdr.nSamples = NRecords(1) * 512;
hdr.nSamples = NRecords(1) * recordsize;
hdr.FirstTimeStamp = FirstTimeStamp(1);
hdr.LastTimeStamp = LastTimeStamp(1);
hdr.TimeStampPerSample = TimeStampPerSample(1);
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14 changes: 9 additions & 5 deletions ft_preprocessing.m
Original file line number Diff line number Diff line change
Expand Up @@ -415,11 +415,15 @@
trl(1,2) = hdr.nSamples*hdr.nTrials;
trl(1,3) = -hdr.nSamplesPre;
else
trl = zeros(hdr.nTrials, 3);
for i=1:hdr.nTrials
trl(i,1) = (i-1)*hdr.nSamples + 1;
trl(i,2) = (i )*hdr.nSamples ;
trl(i,3) = -hdr.nSamplesPre;
if isfield(hdr, 'trl')
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you cannot suddenly add a field to the output of ft_read_header and start using that here. That breaks the API, which is shared with EEGLAB, SPM and other software. See http://www.fieldtriptoolbox.org/development/module/

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Ok good to know. Thanks to your suggestion I created ft_trialfun_neuralynx.m, and besides the fact that I now have to add a few lines before my call to ft_preprocessing, this is indeed way cleaner!

trl = hdr.trl;
else
trl = zeros(hdr.nTrials, 3);
for i=1:hdr.nTrials
trl(i,1) = (i-1)*hdr.nSamples+1;
trl(i,2) = (i)*hdr.nSamples;
trl(i,3) = -hdr.nSamplesPre;
end
end
end
cfg.trl = trl;
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57 changes: 57 additions & 0 deletions trialfun/ft_trialfun_neuralynx.m
Original file line number Diff line number Diff line change
@@ -0,0 +1,57 @@
function trl = ft_trialfun_neuralynx(cfg)

% FT_TRIALFUN_NEURALYNX outputs trials for Neuralynx datasets.
%
% Use as
% cfg = [];
% cfg.dataset = 'directory_with_ncs_files';
% cfg.trialfun = 'ft_trialfun_neuralynx';
% cfg = ft_definetrial(cfg);
% data = ft_preprocessing(cfg);
%
% A Neuralynx dataset consists of a directory containing as many .ncs files
% as electrodes. The option cfg.dataset should refer to this directory.
%
% Neuralynx data is saved as a series of 512-sample blocks (or "records").
% When user presses pause or stop buttons, the recording stops at sample i
% from the current record, and the remaining samples (i+1 to 512) are a
% copy of samples i+1 to 512 of the previous record. These copied samples
% are marked as invalid in ncs.NumValidSamples, and have to be discarded as
% they are not genuine samples and as they introduce redundancies.
%
% Contact ludovic.bellier@berkeley.edu for any questions.


gapThreshold = 3; % in number of records (e.g., at fs=8kHz, 3*512 samples represents 192 ms)
recordsize = 512;

dirname = cfg.dataset;
if ~any(strcmp(dirname(end), {'/', '\'}))
dirname = [dirname filesep];
end

ls = dir([dirname '*ncs']);
fname = cellfun(@(x) fullfile(dirname, x), {ls(:).name}, 'un', 0);

ncs = read_neuralynx_ncs(fname{1});
idxPauses = find(ncs.NumValidSamp < recordsize);
nValidSamples = ncs.NumValidSamp(idxPauses);
nPauses = length(idxPauses);
isPause = false(nPauses, 1);
for i = 1:nPauses
if idxPauses(i) == ncs.NRecords
isPause(i) = true;
else
timeGap = double(diff(ncs.TimeStamp(idxPauses(i):idxPauses(i)+1)));
if timeGap > gapThreshold * mode(diff(ncs.TimeStamp))
isPause(i) = true;
end
end
end
idxPauses = idxPauses(isPause);
nValidSamples = nValidSamples(isPause);
nTrials = length(idxPauses);
trl = zeros(nTrials, 3);
trl(:, 2) = ((idxPauses-1)*recordsize + nValidSamples)';
trl(1, 1) = 1;
trl(2:end, 1) = (idxPauses(1:end-1)*recordsize+1)';