The MneExperiment Pipeline

See also

• MneExperiment class reference for details on all available methods

• Pipeline wiki page for additional information

• TRFExperiment: an experimental extension of the pipeline to Temporal Response Function analysis

Introduction

The MneExperiment pipeline manages the following analysis steps:

1. Preprocessing

2. Epoching

3. Optional source localization

4. 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.

MneExperiment 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., MneExperiment.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, MneExperiment.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, MneExperiment.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 first steps for setting up the pipeline are:

• Arranging the input data files in the expected file structure

• Defining an MneExperiment subclass with the parameters required to find those files

The pipeline expects input files in a strictly determined folder/file structure. In the schema below, curly brackets indicate slots that the pipeline will replace with specific names. For example, {subject} will be replaced with each specific subject’s name:

Root                            {root}
M/EEG directory                    /{data_dir}
M/EEG subject                         /{subject}
trans-file                               /{subject}-trans.fif
raw-file                                 /{subject}_{session}-raw.fif
MRI directory                      /mri
MRI subject                           /{subject}

{data_dir}, the directory in which the pipeline looks for the raw data, is determined by the MneExperiment.data_dir attribute. By default it is 'meg', but it can be changed, for example, to 'eeg'. This is merely to make the filenames less confusing when e.g. working with EEG data, it does not influence the analysis in any other way.

MRI files (including trans-file) are optional and only needed for source localization. The {root}/mri/{subject} directories are FreeSurfer subject directories. 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. 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).

{session} refers to the name of the recording session. The name of one or several recording session(s) has to be specified on an MneExperiment subclass, using the MneExperiment.sessions attribute. Those names will be used to find the raw data files, by filling in the raw-file template:

from eelbrain.pipeline import *

class MyExperiment(MneExperiment):

    data_dir = 'eeg'
    sessions = 'words'

The final step to locating the files is providing the {root} location when initializing that subclass, for example:

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

The pipeline will then determine the subject names based on the names of the folders inside the M/EEG directory. Only names matching a specific expression will be considered, for example “S” followed by 3 or more digits. This expression can be customized in MneExperiment.subject_re.

Assuming a subject is named “S001”, the pipeline will look for data at the following locations:

• The raw data file at ~/Data/Experiment/meg/S001/S001_words-raw.fif (the session is called “words” which is specified in MyExperiment.sessions)

• The trans-file from the coregistration at ~/Data/Experiment/meg/S001/S001-trans.fif

• The FreeSurfer MRI-directory at ~/Data/Experiment/mri/S001

The setup can be tested using MneExperiment.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
...

Note

The default input format for M/EEG data is the FIFF format (*-raw.fif files). To specify an alternative input data format, see MneExperiment.raw.

MneExperiment.visits

Note

If participants come back for the experiment on multiple occasions, a visits attribute might also be needed. For details see the corresponding wiki page.

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.

You will want to access the MneExperiment subclass (MyExperiment above) 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, and run or import that file as needed. IN the example above, the following would be saved in ~/Code/MyProject/my_experiment.py:

from eelbrain.pipeline import *

class MyExperiment(MneExperiment):

    data_dir = 'eeg'
    sessions = 'words'

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 MneExperiment.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 MneExperiment.merge_triggers to handle spurious events. Then, add event labels. Initially, events are only labeled with the trigger ID. Use the MneExperiment.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):

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

• MneExperiment.variables: Add labels based on triggers

• MneExperiment.label_events(): Add any more complex labels

Defining data epochs

Once events are properly labeled, define MneExperiment.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 MneExperiment.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 MneExperiment.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 MneExperiment.next().

If a general threshold is adequate, the selection of bad channels based on neighbor-correlation can be automated using the MneExperiment.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 MneExperiment.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 MneExperiment.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 MneExperiment.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:

• Put an empty room recording in each subject’s MEG directory, just like the other MEG files, with session name emptyroom. If you want to use the same empty room file for all subjects you can make links instead of copies to save space.

• Add "emptyroom” as a session to the experiment definition.

