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function [spike] = ft_datatype_spike(spike, varargin)
% FT_DATATYPE_SPIKE describes the FieldTrip MATLAB structure for spike data
%
% Spike data is obtained using FT_READ_SPIKE to read files from a Plexon,
% Neuralynx or other animal electrophysiology data acquisition system. It
% is characterised as a sparse point-process, i.e. each neuronal firing is
% only represented as the time at which the firing happened. Optionally,
% the spike waveform can also be represented. Using this waveform, the
% neuronal firing events can be sorted into their single units.
%
% A required characteristic of the SPIKE structure is a cell-array with the
% label of the (single or multi) units.
%
% label: {'unit1' 'unit2' 'unit3'}
%
% The fields of the SPIKE structure that contain the specific information
% per spike depends on the available information. A relevant distinction
% can be made between the representation of raw spikes that are not related
% to the temporal structure of the experimental design (i.e trials), and
% the data representation in which the spikes are related to the trial.
%
% For a continuous recording the SPIKE structure must contain a cell-array
% with the raw timestamps as recorded by the hardware system. As example,
% the original content of the .timestamp field can be
%
% timestamp: {[1x504 uint64] [1x50 uint64] [1x101 uint64]}
%
% An optional field that is typically obtained from the raw recording
% contains the waveforms for every unit and label as a cell-array. For
% example, the content of this field may be
%
% waveform: {[1x32x504 double] [1x32x50 double] [1x32x101 double]}
%
% If the data has been organised to reflect the temporal structure of the
% experiment (i.e. the trials), the SPIKE structure should contain a
% cell-array with the spike times relative to an experimental trigger. The
% FT_SPIKE_MAKETRIALS function can be used to reorganise the SPIKE
% structure such that the spike times are expressed relative to a trigger
% instead of relative to the acquisition devices internal timestamp clock.
% The time field then contains only those spikes that ocurred within one of
% the trials . The spike times are now expressed on seconds relative to the
% trigger.
%
% time: {[1x504 double] [1x50 double] [1x101 double]}
%
% In addition, for every spike we register in which trial the spike was
% recorded:
%
% trial: {[1x504 double] [1x50 double] [1x101 double]}
%
% To fully reconstruct the structure of the spike-train, it is required
% that the exact start- and end-point of the trial (in seconds) is
% represented. This is specified in a nTrials x 2 matrix.
%
% trialtime: [100x2 double]
%
% As an example, FT_SPIKE_MAKETRIALS could result in the following
% SPIKE structure that represents the spikes of three units that were
% observed in 100 trials:
%
% label: {'unit1' 'unit2' 'unit3'}
% timestamp: {[1x504 double] [1x50 double] [1x101 double]}
% timestampdimord: '{chan}_spike'
% time: {[1x504 double] [1x50 double] [1x101 double]}
% trial: {[1x504 double] [1x50 double] [1x101 double]}
% trialtime: [100x2 double]
% sampleinfo: [100x2 double]
% waveform: {[1x32x504 double] [1x32x50 double] [1x32x101 double]}
% waveformdimord: '{chan}_lead_time_spike'
%
% For analysing the relation between the spikes and the local field
% potential (e.g. phase-locking), the SPIKE structure can have additional
% fields such as fourierspctrm, lfplabel, freq and fourierspctrmdimord.
%
% For example, from the structure above we may obtain
%
% label: {'unit1' 'unit2' 'unit3'}
% time: {[1x504 double] [1x50 double] [1x101 double]}
% trial: {[1x504 double] [1x50 double] [1x101 double]}
% trialtime: [100x2 double]
% timestamp: {[1x504 double] [1x50 double] [1x101 double]}
% timestampdimord: '{chan}_spike'
% waveform: {[1x32x504 double] [1x32x50 double] [1x32x101 double]}
% waveformdimord: '{chan}_lead_time_spike'
% fourierspctrm: {504x2x20, 50x2x20, 101x2x20}
% fourierspctrmdimord: '{chan}_spike_lfplabel_freq'
% lfplabel: {'lfpchan1', 'lfpchan2'}
% freq: [1x20 double]
%
% Required fields:
% - label
% - timestamp
%
% Optional fields:
% - time, trial, trialtime
% - timestampdimord
% - unit, unitdimord
% - waveform, waveformdimord
% - fourierspctrm, fourierspctrmdimord, freq, lfplabel (these are extra outputs from FT_SPIKETRIGGEREDSPECTRUM and FT_SPIKE_TRIGGEREDSPECTRUM)
% - hdr
% - cfg
%
% Deprecated fields:
% - origtime, origtrial
%
% Obsoleted fields:
% - <unknown>
%
% Revision history:
%
% (2020/latest) Add an explicit xxxdimord for each of the known fields.
%
% (2012) Changed the dimensionality of the waveform to allow both
% stereotrode and tetrode data to be represented.
%
% (2011) Defined a consistent spike data representation that can
% also contain the Fourier spectrum and other fields. Use the xxxdimord
% to indicate the dimensions of the field.
%
% (2010) Introduced the time and the trialtime fields.
%
% (2007) Introduced the spike data representation.
