The `Pipeline`

Introduction 

The Pipeline manages the following analysis steps:

Preprocessing
Epoching
Optional source localization
Mass univariate group-level statistics

The input to the pipeline are the raw M/EEG data files and, optionally, MRI files for source localization. The first three steps are based on mne functions; statistics are based on Eelbrain functions. The pipeline automatizes the complete analysis, and provides an interface for preprocessing steps that require user intervention like ICA. It allows access to the data at any intermediate stage, to allow for customizing the analysis. It caches intermediate results to make access to these data fast and efficient.

Pipeline is a template for the pipeline. This template is adapted to a specific experiment by specifying properties of the experiment as attributes (technically, by creating a subclass). An instance of this pipeline then provides access to different analysis stages through its methods:

.load_... methods are for loading data and results. Most of these return Eelbrain data types by default, but they can be used to load mne objects by setting ndvar=False (e.g., Pipeline.load_epochs()).

.show_... methods are for retrieving and displaying information at different stages.

.plot_... methods are for generating plots of the data.

.make_... methods are for generating various intermediate results. Most of these methods do not have to be called by the user, as they are invoked automatically when needed. An exception are those that require user input, like ICA component selection, which are mentioned below.

For example, Pipeline.load_test() can be used to directly load a mass-univariate test result, without a need to explicitly load data at any intermediate stage. On the other hand, Pipeline.load_epochs() can be used to load the corresponding data epochs, for example to perform a different analysis that may not be implemented in the pipeline.

Step by Step 

Setting up the file structure 

The pipeline expects input dataset in BIDS (Brain Imaging Data Structure) format. (To convert your data into BIDS format, use the MNE-BIDS <https://mne.tools/mne-bids/stable/use.html>_ library.) In the schema below, curly brackets indicate slots that the pipeline will replace with specific names:

root                              {root}
subject folder                       /sub-{subject}
session folder                          /ses-{session}
datatype folder                            /{datatype}
raw data file                                 /sub-{subject}_ses-{session}_task-{task}_run-{run}_{datatype}.fif
derivatives root                     /derivatives
trans file                              /trans/sub-{subject}_ses-{session}_{datatype}_trans.fif
FreeSurfer SUBJECTS_DIR                 /freesurfer
mri for each subject                       /sub-{subject}
mri for template brain                     /fsaverage
Eelbrain generated files                /eelbrain

Note

In BIDS specification, {root}/derivatives is for files that do not fit into the BIDS structure, such as FreeSurfer MRIs and Eelbrain-generated files.

{subject}, {session}, {task} and {run} are BIDS entities. {session} and {run} are optional. {datatype} is inferred by the pipeline from the data files, and can be 'meg' or 'eeg'. Apart from the common entities shown above, there can be other ones depending on your dataset, such as acquisition or split.

MRI files (including trans-file) are optional and only needed for source localization. The {root}/derivatives/freesurfer directory is FreeSurfer subject directory. They either contain the files created by FreeSurfer’s recon-all command, or are created by the MNE-Python coregistration utility for scaled template brains. An fsaverage folder can be used to store the template brain. Note that the pipeline doesn’t use the NIfTI format that BIDS specifies. A corresponding trans-file is created with the MNE-Python coregistration utility in either case (see more information on using structural MRIs or the fsaverage template brain).

A BIDS dataset can be scanned by initializing a Pipeline with the data {root} location, for example:

e = Pipeline("~/Data/Experiment")

Assuming a subject without explicit {session} is named “S001”, the pipeline will look for data at the following locations:

The raw data file at ~/Data/Experiment/sub-S001/meg/sub-S001_task-words_meg.fif
The trans-file from the coregistration at ~/Data/Experiment/derivatives/trans/sub-S001_meg_trans.fif
The FreeSurfer MRI-directory at ~/Data/Experiment/derivatives/freesurfer/sub-S001
The template brain MRI-directory at ~/Data/Experiment/derivatives/freesurfer/fsaverage

The setup can be tested using Pipeline.show_subjects(), which shows a list of the subjects and corresponding MRIs that were discovered:

>>> e.show_subjects()
#    subject   mri
-----------------------------------------
0    R0026     R0026
1    R0040     fsaverage * 0.92
2    R0176     fsaverage * 0.954746600461
...

Setting up the analysis code 

It is recommended to organize analysis scripts in a dedicated folder. For example, we will assume that all analysis scripts will be saved in a directory called ~/Code/MyProject. This makes it easy to keep track of the history of this folder, for example using Git.

The analysis scripts will consist of two components:

A Pipeline subclass which describes the general experiment structure (MyExperiment below).
Analysis scripts (or Jupyter notebooks) using this subclass.

You will want to access the Pipeline subclass (MyExperiment) from different locations (for instance, from a terminal to do artifact rejection, and from different Jupyter Notebooks to pursue different analyses). Thus, it makes sense to define the experiment subclass in a separate Python file (e.g., MyProject/my_experiment.py), and run or import that file as needed. Thus, MyProject/my_experiment.py may look like this:

from eelbrain.pipeline import *

class MyExperiment(Pipeline):

    # Define experiment attributes here

e = MyExperiment("~/Data/Experiment")

From a terminal, this could then be used as follows:

~/Code/MyProject $ eelbrain  # eelbrain on macOS; iPython on Linux
In [1]: run my_experiment.py
In [2]: e.show_subjects()
#    subject   mri
-----------------------------------------
0    R0026     R0026
1    R0040     fsaverage * 0.92
2    R0176     fsaverage * 0.954746600461
...

Similarly, you can run my_experiment.py in the first cell of a Jupyter Notebook that is saved in the same folder.

Note

If your project contains Jupyter Notebooks, consider Jupytext to efficiently track those notebooks in Git.

Pre-processing 

Make sure an appropriate pre-processing pipeline is defined as Pipeline.raw.

To inspect raw data for a given pre-processing step use:

>>> e.set(raw='1-40')
>>> y = e.load_raw(ndvar=True)
>>> p = plot.TopoButterfly(y, xlim=10, w=0)

Which will plot a 10 s excerpt and allow scrolling through the rest of the data.

Events 

If needed, set Pipeline.merge_triggers to handle spurious events. Then, add event labels. Initially, events are only labeled with the trigger ID. Use the Pipeline.variables settings to add labels. Events are represented as Dataset objects and can be inspected with corresponding methods and functions, for example:

>>> e = MyExperiment("~/Data/Experiment")
>>> data = e.load_events()
>>> data.head()
>>> print(table.frequencies('trigger', data=data))

For more complex designs and variables, you can override methods that provide complete control over the events. These are the transformations applied to the triggers extracted from raw files (in this order):

Pipeline.fix_events(): Change event order, timing and remove/add events

Pipeline.variables: Add labels based on triggers

Pipeline.label_events(): Add any more complex labels

Defining data epochs 

Once events are properly labeled, define Pipeline.epochs.

There is one special epoch to define, which is called 'cov'. This is the data epoch that will be used to estimate the sensor noise covariance matrix for source estimation.

In order to find the right sel epoch parameter, it can be useful to actually load the events with Pipeline.load_events() and test different selection strings. The epoch selection is determined by selection = event_ds.eval(epoch['sel']). Thus, a specific setting could be tested with:

>>> data = e.load_events()
>>> print(data.sub("event == 'value'"))

Bad channels 

Flat channels are automatically excluded from the analysis.

An initial check for noisy channels can be done by looking at the raw data (see Pre-processing above). If this inspection reveals bad channels, they can be excluded using Pipeline.make_bad_channels().

Another good check for bad channels is plotting the average evoked response, and looking for channels which are uncorrelated with neighboring channels. To plot the average before trial rejection, use:

>>> data = e.load_epochs(epoch='epoch', reject=False)
>>> plot.TopoButterfly('meg', data=data)

The neighbor correlation can also be quantified, using:

>>> nc = neighbor_correlation(concatenate(data['meg']))
# Plot topographical map of the neighbor correlation
>>> plot.Topomap(nc)
# Check for channels whose average correlation with its neighbors is < 0.3
>>> nc.sensor.names[nc < 0.3]
Datalist(['MEG 099'])
# Remove that channel
>>> e.make_bad_channels(['MEG 099'])

A simple way to cycle through subjects when performing a manual pre-processing step is Pipeline.next().

If a general threshold is adequate, the selection of bad channels based on neighbor-correlation can be automated using the Pipeline.make_bad_channels_neighbor_correlation() method:

>>> for subject in e:
...     e.make_bad_channels_neighbor_correlation(0.3)

ICA 

If preprocessing includes ICA, select which ICA components should be removed. To open the ICA selection GUI, The experiment raw state needs to be set to the ICA stage of the pipeline:

>>> e.set(raw='ica')
>>> e.make_ica_selection()

See Pipeline.make_ica_selection() for more information on display options and on how to precompute ICA decomposition for all subjects.

When selecting ICA components for multiple subject, a simple way to cycle through subjects is Pipeline.next(), like:

>>> e.make_ica_selection(epoch='epoch', decim=10)
>>> e.next()
subject: 'R1801' -> 'R2079'
>>> e.make_ica_selection(epoch='epoch', decim=10)
>>> e.next()
subject: 'R2079' -> 'R2085'
...

Trial selection 

For each primary epoch that is defined, bad trials can be rejected using Pipeline.make_epoch_selection(). Rejections are specific to a given raw state:

>>> e.set(raw='ica1-40', epoch='word')
>>> e.make_epoch_selection()
>>> e.next()
subject: 'R1801' -> 'R2079'
>>> e.make_epoch_selection()
...

To reject trials based on a pre-determined threshold, a loop can be used:

>>> for subject in e:
...     e.make_epoch_selection(auto=1e-12)
...

Empty room noise covariance 

To use empty room data for estimating the noise covariance, follow these steps:

Set up empty room data according to the instruction in BIDS specification.
Use the empty room covariance through cov with e.set(cov='emptyroom').

Analysis 

With preprocessing completed, there are different options for analyzing the data.

The most flexible option is loading data from the desired processing stage using one of the many .load_... methods of the Pipeline. For example, load a eelbrain.Dataset with source-localized condition averages using Pipeline.load_evoked_stc(), then test a hypothesis using one of the mass-univariate test from the testnd module. To make this kind of analysis replicable, it is probably useful to write the complete analysis as a separate script that imports the experiment (see the example experiment folder).

Many statistical comparisons can also be specified in the Pipeline.tests attribute, and then loaded directly using the Pipeline.load_test() method. This has the advantage that the tests will be cached automatically and, once computed, can be loaded very quickly. However, these definitions are not quite as flexible as writing a custom script.

Finally, for tests defined in Pipeline.tests, the Pipeline can generate HTML report files. These are generated with the Pipeline.make_report() and Pipeline.make_report_rois() methods.

Warning

If source files are changed (raw files, epoch rejection or bad channel files, …) reports are not updated automatically unless the corresponding Pipeline.make_report() function is called again. For this reason it is useful to have a script to generate all desired reports. Running the script ensures that all reports are up-to-date, and will only take seconds if nothing has to be recomputed (for an example see make-reports.py in the example experiment folder).

Example 

The following is a complete example for an experiment class definition file (the source file can be found in the Eelbrain examples folder at examples/imagenet/imagenet.py):

# skip test: data unavailable
from eelbrain.pipeline import Pipeline, RawFilter, RawICA, LabelVar, PrimaryEpoch, SecondaryEpoch, TTestOneSample, TTestRelated, ANOVA


class ImageNet(Pipeline):

    preload = True

    ignore_entities = {
        'ignore_subjects': ('02', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', 'emptyroom'),
        'ignore_sessions': ('ImageNet02', 'ImageNet03', 'ImageNet04', 'MRI'),
        'ignore_runs': ('02'),
    }

    raw = {
        '1-40': RawFilter('raw', 1, 40),
        'ica': RawICA('1-40', 'ImageNet', n_components=0.99),
    }

    variables = {
        'position': LabelVar('trigger', {1: 'begin', 2: 'end', (3, 4): 'middle'}),
        'event': LabelVar('trigger', {(1, 2): 'unused', 3: 'resp', 4: 'stim_on'}),
    }

    epochs = {
        'used': PrimaryEpoch('ImageNet', "position == 'middle'", samplingrate=200),
        'resp': SecondaryEpoch('used', "event == 'resp'"),
        'stim_on': SecondaryEpoch('used', "event == 'stim_on'"),
        'cov': SecondaryEpoch('used', tmax=0),
    }

    tests = {
        '=0': TTestOneSample(),
        'connection': TTestRelated('event', 'stim_on', 'resp'),
        'anova': ANOVA('event * subject'),
    }


root = '~/Data/ds005810'
e = ImageNet(root)

The event structure is illustrated by looking at the first few events:

>>> from imagenet import *
>>> data = e.load_events()
>>> data.head()
#     i_start   trigger   event     T        SOA       subject   position
-------------------------------------------------------------------------
0     2814      1         unused    2.345    5.0392    01        begin
1     8861      4         stim_on   7.3842   1.0242    01        middle
2     10090     3         resp      8.4083   0.2925    01        middle
3     10441     4         stim_on   8.7008   0.915     01        middle
4     11539     3         resp      9.6158   0.63417   01        middle
5     12300     4         stim_on   10.25    0.90167   01        middle
6     13382     3         resp      11.152   0.64833   01        middle

Experiment Definition 

Basic setup 

Pipeline.owner: str

Set Pipeline.owner to your email address if you want to be able to receive notifications. Whenever you run a sequence of commands with Pipeline.notification: you will get an email once the respective code has finished executing or run into an error, for example:

>>> e = MyExperiment()
>>> with e.notification:
...     e.make_report('mytest', tstart=0.1, tstop=0.3)
...

will send you an email as soon as the report is finished (or the program encountered an error)

Pipeline.auto_delete_results: bool

Whenever a Pipeline instance is initialized with a valid root path, it checks whether changes in the class definition invalidate previously computed results. By default, the user is prompted to confirm the deletion of invalidated results. Set auto_delete_results to True to delete them automatically without interrupting initialization.

Pipeline.auto_delete_cache: bool

Pipeline caches various intermediate results. By default, if a change in the experiment definition would make cache files invalid, the outdated files are automatically deleted. Set auto_delete_cache to 'ask' to ask for confirmation before deleting files. This can be useful to prevent accidentally deleting files that take long to compute when editing the pipeline definition. When using this option, set screen_log_level to 'debug' to learn about what change caused the cache to be invalid.

Pipeline.screen_log_level: str

Determines the amount of information displayed on the screen while using an Pipeline (see logging).

Pipeline.defaults: Dict[str, str]

The defaults dictionary can contain default settings for experiment analysis parameters (see State Parameters), e.g.:

defaults = {
    'epoch': 'my_epoch',
    'cov': 'noreg',
    'raw': '1-40',
}

Finding files 

Pipeline.ignore_entities: Dict[str, list[str]]

Exclude certain entities from the experiment, e.g.:

ignore_entities = {
    'subject': ['S666', 'S999'],
    'session': ['02'],
}

Pipeline.mri_subjects: Dict[str, Dict[str, str]]

Map MEG/EEG subjects to FreeSurfer MRI subjects. Keys in mri_subjects are names for different mappings and correspond to values of the state parameter mri; the inner dictionaries map subject values to MRI subject names (i.e., directory names under {root}/derivatives/freesurfer). By default, an identity mapping is used (each subject uses their own MRI directory), but custom mappings can be defined, for example to let several subjects share a template brain or to point to individually scaled MRI subjects, e.g.:

mri_subjects = {
    '': {  # default identity mapping
        'S001': 'S001',
        'S002': 'S002',
    },
    'fsaverage': {  # all subjects use the template brain
        'S001': 'fsaverage',
        'S002': 'fsaverage',
    },
}

Pipeline.preload: bool

Whether to preload raw data into memory before creating epochs. Default is False. It is observed that in some datasets reading raw data when creating epochs is time consuming, and in these cases setting preload=True can speed up epoch creation.

Reading files 

Note

Gain more control over reading files through adding a RawPipe to Pipeline.raw.

Pipeline.stim_channel: str | Sequence[str]

By default, events are loaded from all stim channels; use this parameter to restrict events to one or several stim channels.

Pipeline.merge_triggers: int

Use a non-default merge parameter for load.mne.events().

Pipeline.trigger_shift: float | Dict[str, float]

Set this attribute to shift all trigger times by a constant (in seconds). For example, with trigger_shift = 0.03 a trigger that originally occurred 35.10 seconds into the recording will be shifted to 35.13. If the trigger delay differs between subjects, this attribute can also be a dictionary mapping subject names to shift values, e.g. trigger_shift = {'S001': 0.02, 'S002': 0.05, ...}.

Pipeline.meg_system: str

Specify the MEG system used to acquire the data so that the right sensor neighborhood graph can be loaded. This is usually automatic, but is needed for KIT files convert with with mne < 0.13. Equivalent to the sysname parameter in load.mne.epochs_ndvar() etc. For example, for data from NYU New York, the correct value is meg_system="KIT-157".

Pre-processing (raw)

Pipeline.raw

Define a pre-processing pipeline as a series of linked processing steps (mne refers to continuous data that is not time-locked to a specific event as Raw, with filenames matching *_raw.fif):

`RawFilter`(source[, l_freq, h_freq, cache, ...])	Filter raw pipe
`RawICA`(source, task[, method, random_state, ...])	ICA raw pipe
`RawApplyICA`(source, ica[, cache])	Apply ICA estimated in a `RawICA` pipe
`RawMaxwell`(source[, bad_condition, cache, flat])	Maxwell filter raw pipe.
`RawOversampledTemporalProjection`(source[, ...])	Oversampled temporal projection: see `mne.preprocessing.oversampled_temporal_projection()`
`RawSource`([sysname, rename_channels, ...])	Raw data source
`RawReReference`(source[, reference, add, ...])	Re-reference EEG data

Each preprocessing step is defined as a named entry with its input as first argument (source). The raw data that constitutes the input to the pipeline can be accessed as "raw" For example, the following definition sets up a pipeline for MEG, using TSSS, a band-pass filter and ICA:

class Experiment(Pipeline):

    raw = {
        'tsss': RawMaxwell('raw', st_duration=10., ignore_ref=True, st_correlation=0.9, st_only=True),
        '1-40': RawFilter('tsss', 1, 40),
        'ica': RawICA('1-40', 'task', 'extended-infomax', n_components=0.99),
    }

To use the raw --> TSSS --> 1-40 Hz band-pass pipeline, use e.set(raw="1-40"). To use raw --> TSSS --> 1-40 Hz band-pass --> ICA, select e.set(raw="ica").

The following is an example for EEG using band-pass filter, ICA and re-referencing:

class Experiment(Pipeline):

    raw = {
        '1-20': RawFilter('raw', 1, 20, cache=False),
        'ica': RawICA('1-20', 'stories'),
        'reref': RawReReference('ica', ['A1', 'A2'], 'A2')
        # Use the same ICA, but with a high pass filter with a lower cutoff frequency:
        '0.2-20': RawFilter('raw', 0.2, 20, cache=False),
        '0.2-20ica': RawApplyICA('0.2-20', 'ica'),
        '0.2reref': RawReReference('0.2-20ica', ['A1', 'A2'], 'A2'),
    }

Note

Continuous files take up a lot of hard drive space. By default, files for most pre-processing steps are cached. This can be controlled with the cache parameter: set cache=False to avoid caching. To delete files corresponding to a specific step (e.g., raw='1-40'), use the Pipeline.rm() method:

>>> e.rm('cached-raw-file', True, raw='1-40')

Events 

Note

Gain more control over events through overriding Pipeline.fix_events() and Pipeline.label_events().

Pipeline.variables

Event variables add labels and variables to the events:

`LabelVar`(source, codes[, default, task, fnmatch])	Variable assigning labels to values
`EvalVar`(code[, task])	Variable based on evaluating a statement
`GroupVar`(groups[, task])	Group membership for each subject

Most of the time, the main purpose of this attribute is to turn trigger values into meaningful labels:

class Mouse(Pipeline):

    variables = {
        'stimulus': LabelVar('trigger', {(162, 163): 'target', (166, 167): 'prime'}),
        'prediction': LabelVar('trigger', {162: 'expected', 163: 'unexpected'}),
    }

This defines a variable called “stimulus”, and on this variable all events that have triggers 162 and 163 have the value "target", and events with trigger 166 and 167 have the value "prime". The “prediction” variable only labels triggers 162 and 163. Unmentioned trigger values are assigned the empty string ('').

Epochs 

Pipeline.epochs

Epochs are specified as a {name: epoch_definition} dictionary. Names are str, and epoch_definition are instances of the classes described below:

`PrimaryEpoch`(task[, sel, tmin, tmax, ...])	Epoch based on selecting events from a raw file
`SecondaryEpoch`(base[, sel])	Epoch inheriting events from another epoch
`SuperEpoch`(sub_epochs, **kwargs)	Combine several other epochs
`ContinuousEpoch`(task[, sel, pad_start, ...])	Epoch spanning multiple events for continuous analysis

Examples:

epochs = {
    # some primary epochs:
    'picture': PrimaryEpoch('words', "stimulus == 'picture'"),
    'word': PrimaryEpoch('words', "stimulus == 'word'"),
    # use the picture baseline for the sensor covariance estimate
    'cov': SecondaryEpoch('picture', tmax=0),
    # another secondary epoch:
    'animal_words': SecondaryEpoch('noun', sel="word_type == 'animal'"),
    # a superset-epoch:
    'all_stimuli': SuperEpoch(('picture', 'word')),
}

Tests 

Pipeline.tests

Statistical tests are defined as {name: test_definition} dictionary. This allows automatic caching of permutation test results when using Pipeline.load_test(). Tests are defined using the following classes:

`TTestOneSample`([tail])	One-sample t-test
`TTestRelated`(model, c1, c0[, tail])	Related measures t-test
`TTestIndependent`(model, c1, c0[, tail])	Independent measures t-test (comparing groups of subjects)
`ANOVA`(x[, model, vars])	ANOVA test
`TContrastRelated`(model, contrast[, tail])	Contrasts of T-maps (see `eelbrain.testnd.TContrastRelated`)
`TwoStageTest`(stage_1[, vars, model])	Two-stage test: T-test of regression coefficients

Example:

tests = {
    'my_anova': ANOVA('noise * word_type * subject'),
    'my_ttest': TTestRelated('noise', 'a_lot_of_noise', 'no_noise'),
}

Subject groups 

Pipeline.groups

A subject group called 'all' containing all subjects is always implicitly defined. Additional subject groups can be defined in Pipeline.groups with {name: group_definition} entries:

`Group`(subjects)	Group defined as collection of subjects
`SubGroup`(base, exclude)	Group defined by removing subjects from a base group

Example:

groups = {
    'good': SubGroup('all', ['R0013', 'R0666']),
    'bad': Group(['R0013', 'R0666']),
}

Parcellations (`parcs`)

Pipeline.parcs

A parcellation determines how the brain surface is divided into regions. A number of standard parcellations are automatically defined (see parc (parcellations) below). Additional parcellations can be defined in the Pipeline.parcs dictionary with {name: parc_definition} entries.

`SubParc`(base, labels[, views])	A subset of labels in another parcellation
`CombinationParc`(base, labels[, views])	Recombine labels from an existing parcellation
`SeededParc`(seeds[, mask, surface, views])	Parcellation that is grown from seed coordinates
`IndividualSeededParc`(seeds[, mask, surface, ...])	Seed parcellation with individual seeds for each subject
`FreeSurferParc`([views])	Parcellation that is created outside Eelbrain for each subject
`FSAverageParc`([views])	Fsaverage parcellation that is morphed to individual subjects

Visualization defaults 

Pipeline.brain_plot_defaults

The Pipeline.brain_plot_defaults dictionary can contain options that changes defaults for brain plots (for reports and movies). The following options are available:

surf‘inflated’ | ‘pial’ | ‘smoothwm’ | ‘sphere’ | ‘white’: Freesurfer surface to use as brain geometry.
viewsstr | iterator of str: View or views to show in the figure. Can also be set for each parcellation, see Pipeline.parc.
foregroundmayavi color: Figure foreground color (i.e., the text color).
backgroundmayavi color: Figure background color.
smoothing_stepsNone | int: Number of smoothing steps to display data.

State Parameters 

An Pipeline instance has a state, which determines what data and settings it is currently using. Not all settings are always relevant. For example, subject is relevant for steps applied separately to each subject, like make_ica_selection(), whereas group defines the group of subjects in group level analysis, such as in load_test().

State Parameters can be set after an Pipeline has been initialized to affect the analysis, for example:

>>> my_experiment = Pipeline()
>>> my_experiment.set(raw='1-40', cov='noreg')

sets up my_experiment to use a 1-40 Hz band-pass filter as preprocessing, and to use sensor covariance matrices without regularization. Most methods also accept state parameters, so Pipeline.set() does not have to be used separately.

`session`

Which session to work with.

`task`

Which task to work with (usually set automatically when epoch is set).

`run`

Which run to work with.

`raw`

Select the preprocessing pipeline applied to the continuous data. Options are all the processing steps defined in Pipeline.raw, as well as "raw" for using unprocessed raw data.

`subject`

Any subject in the experiment.

`group`

Any group defined in Pipeline.groups. Will restrict the analysis to that group of subjects.

`epoch`

Any epoch defined in Pipeline.epochs. Specify the epoch on which the analysis should be conducted.

`rej` (trial rejection)

Trial rejection can be turned off e.set(rej=''), meaning that no trials are rejected, and back on, meaning that the corresponding rejection files are used e.set(rej='man').

`model`

While the epoch state parameter determines which events are included when loading data, the model parameter determines how these events are split into different condition cells. The parameter should be set to the name of a categorial event variable which defines the desired cells. In the Example, e.load_evoked(epoch='target', model='prediction') would load responses to the target, averaged for expected and unexpected trials.

Cells can also be defined based on crossing two variables using the % sign. In the Example, to load corresponding primes together with the targets, you would use e.load_evoked(epoch='word', model='stimulus % prediction').

`equalize_evoked_count`

By default, the analysis uses all epochs marked as good during rejection. Set equalize_evoked_count='eq' to discard trials to make sure the same number of epochs goes into each cell of the model (see equal_count parameter to Dataset.aggregate()).

‘’ (default): Use all epochs.
‘eq’: Make sure the same number of epochs n is used in each cell by discarding epochs. The first n epochs are used for each condition (assuming that habituation increases by condition).

`cov`

The method for correcting the sensor covariance.

‘noreg’: Use raw covariance as estimated from the data (do not regularize).
‘bestreg’ (default): Find the regularization parameter that leads to optimal whitening of the baseline.
‘reg’: Use the default regularization parameter (0.1).
‘auto’: Use automatic selection of the optimal regularization method, as described in mne.compute_covariance().
empty_room: Empty room covariance; for required setup, see Empty room noise covariance.
‘ad_hoc’: Use diagonal covariance based on mne.cov.make_ad_hoc_cov().

`src`

The source space to use.

ico-x: Surface source space based on icosahedral subdivision of the white matter surface x steps (e.g., ico-4, the default).

vol-x: Volume source space based on a volume grid with x mm resolution (x is the distance between sources, e.g. vol-10 for a 10 mm grid).

`inv`

What inverse solution to use for source localization. inv can be set with Pipeline.set_inv(), which has a detailed description of the options. inv can also be set directly using the appropriate string, e.g., e.set(inv='fixed-6-MNE-0'). To determine the string corresponding to a given set of parameters, use Pipeline.inv_str(). For example:

>>> Pipeline.inv_str('fixed', snr=6, method='MNE', depth=0)
'fixed-6-MNE-0'

Consequently, the following two are equivalent for setting inv:

>>> Pipeline.set_inv('fixed', snr=6, method='MNE', depth=0)
>>> Pipeline.set(inv='fixed-6-MNE-0')

`parc` (parcellations)

The parcellation determines how the brain surface is divided into regions. Parcellations included with FreeSurfer can directly be used:

FreeSurfer Parcellations: aparc.a2005s, aparc.a2009s, aparc, aparc.DKTatlas, PALS_B12_Brodmann, PALS_B12_Lobes, PALS_B12_OrbitoFrontal, PALS_B12_Visuotopic.

Additional parcellation can be defined in the Pipeline.parcs attribute. Parcellations are used in different contexts:

When loading source space data, the current parc state determines the parcellation of the source space (change the state parameter with e.set(parc='aparc')).
When loading tests, setting the parc parameter treats each label as a separate ROI. For spatial cluster-based tests that means that no clusters can cross the boundary between two labels. On the other hand, using the mask parameter treats all named labels as connected surface, but discards any sources labeled as "unknown". For example, loading a test with mask='PALS_B12_Lobes' will perform a whole-brain test on the cortex, while discarding subcortical sources.

Parcellations are set with their name, with the exception of SeededParc: for those, the name is followed by the radius in mm, for example, to use seeds defined in a parcellation named 'myparc' with a radius of 25 mm around the seed, use e.set(parc='myparc-25').

A few additional parcellations that provide homogeneous masks are included for backwards compatibility. For future work, it is recommended to build such masks from aparc or another parcellation with more fine-grained subdivision into labels.

cortex: All sources in cortex, based on the FreeSurfer “cortex” label.
lobes: Modified version of PALS_B12_Lobes in which the limbic lobe is merged into the other 4 lobes.
lobes-op: One large region encompassing occipital and parietal lobe in each hemisphere.
lobes-ot: One large region encompassing occipital and temporal lobe in each hemisphere.

`adjacency`

Possible values: '', 'link-midline'

Adjacency refers to the edges connecting data channels (sensors for sensor space data and sources for source space data). These edges are used to find clusters in cluster-based permutation tests. For source spaces, the default is to use FreeSurfer surfaces in which the two hemispheres are unconnected. By setting adjacency='link-midline', this default adjacency can be modified so that the midline gyri of the two hemispheres get linked at sources that are at most 15 mm apart. This parameter currently does not affect sensor space adjacency.

`select_clusters` (cluster selection criteria)

In thresholded cluster test, clusters are initially filtered with a minimum size criterion. This can be changed with the select_clusters analysis parameter with the following options:

Name	Min time	Min sources	Min sensors
`"all"`
`"10ms"`	10 ms	10	4
`""` (default)	25 ms	10	4
`"large"`	25 ms	20	8

To change the cluster selection criterion use for example:

>>> e.set(select_clusters='all')

The Pipeline

The `Pipeline`