eelbrain.pipeline.MneExperiment.load_test
- MneExperiment.load_test(test, tstart=None, tstop=None, pmin=None, parc=None, mask=None, samples=10000, data='source', baseline=True, smooth=None, src_baseline=None, samplingrate=None, return_data=False, make=False, **state)
Create and load spatio-temporal cluster test results
- Parameters:
test (str) – Test for which to create a report (entry in MneExperiment.tests.
tstart (float) – Beginning of the time window for the test in seconds (default is the beginning of the epoch).
tstop (float) – End of the time window for the test in seconds (default is the end of the epoch).
pmin (Literal['tfce'] | float | None) – Kind of test.
parc (str) –
Run the test separately in each label of parc.
Warning
Results from spatio-temporal tests using
parc
are not corrected for multiple comparisons. You must manually correct for multiple comparisons based on the number of labels inparc
before interpreting p-values.mask (str) – Parcellation to use as anatomical mask in which to perform the test.
samples (int) – Number of random permutations of the data used to determine cluster p-values (default 10’000). If the test is already cached with a number ≥
samples
the cached version is returned, otherwise the test is recomputed.data (str) –
Data to test, for example:
'source'
spatio-temporal test in source space.'sensor'
spatio-temporal test in sensor space (MEG).'eeg'
spatio-temporal test in EEG sensor space.'source.mean'
ROI mean time course.'sensor.rms'
RMS across sensors.
baseline (bool | Tuple[float | None, float | None]) – Apply baseline correction using this period in sensor space. True to use the epoch’s baseline specification (default).
smooth (float) – Smooth data in space before test (value in [m] STD of Gaussian).
src_baseline (bool | Tuple[float | None, float | None]) – Apply baseline correction using this period in source space. True to use the epoch’s baseline specification. The default is to not apply baseline correction.
samplingrate (int) – Samplingrate in Hz for the analysis (default is specified in epoch definition).
return_data (bool) –
Return the data along with the test result (see below).
Warning
Single trial data (i.e., two-stage tests) take up a lot of memory and it might not be possible to load all data at once. Instead, loop through subjects and collect summary statistics.
make (bool) – If the target file does not exist, create it (could take a long time depending on the test; if False, raise an IOError).
... – State parameters (Use the
group
state parameter to select the subject group for which to perform the test).
- Returns:
ds (Dataset | dict (if return_data==True)) – Data that forms the basis of the test (for ROI tests, a
{roi: dataset}
dictionary).res (NDTest | ROITestResult) – Test result for the specified test (for ROIs tests, an
ROITestResult
object).