%
% See also FT_DATATYPE, FT_DATATYPE_RAW, FT_DATATYPE_FREQ, FT_DATATYPE_TIMELOCK
% Copyright (C) 2011-2020, Robert Oostenveld & Martin Vinck
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id$
% get the optional input arguments, which should be specified as key-value pairs
version = ft_getopt(varargin, 'version', 'latest');
if strcmp(version, 'latest')
version = '2020';
end
if isempty(spike)
return;
end
switch version
case '2020'
% ensure it is up to date with the previous version, which incoreporates changes from 2011 and 2012
spike = ft_datatype_spike(spike, 'version', '2012');
% remove the general dimord
if isfield(spike, 'dimord')
spike = rmfield(spike, 'dimord');
end
% add explicit dimords for the known fields
if isfield(spike, 'timestamp') && ~isfield(spike, 'timestampdimord')
spike.timestampdimord = '{chan}_spike';
end
if isfield(spike, 'unit') && ~isfield(spike, 'unitdimord')
spike.unitdimord = '{chan}_spike';
end
if isfield(spike, 'waveform') && ~isfield(spike, 'waveformdimord')
spike.waveformdimord = '{chan}_lead_time_spike';
end
if isfield(spike, 'fourierspctrm') && ~isfield(spike, 'fourierspctrmdimord')
spike.fourierspctrmdimord = '{chan}_spike_lfplabel_freq';
end
case '2012'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if isfield(spike,'origtrial') && isfield(spike,'origtime')
% this was the old spiketriggered spectrum output
ft_warning('The spike datatype format you are using is depreciated. Converting to newer spike format');
spike.trial = {spike.origtrial};
spike = rmfield(spike,'origtrial');
spike.time = {spike.origtime};
spike = rmfield(spike,'origtime');
if ~isa(spike.fourierspctrm, 'cell')
spike.fourierspctrm = {spike.fourierspctrm};
end
if ~isfield(spike, 'trialtime')
% determine from the data itself
ft_warning('Reconstructing the field trialtime from spike.origtime and spike.origtrial. This is not the original representation');
tmax = nanmax(spike.trial{1});
tsmin = nanmin(spike.time{1});
tsmax = nanmax(spike.time{1});
spike.trialtime = [tsmin*ones(tmax,1) tsmax*ones(tmax,1)];
end
spike.lfplabel = spike.label; % in the old format, these were the lfp channels
try
spike.label = spike.cfg.spikechannel;
catch
try
spike.label = spike.spikechannel;
catch
spike.label = {'unit1'}; %default
end
end
spike.dimord = '{chan}_spike_lfpchan_freq';
end
% fix the waveform dimensions
if isfield(spike,'waveform')
nUnits = length(spike.waveform);
hasdat = false(1,nUnits);
for iUnit = 1:nUnits
hasdat(iUnit) = ~isempty(spike.waveform{iUnit});
end
if any(hasdat) %otherwise, ignore
if ~isfield(spike, 'dimord')
spike.dimord = '{chan}_lead_time_spike';
end
% fix the dimensions of the waveform dimord.
for iUnit = 1:nUnits
dim = size(spike.waveform{iUnit});
if length(dim)==2 && ~isempty(spike.waveform{iUnit})
nSpikes = length(spike.timestamp{iUnit}); % check what's the spike dimension from the timestamps
spikedim = dim==nSpikes;
if isempty(spikedim)
ft_error('waveforms contains data but number of waveforms does not match number of spikes');
end
if spikedim==1
spike.waveform{iUnit} = permute(spike.waveform{iUnit},[3 2 1]);
else
spike.waveform{iUnit} = permute(spike.waveform{iUnit},[3 1 2]);
end
elseif length(dim)==3 && ~isempty(spike.waveform{iUnit})
nSpikes = length(spike.timestamp{iUnit}); % check what's the spike dimension from the timestamps
spikedim = dim==nSpikes;
% determine from the remaining dimensions which is the lead
leaddim = dim<6 & dim~=nSpikes;
sampdim = dim>=6 & dim~=nSpikes;
if isempty(spikedim)
ft_error('waveforms contains data but number of waveforms does not match number of spikes');
end
if sum(leaddim)~=1 || sum(sampdim)~=1, continue,end % in this case we do not know what to do
if find(spikedim)~=3 && find(leaddim)~=1 && find(sampdim)~=2
spike.waveform{iUnit} = permute(spike.waveform{iUnit}, [find(leaddim) find(sampdim) find(spikedim)]);
end
end
end
end
end
% ensure that we always deal with row vectors: for consistency of
% representation
if isfield(spike,'time')
for iUnit = 1:length(spike.time)
if size(spike.time{iUnit},2)==1
spike.time{iUnit} = spike.time{iUnit}(:)';
end
end
end
if isfield(spike,'time')
for iUnit = 1:length(spike.trial)
if size(spike.trial{iUnit},2)==1
spike.trial{iUnit} = spike.trial{iUnit}(:)';
end
end
end
if isfield(spike,'timestamp')
for iUnit = 1:length(spike.timestamp)
if size(spike.timestamp{iUnit},2)==1
spike.timestamp{iUnit} = spike.timestamp{iUnit}(:)';
end
end
end
if isfield(spike,'unit')
for iUnit = 1:length(spike.unit)
if size(spike.unit{iUnit},2)==1
spike.unit{iUnit} = spike.unit{iUnit}(:)';
end
end
end
otherwise
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ft_error('unsupported version "%s" for spike datatype', version);
end
spike = sortfields(spike);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function s2 = sortfields(s1)
s2 = struct();
fn = sort(fieldnames(s1));
for i=1:numel(fn)
s2.(fn{i}) = s1.(fn{i});
end