cosmo meeg dataset hdrΒΆ

function ds=cosmo_meeg_dataset(filename, varargin)
% Returns a dataset structure based on MEEG data
% ds=cosmo_meeg_dataset(filename, varargin)
% Inputs:
%   filename          filename of MEEG data to be loaded. Currently
%                     supported are files with extensions:
%                       .mat :        FieldTrip time-locked or
%                                     time-frequency  data at  either the
%                                     sensor or source level.
%                       .txt :        exported EEGLab with time-locked
%                                     data.
%                       .daterp       time-locked               }
%                       .icaerp       ICA time-locked           } EEGLab
%                       .dattimef     time-freq                 }
%                       .icatimef     ICA time-freq             }
%                       .datitc       inter-trial coherence     }
%                       .icaitc       ICA inter-trial coherence }
%                       .datersp      ERSP data                 }
%                       .icaersp      ICA ERSP data             }
%                     Alternatively it can be a FieldTrip or EEGLab struct
%                     with time-locked or time-frequency data
%   'targets', t      Px1 targets for P samples; these will be stored in
%                     the output as (optional)
%   'chunks', c       Px1 chunks for P samples; these will be stored in the
%                     the output as (optional)
%   'data_field', f   - For FieldTrip MEEG source dataset with multiple
%                       data fields (such as 'pow' and 'mom'), this sets
%                       which data is returned. (only for source data)
%                     - For EEGLAB 'ersp' data
%                        f='ersp'       the original data is returned
%                                       (without baseline correction),
%                                       with data for each channel,
%                                       frequency and time point.
%                                       Based on datasets generated by
%                                       EEGLAB that were inspected when
%                                       writing this function, it seems
%                                       that this data represents raw power
%                                       values.
%                        f='erspbase'   the baseline is returned, with data
%                                       for each channel and frequency
%                                       (but not time point)
%                        Note: this function does currently not support
%                              returning baseline-corrected data.
%   'trials', idx     Mx1 array with indices of trials to load (optional).
%                     If not provided then all trials are loaded. The
%                     output has a .samples field with the number of rows
%                     equal to numel(idx).
% Returns:
%   ds                dataset struct with the following fields
%     .samples        PxQ for P samples and Q features.
%     .sa.targets     Px1 sample targets (if provided)
%     .sa.chunks      Px1 sample chunks (if provided)
%     .a
%       .meeg
%         .sample_field   name of sample field. One of 'fourierspctrm',
%                         'powspctrm', or 'trial'.
%         .samples_type   'timelock' or 'timefreq'.
%         .samples_label  Usually 'rpt'; or the first field of .dimord
%                         for FieldTrip data
%       .dim
%         .labels     1xS cell struct with labels for the feature
%                     dimensions of the input. Usually this is
%                     {'chan','time'} or {'chan','freq','time'}.
%         .values     1xS cell struct with values associated with .labels.
%                     If the K-th value has N_K values, this means that
%                     the feature dimension .labels{K} takes the
%                     values in .values{K}. For example, if
%                     .labels{1}=='chan', then .values{1} contains the
%                     channel labels.
%     .fa
%       .{D}          if D==a.fdim.labels{K} is the label for the K-th
%                     feature dimension, then .{D} contains the
%                     indices referencing a.fdim.values. Thus, all values in
%                     .{D} are in the range 1:N_K if a.fdim.values{K} has
%                     N_K values, and the J-th feature has dimension value
%                     .dim.values{K}(.{D}(J)) in the K-th dimension.
% Notes:
%  - The resulting dataset can be mapped back to MEEG format using
%    cosmo_map2meeg
%  - if the input contains data from a single sample (such as an average)
%    the .sample_field is set to .trial, and mapping back to MEEG format
%    adds a singleton dimension to the .trial data output field.
%  - For single-subject MVPA of single trials using data preprocessed with
%    FieldTrip, consider setting, depending on the data type:
%       * timelock (ft_timelockanalysis): cfg.keeptrials = 'yes'
%       * timefreq (ft_timefreqanalysis): cfg.keeptrials = 'yes'
%       * source   (ft_sourceanalysis)  : cfg.keeptrials = 'yes' *and*
%                                                   cfg.rawtrials = 'yes'
%  - Most MVPA applications require that .sa.targets (experimental
%    condition of each sample) and .sa.chunks (partitioning of the samples
%    in independent sets) are set, either by using this function or
%    manually afterwards.
%  - If the input is a FieldTrip struct with a field .trialinfo, then this
%    field is present in .sa.trialinfo. Depending on the contents of
%    .trialinfo, this could be used to specify conditions in each trial.
%    For example, if the third column of .trialinfo contains an integer
%    specifying the condition of each trial, after running this function
%    one can do
%    to set the trial conditions.
%  - Implementation note: when loading EEGLAB data from a file, using the
%    'trials' option means that data from different channels are loaded
%    through different 'load' commands. When loading a subset of all
%    trials, the advantage of this implementation is that significant
%    less memory is needed compared to an alternative implementation in
%    which the full dataset is loaded and then the trials of interest
%    are selected through slicing. The disadvantage is that loading may
%    take longer, because the file is opened and closed multiple times. yet
%    this approach allows one to load subsets of trials from data files
%    that are larger than the available RAM.
%    Such memory reductions are currently not available for FieldTrip
%    data, as FieldTrip's data structures do not store data for different
%    channels in different variables.
% See also: cosmo_map2meeg
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #