force udpate...
@@ -53,7 +53,7 @@ event_codes = [5, 6, 9, 10, 13, 14]
# This will download the files if you don't have them yet,
# and then return the paths to the files.
-physionet_paths = mne.datasets.eegbci.load_data(subject_id, event_codes)
+physionet_paths = mne.datasets.eegbci.load_data(subject_id, event_codes, force_update=True)
# Load each of the files
raws = [mne.io.read_raw_edf(path, preload=False, stim_channel='auto',
@@ -211,7 +211,7 @@ model.predict_outs(test_set.X)
from braindecode.datautil import SignalAndTarget
# First 50 subjects as train
-physionet_paths = [mne.datasets.eegbci.load_data(sub_id, [4, 8, 12])
+physionet_paths = [mne.datasets.eegbci.load_data(sub_id, [4, 8, 12], force_update=True)
for sub_id in range(1, 51)]
physionet_paths = np.concatenate(physionet_paths)
raws = [mne.io.read_raw_edf(path, preload=False, stim_channel='auto')
@@ -232,7 +232,7 @@ epoched = mne.Epochs(raw, events, dict(hands=2, feet=3), tmin=1,
baseline=None, preload=True)
# 51-55 as validation subjects
-physionet_paths_valid = [mne.datasets.eegbci.load_data(sub_id, [4, 8, 12])
+physionet_paths_valid = [mne.datasets.eegbci.load_data(sub_id, [4, 8, 12], force_update=True)
for sub_id in range(51, 56)]
physionet_paths_valid = np.concatenate(physionet_paths_valid)
raws_valid = [mne.io.read_raw_edf(path, preload=False, stim_channel='auto')
