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