• Use the empty room covariance though 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 MneExperiment. For example, load a Dataset with source-localized condition averages using MneExperiment.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 MneExperiment.tests attribute, and then loaded directly using the MneExperiment.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 MneExperiment.tests, the MneExperiment can generate HTML report files. These are generated with the MneExperiment.make_report() and MneExperiment.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 MneExperiment.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/mouse/mouse.py):

# skip test: data unavailable
from eelbrain.pipeline import *


class Mouse(MneExperiment):

    # Name of the experimental session(s), used to locate *-raw.fif files
    sessions = 'CAT'

    # Pre-processing pipeline: each entry in `raw` specifies one processing step. The first parameter
    # of each entry specifies the source (another processing step or 'raw' for raw input data).
    raw = {
        # Maxwell filter as first step (taking input from raw data, 'raw')
        'tsss': RawMaxwell('raw', st_duration=10., ignore_ref=True, st_correlation=0.9, st_only=True),
        # Band-pass filter data between 1 and 40 Hz (taking Maxwell-filtered data as input, 'tsss)
        '1-40': RawFilter('tsss', 1, 40),
        # Perform ICA on filtered data
        'ica': RawICA('1-40', 'CAT', n_components=0.99),
    }

    # Variables determine how event triggeres are mapped to meaningful labels. Events are represented
    # as data-table in which each row corresponds to one event (i.e., one trigger). Each variable
    # defined here adds one column in that data-table, assigning a label or value to each event.
    variables = {
        # The first parameter specifies the source variable (here the trigger values),
        # the second parameter a mapping from source to target labels/values
        'stimulus': LabelVar('trigger', {(162, 163): 'target', (166, 167): 'prime'}),
        'prediction': LabelVar('trigger', {(162, 166): 'expected', (163, 167): 'unexpected'}),
    }

    # Epochs specify how to extract time-locked data segments ("epochs") from the continuous data.
    epochs = {
        # A PrimaryEpoch definition extracts epochs directly from continuous data. The first argument
        # specifies the recording session from which to extract the data (here: 'CAT'). The second
        # argument specifies which events to extract the data from (here: all events at which the
        # 'stimulus' variable, defined above, has a value of either 'prime' or 'target').
        'word': PrimaryEpoch('CAT', "stimulus.isin(('prime', 'target'))", samplingrate=200),
        # A secondary epoch inherits its properties from the base epoch ("word") unless they are
        # explicitly modified (here, selecting a subset of events)
        'prime': SecondaryEpoch('word', "stimulus == 'prime'"),
        'target': SecondaryEpoch('word', "stimulus == 'target'"),
        # The 'cov' epoch defines the data segments used to compute the noise covariance matrix for
        # source localization
        'cov': SecondaryEpoch('prime', tmax=0),
    }

    # Tests define contrasts or comparisons based on variables.
    # In the example, the following would test for the difference between expected and unexpected words in
    # target words:
    # >>> result = mouse.load_test(test='surprise', epoch='target')
    tests = {
        '=0': TTestOneSample(),
        'surprise': TTestRelated('prediction', 'unexpected', 'expected'),
        'anova': ANOVA('prediction * subject'),
    }

    # Define masks for permutation tests in anatomically constrained regions. Fro exmample:
    # >>> result = mouse.load_test(test='surprise', epoch='target', mask='lateraltemporal')
    parcs = {
        'STG': SubParc('aparc', ('transversetemporal', 'superiortemporal')),
        'IFG': SubParc('aparc', ('parsopercularis', 'parsorbitalis', 'parstriangularis')),
        'lateraltemporal': SubParc('aparc', (
            'transversetemporal', 'superiortemporal', 'bankssts',
            'middletemporal', 'inferiortemporal')),
    }


# Initialize the pipeline instance with the root path where the data is located
root = '~/Data/Mouse'
e = Mouse(root)

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

>>> from mouse import *
>>> data = e.load_events()
>>> data.head()
trigger   i_start   T        SOA     subject   stimulus   prediction
--------------------------------------------------------------------
182       104273    104.27   12.04   S0001
182       116313    116.31   1.313   S0001
166       117626    117.63   0.598   S0001     prime      expected
162       118224    118.22   2.197   S0001     target     expected
166       120421    120.42   0.595   S0001     prime      expected
162       121016    121.02   2.195   S0001     target     expected
167       123211    123.21   0.596   S0001     prime      unexpected
163       123807    123.81   2.194   S0001     target     unexpected
167       126001    126      0.598   S0001     prime      unexpected
163       126599    126.6    2.195   S0001     target     unexpected

