function [cfg] = ft_badchannel(cfg, data)

% FT_BADCHANNEL tries to identify bad channels in a MEG or EEG dataset. Different

% methods are implemented to identify bad channels, these are largely shared with

% those implemented in FT_REJECTVISUAL with the summary method. The methods are

% shortly described in detail below.

%

% VAR, STD, MIN, MAX, MAXABS, RANGE, KURTOSIS, ZVALUE - compute the specified metric

% for each channel in each trial and check whether it exceeds the threshold.

%

% NEIGHBEXPVAR - identifies channels that cannot be explained very well by a linear

% combination of their neighbours. A general linear model is used to compute the

% explained variance. A value close to 1 means that a channel is similar to its

% neighbours, a value close to 0 indicates a "bad" channel.

%

% NEIGHBCORR - identifies channels that have low correlation with each of their

% neighbours. The rationale is that "bad" channel have inherent noise that is

% uncorrelated with other sensors.

%

% NEIGHBSTDRATIO - identifies channels that have a standard deviation which is very

% different from that of each of their neighbours. This computes the difference in

% the standard deviation of each channel to each of its neighbours, relative to that

% of the neighbours.

%

% Use as

% [cfg] = ft_badchannel(cfg, data)

% where the input data corresponds to the output from FT_PREPROCESSING.

%

% The configuration should contain

% cfg.metric = string, describes the metric that should be computed in summary mode for each channel in each trial, can be

% 'var' variance within each channel (default)

% 'std' standard deviation within each channel

% 'mad' median absolute deviation within each channel

% '1/var' inverse variance within each channel

% 'min' minimum value in each channel

% 'max' maximum value in each channel

% 'maxabs' maximum absolute value in each channel

% 'range' range from min to max in each channel

% 'kurtosis' kurtosis, i.e. measure of peakedness of the amplitude distribution

% 'zvalue' mean and std computed over all time and trials, per channel

% cfg.threshold = scalar, the optimal value depends on the methods and on the data characteristics

% cfg.neighbours = neighbourhood structure, see FT_PREPARE_NEIGHBOURS for details

% cfg.nbdetect = 'any', 'most', 'all', 'median', see below (default = 'median')

% cfg.feedback = 'yes' or 'no', whether to show an image of the neighbour values (default = 'no')

%

% The following options allow you to make a pre-selection

% cfg.channel = Nx1 cell-array with selection of channels (default = 'all'), see FT_CHANNELSELECTION for details

% cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all')

%

% The 'neighcorrel' and 'neighstdratio' methods implement the bad channel detection

% (more or less) according to the paper "Adding dynamics to the Human Connectome

% Project with MEG", Larson-Prior et al. https://doi.org/10.1016/j.neuroimage.2013.05.056.

%

% Most methods compute a scalar value for each channel that can simply be

% thresholded. The NEIGHBCORR and NEIGHBSTDRATIO compute a vector with a value for

% each of the neighbour of a channel. The cfg.nbdetect option allows you to specify

% whether you want to flag the channel as bad in case 'all' of its neighbours exceed

% the threshold, if 'most' exceed the threshold, or if 'any' of them exceeds the

% threshold. Note that when you specify 'any', then all channels neighbouring a bad

% channel will also be marked as bad, since they all have at least one bad neighbour.

% You can also specify 'median', in which case the threshold is applied to the median

% value over neighbours.

%

% See also FT_BADSEGMENT, FT_REJECTVISUAL, FT_CHANNELREPAIR

% Undocumented options

% cfg.thresholdside = above or below

% Copyright (C) 2021, Robert Oostenveld

%

% 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/>.

%

% $Id$

% 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_defaults

ft_preamble init

ft_preamble debug

ft_preamble loadvar data

ft_preamble provenance

% the ft_abort variable is set to true or false in ft_preamble_init

if ft_abort

return

end

% check if the input data is valid for this function

data = ft_checkdata(data, 'datatype', 'raw', 'feedback', 'yes');

% check if the input cfg is valid for this function

cfg = ft_checkconfig(cfg, 'forbidden', {'channels', 'trial'}); % prevent accidental typos, see issue 1729

cfg = ft_checkconfig(cfg, 'required', 'metric');

% ensure that the preproc specific options are located in the cfg.preproc substructure

cfg = ft_checkconfig(cfg, 'createsubcfg', {'preproc'});

