[GSOC] Add surrogate data generation for significant connectivity estimation

doc/api.rst

@@ -111,4 +111,5 @@ Dataset functions
 .. autosummary::
    :toctree: generated/
 
-   make_signals_in_freq_bands
+   make_signals_in_freq_bands
+   make_surrogate_data

doc/references.bib

@@ -61,6 +61,16 @@ @article{Dawson_2016
  year = {2016}
 }
 
+@article{DowdingHaufe2018,
+  title={Powerful statistical inference for nested data using sufficient summary statistics},
+  author={Dowding, Irene and Haufe, Stefan},
+  doi={10.3389/fnhum.2018.00103},
+  journal={Frontiers in Human Neuroscience},
+  volume={12},
+  pages={103},
+  year={2018}
+}
+
 @article{EwaldEtAl2012,
  author = {Ewald, Arne and Marzetti, Laura and Zappasodi, Filippo and Meinecke, Frank C. and Nolte, Guido},
  doi = {10.1016/j.neuroimage.2011.11.084},
@@ -185,6 +195,17 @@ @book{OppenheimEtAl1999
  year = {1999}
 }
 
+@article{PellegriniEtAl2023,
+  title={Identifying good practices for detecting inter-regional linear functional connectivity from {EEG}},
+  author={Pellegrini, Franziska and Delorme, Arnaud and Nikulin, Vadim and Haufe, Stefan},
+  doi={10.1016/j.neuroimage.2023.120218},
+  journal={NeuroImage},
+  volume={277},
+  pages={120218},
+  year={2023},
+  publisher={Elsevier}
+}
+
 @book{SekiharaNagarajan2008,
  author = {Sekihara, Kensuke and Nagarajan, Srikantan S.},
  doi = {10.1007/978-3-540-79370-0},

examples/sensor_connectivity.py

@@ -12,6 +12,8 @@
 #
 # License: BSD (3-clause)
 
+# %%
+
 import os.path as op
 
 import mne
@@ -22,7 +24,8 @@
 
 print(__doc__)
 
-###############################################################################
+# %%
+
 # Set parameters
 data_path = sample.data_path()
 raw_fname = op.join(data_path, "MEG", "sample", "sample_audvis_filt-0-40_raw.fif")
@@ -41,7 +44,7 @@
 )
 
 # Create epochs for the visual condition
-event_id, tmin, tmax = 3, -0.2, 1.5  # need a long enough epoch for 5 cycles
+event_id, tmin, tmax = 3, -0.2, 1.5  # want a long enough epoch for 5 cycles
 epochs = mne.Epochs(
     raw,
     events,
@@ -52,24 +55,18 @@
     baseline=(None, 0),
     reject=dict(grad=4000e-13, eog=150e-6),
 )
+epochs.load_data().pick("grad")  # just keep MEG and no EOG now
 
-# Compute connectivity for band containing the evoked response.
-# We exclude the baseline period:
-fmin, fmax = 4.0, 9.0
-sfreq = raw.info["sfreq"]  # the sampling frequency
+# Compute Fourier coefficients for the epochs (returns an EpochsSpectrum object)
+# (storing Fourier coefficients in EpochsSpectrum objects requires MNE >= 1.8)
 tmin = 0.0  # exclude the baseline period
-epochs.load_data().pick_types(meg="grad")  # just keep MEG and no EOG now
+spectrum = epochs.compute_psd(method="multitaper", tmin=tmin, output="complex")
+
+# Compute connectivity for the frequency band containing the evoked response
+# (passing EpochsSpectrum objects as data requires MNE-Connectivity >= 0.8)
+fmin, fmax = 4.0, 9.0
 con = spectral_connectivity_epochs(
-    epochs,
-    method="pli",
-    mode="multitaper",
-    sfreq=sfreq,
-    fmin=fmin,
-    fmax=fmax,
-    faverage=True,
-    tmin=tmin,
-    mt_adaptive=False,
-    n_jobs=1,
+    data=spectrum, method="pli", fmin=fmin, fmax=fmax, faverage=True, n_jobs=1
 )
 
 # Now, visualize the connectivity in 3D: