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function [sts] = ft_spiketriggeredspectrum_fft(cfg, data, spike)
% FT_SPIKETRIGGEREDSPECTRUM_FFT computes the Fourier spectrum (amplitude and phase)
% of the LFP around the % spikes. A phase of zero corresponds to the spike being on
% the peak of the LFP oscillation. A phase of 180 degree corresponds to the spike being
% in the through of the oscillation. A phase of 45 degrees corresponds to the spike
% being just after the peak in the LFP.
% If the triggered spike leads a spike in another channel, then the angle of the Fourier
% spectrum of that other channel will be negative. Earlier phases are in clockwise
% direction.
% Use as
% [sts] = ft_spiketriggeredspectrum_convol(cfg,data,spike)
% or
% [sts] = ft_spiketriggeredspectrum_convol(cfg,data)
% where the spike data can either be contained in the DATA input or in the SPIKE input.
% The input DATA should be organised as the raw datatype, obtained from FT_PREPROCESSING
% The (optional) input SPIKE should be organised as the spike or the raw datatype,
% obtained from FT_SPIKE_MAKETRIALS or FT_PREPROCESSING (in that case, conversion is done
% within the function)
% Important is that data.time and spike.trialtime should be referenced relative to the
% same trial trigger times.
% The configuration should be according to
% cfg.timwin = [begin end], time around each spike (default = [-0.1 0.1])
% cfg.foilim = [begin end], frequency band of interest (default = [0 150])
% cfg.taper = 'dpss', 'hanning' or many others, see WINDOW (default = 'hanning')
% cfg.tapsmofrq = number, the amount of spectral smoothing through
% multi-tapering. Note that 4 Hz smoothing means plus-minus 4 Hz,
% i.e. a 8 Hz smoothing box. Note: multitapering rotates phases (no
% problem for consistency)
% cfg.spikechannel = string, name of spike channels to trigger on = Nx1
% cell-array with selection of channels (default = 'all'),
% see FT_CHANNELSELECTION for details
% = 'no', 'text', 'textbar', 'gui' (default = 'no')
% The output STS data structure can be input to FT_SPIKETRIGGEREDSPECTRUM_STAT
% This function uses a NaN-aware spectral estimation technique, which will default to the
% standard Matlab FFT routine if no NaNs are present. The fft_along_rows subfunction below
% demonstrates the expected function behavior.
% See FT_SPIKETRIGGEREDINTERPOLATION to remove segments of LFP around spikes.
% See FT_SPIKETRIGGEREDSPECTRUM_CONVOL for an alternative implementation based
% on convolution
% Copyright (C) 2008, Robert Oostenveld
% This file is part of FieldTrip, see
% 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
% 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 <>.
% $Id$
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_preamble init
ft_preamble provenance data spike
% check input data structure
data = ft_checkdata(data,'datatype', 'raw', 'feedback', 'yes');
if nargin==3
spike = ft_checkdata(spike, 'datatype', {'spike'}, 'feedback', 'yes');
% these were supported in the past, but are not any more (for consistency with other spike functions)
cfg = ft_checkconfig(cfg, 'forbidden', {'inputfile','outputfile'});
%get the options
cfg.timwin = ft_getopt(cfg, 'timwin',[-0.1 0.1]);
cfg.spikechannel = ft_getopt(cfg,'spikechannel', 'all'); = ft_getopt(cfg,'channel', 'all'); = ft_getopt(cfg,'feedback', 'no');
cfg.tapsmofrq = ft_getopt(cfg,'tapsmofrq', 4);
cfg.taper = ft_getopt(cfg,'taper', 'hanning');
cfg.foilim = ft_getopt(cfg,'foilim', [0 150]);
