Note
Go to the end to download the full example code.
Split Dataset Example#
In this example, we aim to show multiple ways of how you can split your datasets for training, testing, and evaluating your models.
# Authors: Lukas Gemein <l.gemein@gmail.com>
#
# License: BSD (3-clause)
from braindecode.datasets import MOABBDataset
from braindecode.preprocessing import create_windows_from_events
Loading the dataset#
Firstly, we create a dataset using the braindecode
MOABBDataset to load
it fetched from MOABB. In this example, we’re using Dataset 2a from BCI
Competition IV.
dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
The dataset class name 'BNCI2014001' must be an abbreviation of its code 'BNCI2014-001'. See moabb.datasets.base.is_abbrev for more information.
Splitting#
By description information#
The class MOABBDataset has a pandas
DataFrame containing additional description of its internal datasets,
which can be used to help splitting the data
based on recording information, such as subject, session, and run of each trial.
dataset.description
Here, we’re splitting the data based on different runs. The method split returns a dictionary with string keys corresponding to unique entries in the description DataFrame column.
splits = dataset.split("run")
print(splits)
splits["4"].description
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2ba3470>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d338bd10>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d319ccb0>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d319ed50>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d73b90>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d71970>}
By row index#
Another way we can split the dataset is 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
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2cdd8e0>}
However, if we want multiple splits based on indices, we can also define a list containing lists of integers. In this case, the dictionary will have string keys representing the index of the dataset split in the order of the given list of integers.
splits = dataset.split([[0, 1, 5], [2, 3, 4], [6, 7, 8, 9, 10, 11]])
print(splits)
splits["2"].description
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2ba3470>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d71a60>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d730b0>}
You can also name each split in the output dictionary by specifying the keys of each list of indexes in the input dictionary:
splits = dataset.split(
{"train": [0, 1, 5], "valid": [2, 3, 4], "test": [6, 7, 8, 9, 10, 11]}
)
print(splits)
splits["test"].description
{'train': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff1b2471280>, 'valid': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d706b0>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d73e90>}
Observation#
Similarly, we can split datasets after creating windows using the same methods.
windows = create_windows_from_events(
dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0
)
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
# Splitting by different runs
print("Using description info")
splits = windows.split("run")
print(splits)
print()
# Splitting by row index
print("Splitting by row index")
splits = windows.split([4, 8])
print(splits)
print()
print("Multiple row index split")
splits = windows.split([[4, 8], [5, 9, 11]])
print(splits)
print()
# Specifying output's keys
print("Specifying keys")
splits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))
print(splits)
Using description info
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d258b740>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2ba3470>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0c9d30f50>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d73860>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d73b30>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d73020>}
Splitting by row index
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d73e90>}
Multiple row index split
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2ba3470>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0b6dcc680>}
Specifying keys
{'train': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff1b2471280>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7ff0d2d739e0>}
Total running time of the script: (0 minutes 4.883 seconds)
Estimated memory usage: 791 MB