Split Dataset Example

In this example, we show multiple ways of how to split datasets.

# Authors: Lukas Gemein <l.gemein@gmail.com>
#
# License: BSD (3-clause)

from IPython.display import display

from braindecode.datasets import MOABBDataset
from braindecode.datautil.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])

Out:

/home/circleci/.local/lib/python3.7/site-packages/moabb/datasets/download.py:52: RuntimeWarning: Setting non-standard config type: "MNE_DATASETS_BNCI_PATH"
  set_config(key, osp.join(osp.expanduser("~"), "mne_data"))
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]
48 events found
Event IDs: [1 2 3 4]

ds has a pandas DataFrame with additional description of its internal datasets

display(ds.description)

Out:

    subject  ...    run
0         1  ...  run_0
1         1  ...  run_1
2         1  ...  run_2
3         1  ...  run_3
4         1  ...  run_4
5         1  ...  run_5
6         1  ...  run_0
7         1  ...  run_1
8         1  ...  run_2
9         1  ...  run_3
10        1  ...  run_4
11        1  ...  run_5

[12 rows x 3 columns]

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)

Out:

{'run_0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a1079d0>, 'run_1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c596ff510>, 'run_2': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a4b8750>, 'run_3': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c58527110>, 'run_4': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a400d50>, 'run_5': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a400ad0>}
   subject  ...    run
0        1  ...  run_4
1        1  ...  run_4

[2 rows x 3 columns]

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)

Out:

{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5851bf10>}
   subject  ...    run
0        1  ...  run_0
1        1  ...  run_1
2        1  ...  run_5

[3 rows x 3 columns]

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 integers

splits = ds.split([[0, 1, 5], [2, 3, 4], [6, 7, 8, 9, 10, 11]])
display(splits)
display(splits["2"].description)

Out:

{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a9a3f50>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a400d50>, '2': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a400750>}
   subject  ...    run
0        1  ...  run_0
1        1  ...  run_1
2        1  ...  run_2
3        1  ...  run_3
4        1  ...  run_4
5        1  ...  run_5

[6 rows x 3 columns]

Similarly, we can split datasets after creating windows

windows = create_windows_from_events(
    ds, trial_start_offset_samples=0, trial_stop_offset_samples=0)
splits = windows.split("run")
display(splits)
splits = windows.split([4, 8])
display(splits)
splits = windows.split([[4, 8], [5, 9, 11]])
display(splits)

Out:

Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
Used Annotations descriptions: ['feet', 'left_hand', 'right_hand', 'tongue']
Adding metadata with 4 columns
Replacing existing metadata with 4 columns
48 matching events found
No baseline correction applied
0 projection items activated
Loading data for 48 events and 1000 original time points ...
0 bad epochs dropped
{'run_0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5852cbd0>, 'run_1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c59779d50>, 'run_2': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c58558e90>, 'run_3': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c58558b50>, 'run_4': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c58558fd0>, 'run_5': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c596ee410>}
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c5a400750>}
{'0': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c58558fd0>, '1': <braindecode.datasets.base.BaseConcatDataset object at 0x7f3c58558410>}

Total running time of the script: ( 0 minutes 17.760 seconds)

Estimated memory usage: 406 MB

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