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 0x7f533ed84c50>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f533ee028d0>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea0200>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea2960>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea22a0>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea2900>}

	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 0x7f5200683740>}

	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 0x7f51fdea3200>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea1a30>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea2960>}

	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 0x7f51fdea2750>, 'valid': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea2090>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea1190>}

	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 0x7f51fda91670>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51ff90a900>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea3440>, '3': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea2900>, '4': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea33e0>, '5': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea0980>}

Splitting by row index
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea1190>}

Multiple row index split
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f52005db380>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea0980>}

Specifying keys
{'train': <braindecode.datasets.base.BaseConcatDataset object at 0x7f5200681070>, 'test': <braindecode.datasets.base.BaseConcatDataset object at 0x7f51fdea2a50>}

Estimated memory usage: 1311 MB