Note
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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 class <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 0x7fdfc9b27c50>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fdfc3cf0f50>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fdfc5018500>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7fdfc5019100>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50a7e0>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50a180>}
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 0x7fdfc50197c0>}
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 0x7fe09b7e5160>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe0b13486e0>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50a180>}
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 0x7fdfc5019a90>, 'valid': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50b350>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b509610>}
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 0x7fdfc5019280>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b509280>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b509670>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50bb60>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50b830>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50a3c0>}
Splitting by row index
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b509610>}
Multiple row index split
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fdfc5019010>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50a9f0>}
Specifying keys
{'train': <braindecode.datasets.base.BaseConcatDataset object at 0x7fdfc5019a90>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7fe09b50aba0>}
Total running time of the script: (0 minutes 4.279 seconds)
Estimated memory usage: 737 MB