Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
format: string
pipeline_name: string
compression: string
compression_level: int64
braindecode_version: string
n_recordings: int64
total_samples: int64
total_size_mb: double
vs
shape: list<item: int64>
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>>
attributes: struct<>
zarr_format: int64
node_type: string
storage_transformers: list<item: null>
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
format: string
pipeline_name: string
compression: string
compression_level: int64
braindecode_version: string
n_recordings: int64
total_samples: int64
total_size_mb: double
vs
shape: list<item: int64>
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64, checksum: bool>>>
attributes: struct<>
zarr_format: int64
node_type: string
storage_transformers: list<item: null>Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
EEG Dataset
This dataset was created using braindecode, a deep learning library for EEG/MEG/ECoG signals.
Dataset Information
| Property | Value |
|---|---|
| Recordings | 45 |
| Type | Windowed (from Raw object) |
| Channels | 64 |
| Sampling frequency | 200 Hz |
| Total duration | 5:11:00 |
| Windows/samples | 6,000 |
| Size | 2.45 MB |
| Format | zarr |
Quick Start
from braindecode.datasets import BaseConcatDataset
# Load from Hugging Face Hub
dataset = BaseConcatDataset.pull_from_hub("username/dataset-name")
# Access a sample
X, y, metainfo = dataset[0]
# X: EEG data [n_channels, n_times]
# y: target label
# metainfo: window indices
Training with PyTorch
from torch.utils.data import DataLoader
loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)
for X, y, metainfo in loader:
# X: [batch_size, n_channels, n_times]
# y: [batch_size]
pass # Your training code
BIDS-inspired Structure
This dataset uses a BIDS-inspired organization. Metadata files follow BIDS conventions, while data is stored in Zarr format for efficient deep learning.
BIDS-style metadata:
dataset_description.json- Dataset informationparticipants.tsv- Subject metadata*_events.tsv- Trial/window events*_channels.tsv- Channel information*_eeg.json- Recording parameters
Data storage:
dataset.zarr/- Zarr format (optimized for random access)
sourcedata/braindecode/
├── dataset_description.json
├── participants.tsv
├── dataset.zarr/
└── sub-<label>/
└── eeg/
├── *_events.tsv
├── *_channels.tsv
└── *_eeg.json
Accessing Metadata
# Participants info
if hasattr(dataset, "participants"):
print(dataset.participants)
# Events for a recording
if hasattr(dataset.datasets[0], "bids_events"):
print(dataset.datasets[0].bids_events)
# Channel info
if hasattr(dataset.datasets[0], "bids_channels"):
print(dataset.datasets[0].bids_channels)
Created with braindecode
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