update tutorials

doc/documentation/datasets.rst

@@ -164,15 +164,20 @@ EEGBCI motor imagery
 The EEGBCI dataset is documented in :footcite:`SchalkEtAl2004` and on the
 `PhysioNet documentation page <https://physionet.org/content/eegmmidb/1.0.0/>`_.
 The data set is available at PhysioNet :footcite:`GoldbergerEtAl2000`.
-It 64-channel EEG recordings from 109 subjects and 14 runs on each subject in EDF+
-format. The recordings were made using the BCI2000 system. To load a subject,
-do::
+It contains 64-channel EEG recordings from 109 subjects and 14 runs on each
+subject in EDF+ format. The recordings were made using the BCI2000 system.
+To load a subject, do::
 
     from mne.io import concatenate_raws, read_raw_edf
     from mne.datasets import eegbci
-    raw_fnames = eegbci.load_data(subject, runs)
+    subjects = [1]  # may vary
+    runs = [4, 8, 12]  # may vary
+    raw_fnames = eegbci.load_data(subjects, runs)
     raws = [read_raw_edf(f, preload=True) for f in raw_fnames]
+    # concatenate runs from subject
     raw = concatenate_raws(raws)
+    # make channel names follow standard conventions
+    eegbci.standardize(raw)
 
 .. topic:: Examples
 

examples/decoding/decoding_csp_eeg.py

@@ -41,10 +41,10 @@
 # avoid classification of evoked responses by using epochs that start 1s after
 # cue onset.
 tmin, tmax = -1.0, 4.0
-subject = 1
+subjects = 1
 runs = [6, 10, 14]  # motor imagery: hands vs feet
 
-raw_fnames = eegbci.load_data(subject, runs)
+raw_fnames = eegbci.load_data(subjects, runs)
 raw = concatenate_raws([read_raw_edf(f, preload=True) for f in raw_fnames])
 eegbci.standardize(raw)  # set channel names
 montage = make_standard_montage("standard_1005")

examples/preprocessing/eeg_bridging.py

@@ -65,7 +65,7 @@
 raw_data = dict()  # store infos for electrode positions
 for sub in range(1, 11):
     print(f"Computing electrode bridges for subject {sub}")
-    raw_fname = mne.datasets.eegbci.load_data(subject=sub, runs=(1,))[0]
+    raw_fname = mne.datasets.eegbci.load_data(subjects=sub, runs=(1,))[0]
     raw = mne.io.read_raw(raw_fname, preload=True, verbose=False)
     mne.datasets.eegbci.standardize(raw)  # set channel names
     raw.set_montage(montage, verbose=False)

examples/preprocessing/muscle_ica.py

@@ -97,7 +97,7 @@
 
 for sub in (1, 2):
     raw = mne.io.read_raw_edf(
-        mne.datasets.eegbci.load_data(subject=sub, runs=(1,))[0], preload=True
+        mne.datasets.eegbci.load_data(subjects=sub, runs=(1,))[0], preload=True
     )
     mne.datasets.eegbci.standardize(raw)  # set channel names
     montage = mne.channels.make_standard_montage("standard_1005")

examples/time_frequency/time_frequency_erds.py

@@ -50,7 +50,7 @@
 # First, we load and preprocess the data. We use runs 6, 10, and 14 from
 # subject 1 (these runs contains hand and feet motor imagery).
 
-fnames = eegbci.load_data(subject=1, runs=(6, 10, 14))
+fnames = eegbci.load_data(subjects=1, runs=(6, 10, 14))
 raw = concatenate_raws([read_raw_edf(f, preload=True) for f in fnames])
 
 raw.rename_channels(lambda x: x.strip("."))  # remove dots from channel names

mne/datasets/utils.py

@@ -347,9 +347,8 @@ def _download_all_example_data(verbose=True):
         sleep_physionet,
     )
 
-    eegbci.load_data(1, [6, 10, 14], update_path=True)
-    for subj in range(4):
-        eegbci.load_data(subj + 1, runs=[3], update_path=True)
+    eegbci.load_data(subjects=1, runs=[6, 10, 14], update_path=True)
+    eegbci.load_data(subjects=range(1, 5), runs=[3], update_path=True)
     logger.info("[done eegbci]")
 
     sleep_physionet.age.fetch_data(subjects=[0, 1], recording=[1])

tutorials/forward/35_eeg_no_mri.py

@@ -48,7 +48,7 @@
 # .. note:: See :ref:`plot_montage` to view all the standard EEG montages
 #           available in MNE-Python.
 
-(raw_fname,) = eegbci.load_data(subject=1, runs=[6])
+(raw_fname,) = eegbci.load_data(subjects=1, runs=[6])
 raw = mne.io.read_raw_edf(raw_fname, preload=True)
 
 # Clean channel names to be able to use a standard 1005 montage

tutorials/preprocessing/40_artifact_correction_ica.py

@@ -532,9 +532,9 @@
 raws = list()
 icas = list()
 
-for subj in range(4):
+for subj in range(1, 5):
     # EEGBCI subjects are 1-indexed; run 3 is a left/right hand movement task
-    fname = mne.datasets.eegbci.load_data(subj + 1, runs=[3])[0]
+    fname = mne.datasets.eegbci.load_data(subj, runs=[3])[0]
     raw = mne.io.read_raw_edf(fname).load_data().resample(50)
     # remove trailing `.` from channel names so we can set montage
     mne.datasets.eegbci.standardize(raw)