% ensure that the options are valid
cfg = ft_checkopt(cfg,'timwin','doublevector');
cfg = ft_checkopt(cfg,'spikechannel',{'cell', 'char', 'double', 'empty'});
cfg = ft_checkopt(cfg,'channel', {'cell', 'char', 'double'});
cfg = ft_checkopt(cfg,'feedback', 'char', {'yes', 'no'});
cfg = ft_checkopt(cfg,'taper', 'char');
cfg = ft_checkopt(cfg,'tapsmofrq', 'doublescalar');
cfg = ft_checkopt(cfg,'foilim', 'doublevector');
if strcmp(cfg.taper, 'sine')
error('sorry, sine taper is not yet implemented');
% get the spikechannels
if nargin==2
% autodetect the spikechannels and EEG channels
[spikechannel, eegchannel] = detectspikechan(data);
% make the final selection of spike channels and check
if strcmp(cfg.spikechannel, 'all'),
cfg.spikechannel = spikechannel;
cfg.spikechannel = ft_channelselection(cfg.spikechannel, data.label);
if ~all(ismember(cfg.spikechannel,spikechannel)),
error('some selected spike channels appear eeg channels');
% make the final selection of EEG channels and check
if strcmp(,'all') = eegchannel;
else = ft_channelselection(, data.label);
if ~all(ismember(,eegchannel)),
warning('some of the selected eeg channels appear spike channels');
% select the data and convert to a spike structure
tmpcfg = []; = cfg.spikechannel;
data_spk = ft_selectdata(tmpcfg, data); =;
data = ft_selectdata(tmpcfg, data); % leave only LFP
spike = ft_checkdata(data_spk,'datatype', 'spike');
clear data_spk % remove the continuous data
cfg.spikechannel = ft_channelselection(cfg.spikechannel, spike.label); = ft_channelselection(, data.label);
% determine the channel indices and number of chans
chansel = match_str(data.label,; % selected channels
nchansel = length(; % number of channels
spikesel = match_str(spike.label, cfg.spikechannel);
nspikesel = length(spikesel); % number of spike channels
if nspikesel==0, error('no spike channel selected'); end
% construct the taper
if ~isfield(data, 'fsample'), data.fsample = 1/mean(diff(data.time{1})); end
begpad = round(cfg.timwin(1)*data.fsample);
endpad = round(cfg.timwin(2)*data.fsample);
numsmp = endpad - begpad + 1;
if ~strcmp(cfg.taper,'dpss')
taper = window(cfg.taper, numsmp);
taper = taper./norm(taper);
% not implemented yet: keep tapers, or selecting only a subset of them.
taper = dpss(numsmp, cfg.tapsmofrq);
taper = taper(:,1:end-1); % we get 2*NW-1 tapers
taper = sum(taper,2)./size(taper,2); % using the linearity of multitapering
taper = sparse(diag(taper));
% preallocate the output structures for different units / trials
ntrial = length(data.trial);
spectrum = cell(nspikesel,ntrial);
spiketime = cell(nspikesel,ntrial);
spiketrial = cell(nspikesel,ntrial);
% select the frequencies
freqaxis = linspace(0, data.fsample, numsmp);
fbeg = nearest(freqaxis, cfg.foilim(1));
fend = nearest(freqaxis, cfg.foilim(2));
% update the configuration to account for rounding off differences
cfg.foilim(1) = freqaxis(fbeg);
cfg.foilim(2) = freqaxis(fend);
% make a representation of the spike, this is used for the phase rotation
spike_repr = zeros(1,numsmp);
time = linspace(cfg.timwin(1),cfg.timwin(2), numsmp);
spike_repr(1-begpad) = 1;
spike_fft = specest_nanfft(spike_repr, time);
spike_fft = spike_fft(fbeg:fend);
spike_fft = spike_fft./abs(spike_fft);
rephase = sparse(diag(conj(spike_fft)));
% compute the spectra
ft_progress('init', 'text', 'Please wait...');
for iUnit = 1:nspikesel
for iTrial = 1:ntrial
% select the spikes that fell in the trial and convert to samples
timeBins = [data.time{iTrial} data.time{iTrial}(end)+1/data.fsample] - (0.5/data.fsample);
hasTrial = spike.trial{spikesel(iUnit)} == iTrial; % find the spikes that are in the trial
ts = spike.time{spikesel(iUnit)}(hasTrial); % get the spike times for these spikes
ts = ts(ts>=timeBins(1) & ts<=timeBins(end)); % only select those spikes that fall in the trial window
[ignore,spikesmp] = histc(ts,timeBins);
if ~isempty(ts)
ts(spikesmp==0 | spikesmp==length(timeBins)) = [];
spikesmp(spikesmp==0 | spikesmp==length(timeBins)) = [];
% store in the output cell arrays as column vectors
spiketime{iUnit, iTrial} = ts(:);
tr = iTrial*ones(size(spikesmp));
spiketrial{iUnit, iTrial} = tr(:);
% preallocate the spectrum
spectrum{iUnit, iTrial} = zeros(length(spikesmp), nchansel, fend-fbeg+1);
% compute the spiketriggered spectrum
ft_progress(iTrial/ntrial, 'spectrally decomposing data for trial %d of %d, %d spikes for unit %d', iTrial, ntrial, length(spikesmp), iUnit);
for j=1:length(spikesmp)
% selected samples
begsmp = spikesmp(j) + begpad;
endsmp = spikesmp(j) + endpad;
% handle spikes near the borders of the trials
if (begsmp<1)
segment = nan(nchansel, numsmp);
elseif endsmp>size(data.trial{iTrial},2)
segment = nan(nchansel, numsmp);
segment = data.trial{iTrial}(chansel,begsmp:endsmp);
% substract the DC component from every segment, to avoid any leakage of the taper
segmentMean = repmat(nanmean(segment,2),1,numsmp); % nChan x Numsmp
segment = segment - segmentMean; % LFP has average of zero now (no DC)
% taper the data segment around the spike and compute the fft
segment_fft = specest_nanfft(segment * taper, time);
% select the desired output frquencies and normalize
segment_fft = segment_fft(:,fbeg:fend) ./ sqrt(numsmp/2);
% rotate the estimated phase at each frequency to correct for the segment t=0 not being at the first sample
segment_fft = segment_fft * rephase;
% store the result for this spike in this trial
spectrum{iUnit, iTrial}(j,:,:) = segment_fft;
end % for each spike in this trial
end % for each trial
% collect the results in a structure that is a spike structure
sts.lfplabel = data.label(chansel);
sts.freq = freqaxis(fbeg:fend);
sts.dimord = 'rpt_chan_freq';
for iUnit = 1:nspikesel
sts.fourierspctrm{iUnit} = cat(1, spectrum{iUnit,:});
spectrum(iUnit,:) = {[]}; % free from the memory
sts.time{iUnit} = cat(1,spiketime{iUnit,:});
sts.trial{iUnit} = cat(1,spiketrial{iUnit,:});
sts.dimord = '{chan}_spike_lfpchan_freq';
sts.trialtime = spike.trialtime;
sts.label = spike.label(spikesel);
% do the general cleanup and bookkeeping at the end of the function
ft_postamble previous data
ft_postamble provenance sts
ft_postamble history sts
function [spikelabel, eeglabel] = detectspikechan(data)
maxRate = 1000; % default on what we still consider a neuronal signal
% autodetect the spike channels
ntrial = length(data.trial);
nchans = length(data.label);
spikechan = zeros(nchans,1);
for i=1:ntrial
for j=1:nchans
hasAllInts = all(isnan(data.trial{i}(j,:)) | data.trial{i}(j,:) == round(data.trial{i}(j,:)));
hasAllPosInts = all(isnan(data.trial{i}(j,:)) | data.trial{i}(j,:)>=0);
fr = nansum(data.trial{i}(j,:),2) ./ (data.time{i}(end)-data.time{i}(1));
spikechan(j) = spikechan(j) + double(hasAllInts & hasAllPosInts & fr<=maxRate);
spikechan = (spikechan==ntrial);
spikelabel = data.label(spikechan);
eeglabel = data.label(~spikechan);
% SUBFUNCTION for demonstration purpose
function y = fft_along_rows(x)
y = fft(x, [], 2); % use normal Matlab function to compute the fft along 2nd dimension