from braindecode.datasets import MOABBDataset
from braindecode.preprocessing.windowers import create_windows_from_events
###############################################################################
# First, we create a dataset based on BCIC IV 2a fetched with MOABB,
ds = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
###############################################################################
# ds has a pandas DataFrame with additional description of its internal datasets
display(ds.description)
###############################################################################
# We can split the dataset based on the info in the description, for example
# based on different runs. The returned dictionary will have string keys
# corresponding to unique entries in the description DataFrame column
splits = ds.split("run")
display(splits)
display(splits["run_4"].description)
###############################################################################
# We can also split the dataset based on a list of integers corresponding to
# rows in the description. In this case, the returned dictionary will have
# '0' as the only key
splits = ds.split([0, 1, 5])
display(splits)
display(splits["0"].description)
###############################################################################
# If we want multiple splits based on indices, we can also specify a list of
# list of integers. In this case, the dictionary will have string keys
# representing the id of the dataset split in the order of the given list of
##############################################################################
# We can apply preprocessing transforms that are defined in mne and work
# in-place, such as resampling, bandpass filtering, or electrode selection.
transforms = [
MNEPreproc("pick_types", eeg=True, meg=False, stim=True),
MNEPreproc("resample", sfreq=100),
]
print(ds.datasets[0].raw.info["sfreq"])
preprocess(ds, transforms)
print(ds.datasets[0].raw.info["sfreq"])
###############################################################################
# We can easily split ds based on a criteria applied to the description
# DataFrame:
subsets = ds.split("session")
print({subset_name: len(subset) for subset_name, subset in subsets.items()})
###############################################################################
# Next, we use a windower to extract events from the dataset based on events:
windows_ds = create_windows_from_events(ds,
trial_start_offset_samples=0,
trial_stop_offset_samples=100,
window_size_samples=400,
window_stride_samples=100,
drop_last_window=False)
###############################################################################
# We can iterate through the windows_ds which yields a window x,
# a target y, and window_ind (which itself contains `i_window_in_trial`,
# `i_start_in_trial`, and `i_stop_in_trial`, which are required for combining
##############################################################################
# We can apply preprocessing transforms that are defined in mne and work
# in-place, such as resampling, bandpass filtering, or electrode selection.
preprocessors = [
Preprocessor('pick_types', eeg=True, meg=False, stim=True),
Preprocessor('resample', sfreq=100)
]
print(dataset.datasets[0].raw.info["sfreq"])
preprocess(dataset, preprocessors)
print(dataset.datasets[0].raw.info["sfreq"])
###############################################################################
# We can easily split ds based on a criteria applied to the description
# DataFrame:
subsets = dataset.split("session")
print({subset_name: len(subset) for subset_name, subset in subsets.items()})
###############################################################################
# Next, we use a windower to extract events from the dataset based on events:
windows_dataset = create_windows_from_events(dataset,
trial_start_offset_samples=0,
trial_stop_offset_samples=100,
window_size_samples=400,
window_stride_samples=100,
drop_last_window=False)
###############################################################################
# We can iterate through the windows_ds which yields a window x,
# a target y, and window_ind (which itself contains ``i_window_in_trial``,
# ``i_start_in_trial``, and ``i_stop_in_trial``, which are required for
from braindecode.datasets import MOABBDataset
from braindecode.preprocessing import create_windows_from_events
###############################################################################
# First, we create a dataset based on BCIC IV 2a fetched with MOABB,
dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
###############################################################################
# ds has a pandas DataFrame with additional description of its internal datasets
dataset.description
###############################################################################
# We can split the dataset based on the info in the description, for example
# based on different runs. The returned dictionary will have string keys
# corresponding to unique entries in the description DataFrame column
splits = dataset.split("run")
print(splits)
splits["run_4"].description
###############################################################################
# We can also split the dataset based on a list of integers corresponding to
# rows in the description. In this case, the returned dictionary will have
# '0' as the only key
splits = dataset.split([0, 1, 5])
print(splits)
splits["0"].description
###############################################################################
# If we want multiple splits based on indices, we can also specify a list of
# list of integers. In this case, the dictionary will have string keys
# representing the id of the dataset split in the order of the given list of