Experiment Definition

Basic setup

MneExperiment.owner: str

Set MneExperiment.owner to your email address if you want to be able to receive notifications. Whenever you run a sequence of commands with mne_experiment.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)

MneExperiment.auto_delete_results: bool

Whenever a MneExperiment 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.

MneExperiment.auto_delete_cache: bool

MneExperiment 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 MneExperiment.screen_log_level to 'debug' to learn about what change caused the cache to be invalid.

MneExperiment.screen_log_level: str

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

MneExperiment.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

MneExperiment.sessions: str | Sequence[str]

The name, or a list of names of the raw data files (see Setting up the file structure).

MneExperiment.data_dir: str

Folder name for the raw data directory. By default, this is meg, i.e., the experiment will look for raw files at root/meg/{subject}/{subject}_{session}-raw.fif. After setting data_dir = 'eeg', the experiment will look at root/eeg/{subject}/{subject}_{session}-raw.fif.

MneExperiment.subject_re: str

Subjects are identified on initialization by looking for folders in the MneExperiment.data_dir directory (meg by default) whose name matches the MneExperiment.subject_re regular expression. By default, this is one or more characters or underline, followed by one or more digits, for example: S001, subject_1, R0001 (for information about how to define a different pattern, see re).

Reading files

Note

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

MneExperiment.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.

MneExperiment.merge_triggers: int

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

MneExperiment.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, ...}.

MneExperiment.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)

MneExperiment.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, session[, method, ...])	ICA raw pipe
RawApplyICA(source, ica[, cache])	Apply ICA estimated in a RawICA pipe
RawMaxwell(source[, bad_condition, cache])	Maxwell filter raw pipe
RawOversampledTemporalProjection(source[, ...])	Oversampled temporal projection: see mne.preprocessing.oversampled_temporal_projection()
RawSource([filename, reader, sysname, ...])	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(MneExperiment):

    sessions = 'session'
    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', 'session', '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(MneExperiment):

    data_dir = 'eeg'
    sessions = ['stories', 'tones']
    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 MneExperiment.rm() method:

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

Events

Note

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

MneExperiment.variables

Event variables add labels and variables to the events:

LabelVar(source, codes[, default, session, ...])	Variable assigning labels to values
EvalVar(code[, session])	Variable based on evaluating a statement
GroupVar(groups[, session])	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(MneExperiment):

    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

MneExperiment.epochs

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

PrimaryEpoch(session[, sel])	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(session[, 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

MneExperiment.tests

Statistical tests are defined as {name: test_definition} dictionary. This allows automatic caching of permutation test results when using MneExperiment.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

MneExperiment.groups

A subject group called 'all' containing all subjects is always implicitly defined. Additional subject groups can be defined in MneExperiment.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)

MneExperiment.parcs

A parcellation determines how the brain surface is divided into regions. A number of standard parcellations are automatically defined (see parc/mask (parcellations) below). Additional parcellations can be defined in the MneExperiment.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

MneExperiment.brain_plot_defaults

The MneExperiment.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 MneExperiment.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 MneExperiment 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 MneExperiment has been initialized to affect the analysis, for example:

>>> my_experiment = MneExperiment()
>>> 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 MneExperiment.set() does not have to be used separately.

session

Which raw session to work with (one of MneExperiment.sessions; usually set automatically when epoch is set)

visit

Which visit to work with (one of MneExperiment.visits)

raw

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

subject

Any subject in the experiment (subjects are identified based on MneExperiment.subject_re).

group

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

epoch

Any epoch defined in MneExperiment.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 MneExperiment.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 MneExperiment.inv_str(). For example:

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

Consequently, the following two are equivalent for setting inv:

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

parc/mask (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 MneExperiment.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 expception of SeededParc: for those, the name is followed by the radious 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')