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--- |
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dataset_info: |
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features: |
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- name: data |
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sequence: |
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sequence: float32 |
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- name: label |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 531730928 |
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num_examples: 4324 |
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- name: val |
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num_bytes: 5164824 |
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num_examples: 42 |
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- name: test |
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num_bytes: 5164824 |
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num_examples: 42 |
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download_size: 207795149 |
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dataset_size: 542060576 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: val |
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path: data/val-* |
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- split: test |
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path: data/test-* |
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license: odc-by |
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--- |
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The [EEG Motor Movement/Imagery (MMI) Dataset](https://physionet.org/content/eegmmidb/1.0.0/) preprocessed with [DN3](https://github.com/SPOClab-ca/dn3/) to be used for downstream fine-tuning with [BENDR](https://github.com/SPOClab-ca/BENDR). |
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The labels correspond to Task 4 (imagine opening and closing both fists or both feet) from experimental runs 4, 10 and 14. |
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## Creating dataloaders |
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```python |
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from datasets import load_dataset |
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from torch.utils.data import DataLoader |
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dataset = load_dataset("rasgaard/mmi-bendr-preprocessed") |
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dataset.set_format("torch") |
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train_loader = DataLoader(dataset["train"], batch_size=8) |
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val_loader = DataLoader(dataset["val"], batch_size=8) |
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test_loader = DataLoader(dataset["test"], batch_size=8) |
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batch = next(iter(train_loader)) |
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batch["data"].shape, batch["label"].shape |
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>>> (torch.Size([8, 20, 1536]), torch.Size([8])) |
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``` |
