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="BNCI2014_001", subject_ids=[1])

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

	subject	session	run
0	1	0train	0
1	1	0train	1
2	1	0train	2
3	1	0train	3
4	1	0train	4
5	1	0train	5
6	1	1test	0
7	1	1test	1
8	1	1test	2
9	1	1test	3
10	1	1test	4
11	1	1test	5

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 0x7fd3d325d670>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3d0f7e300>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3ef2b84a0>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d2000>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d1df0>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d32f0>}

	subject	session	run
0	1	0train	4
1	1	1test	4

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 0x7fd3d0f7f6b0>}

	subject	session	run
0	1	0train	0
1	1	0train	1
2	1	0train	5

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 0x7fd3bb0d0c20>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d1df0>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d06b0>}

	subject	session	run
0	1	1test	0
1	1	1test	1
2	1	1test	2
3	1	1test	3
4	1	1test	4
5	1	1test	5

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 0x7fd3f55ab3b0>, 'valid': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d2810>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d03e0>}

	subject	session	run
0	1	1test	0
1	1	1test	1
2	1	1test	2
3	1	1test	3
4	1	1test	4
5	1	1test	5

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
)

# 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 0x7fd522815670>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd522266150>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3d0f7f6b0>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3d0f7e0c0>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3d0f7c5c0>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3bb0d1df0>}

Splitting by row index
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3f55ab3b0>}

Multiple row index split
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3d0f7e0c0>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3d0f7e300>}

Specifying keys
{'train': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd3f55ab3b0>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7fd522815670>}

Estimated memory usage: 978 MB