% set the defaults

cfg.channel = ft_getopt(cfg, 'channel', 'all');

cfg.trials = ft_getopt(cfg, 'trials', 'all', true);

cfg.neighbours = ft_getopt(cfg, 'neighbours');

cfg.nbdetect = ft_getopt(cfg, 'nbdetect', 'median');

cfg.feedback = ft_getopt(cfg, 'feedback', 'no');

cfg.thresholdside = ft_getopt(cfg, 'thresholdside', []); % the default depends on cfg.metric, see below

if isempty(cfg.thresholdside)

if ismember(cfg.metric, {'var', 'std', 'max', 'maxabs', 'range', 'kurtosis', '1/var', 'zvalue', 'maxzvalue', 'neighbstdratio'})

% large positive values indicate an artifact, so check for values ABOVE the threshold

cfg.thresholdside = 'above';

elseif ismember(cfg.metric, {'min', 'neighbexpvar', 'neighbcorr'})

% very negative values or small positive values indicate an artifact, so check for values BELOW the threshold

cfg.thresholdside = 'below';

else

% there are also a few where one could look at either side, these require the user to make a choice

ft_error('you must specify cfg.thresholdside');

end

end

% select trials and channels of interest

tmpcfg = keepfields(cfg, {'trials', 'channel', 'tolerance', 'latency', 'showcallinfo', 'trackcallinfo', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo', 'checksize'});

data = ft_selectdata(tmpcfg, data);

% restore the provenance information

[cfg, data] = rollback_provenance(cfg, data);

ntrl = length(data.trial);

nchan = length(data.label);

badchannel = false(nchan,1);

if contains(cfg.metric, 'zvalue')

% cellmean and cellstd (see FT_DENOISE_PCA) would work instead of for-loops, but they are too memory-intensive

runsum = zeros(nchan, 1);

runss = zeros(nchan, 1);

runnum = 0;

for chan=1:ntrl

dat = preproc(data.trial{chan}, data.label, data.time{chan}, cfg.preproc);

runsum = runsum + nansum(dat, 2);

runss = runss + nansum(dat.^2, 2);

runnum = runnum + sum(isfinite(dat), 2);

end

mval = runsum./runnum;

sd = sqrt(runss./runnum - (runsum./runnum).^2);

else

mval = [];

sd = [];

end

if contains(cfg.metric, 'neighb')

cfg = ft_checkconfig(cfg, 'required', 'neighbours');

% creates a NxN Boolean matrix that describes whether channels are connected as neighbours

connectivity = channelconnectivity(cfg, data);

else

connectivity = [];

end

for trl=1:ntrl

% compute the artifact value for each channel in this trial

level = artifact_level(data.trial{trl}, cfg.metric, mval, sd, connectivity);

if isvector(level)

% find channels with a value that exceeds the threshold

switch cfg.thresholdside

case 'below'

badchannel = badchannel | level<cfg.threshold;

case 'above'

badchannel = badchannel | level>cfg.threshold;

end

else

% identify channels with one of their neighbours values that exceeds the threshold

for chan=1:nchan

nblevel = level(chan,:); % select this channel from the matrix

nblevel = nblevel(~isnan(nblevel)); % only select its actual neighbours

switch cfg.nbdetect

case 'all'

switch cfg.thresholdside

case 'below'

badchannel(chan) = badchannel(chan) | all(nblevel<cfg.threshold);

case 'above'

badchannel(chan) = badchannel(chan) | all(nblevel>cfg.threshold);

end % switch

case 'most'

switch cfg.thresholdside

case 'below'

badchannel(chan) = badchannel(chan) | most(nblevel<cfg.threshold);

case 'above'

badchannel(chan) = badchannel(chan) | most(nblevel>cfg.threshold);

end % switch

case 'any'

switch cfg.thresholdside

case 'below'

badchannel(chan) = badchannel(chan) | any(nblevel<cfg.threshold);

case 'above'

badchannel(chan) = badchannel(chan) | any(nblevel>cfg.threshold);

end % switch

case 'median'

switch cfg.thresholdside

case 'below'

badchannel(chan) = badchannel(chan) | nanmedian(nblevel,2)<cfg.threshold;

case 'above'

badchannel(chan) = badchannel(chan) | nanmedian(nblevel,2)>cfg.threshold;

end % switch

otherwise

ft_error('incorrect specification of cfg.nbdetect');

end

end % for each channel

end % if isvector

end % for each trial

ft_info('identified %d out of %d channels as bad\n', sum(badchannel), length(badchannel));

% keep track of bad channels

cfg.badchannel = data.label(badchannel);

% do the general cleanup and bookkeeping at the end of the function

ft_postamble debug

ft_postamble previous data

ft_postamble provenance

ft_postamble hastoolbox

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% SUBFUNCTION

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function tf = most(x)

tf = sum(x(:)==true)>(numel(x)/2);