Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
shape: list<item: int64>
child 0, item: int64
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>>
child 0, chunk_shape: list<item: int64>
child 0, item: int64
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
child 0, name: string
child 1, configuration: struct<separator: string>
child 0, separator: string
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64>>>
child 0, item: struct<name: string, configuration: struct<endian: string, level: int64>>
child 0, name: string
child 1, configuration: struct<endian: string, level: int64>
child 0, endian: string
child 1, level: int64
attributes: struct<>
zarr_format: int64
node_type: string
storage_transformers: list<item: null>
child 0, item: null
chunk_size: int64
pipeline_name: string
n_recordings: int64
total_size_mb: double
braindecode_version: string
format: string
compression_level: int64
compression: string
total_samples: int64
to
{'format': Value('string'), 'pipeline_name': Value('string'), 'compression': Value('string'), 'compression_level': Value('int64'), 'chunk_size': Value('int64'), 'braindecode_version': Value('string'), 'n_recordings': Value('int64'), 'total_samples': Value('int64'), 'total_size_mb': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
shape: list<item: int64>
child 0, item: int64
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>>
child 0, chunk_shape: list<item: int64>
child 0, item: int64
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
child 0, name: string
child 1, configuration: struct<separator: string>
child 0, separator: string
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<endian: string, level: int64>>>
child 0, item: struct<name: string, configuration: struct<endian: string, level: int64>>
child 0, name: string
child 1, configuration: struct<endian: string, level: int64>
child 0, endian: string
child 1, level: int64
attributes: struct<>
zarr_format: int64
node_type: string
storage_transformers: list<item: null>
child 0, item: null
chunk_size: int64
pipeline_name: string
n_recordings: int64
total_size_mb: double
braindecode_version: string
format: string
compression_level: int64
compression: string
total_samples: int64
to
{'format': Value('string'), 'pipeline_name': Value('string'), 'compression': Value('string'), 'compression_level': Value('int64'), 'chunk_size': Value('int64'), 'braindecode_version': Value('string'), 'n_recordings': Value('int64'), 'total_samples': Value('int64'), 'total_size_mb': Value('float64')}
because column names don't matchNeed 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 | 123 |
| Type | Windowed (from Epochs object) |
| Channels | 26 |
| Sampling frequency | 200 Hz |
| Total duration | 2 days, 5:21:13 |
| Windows/samples | 19,217 |
| Size | 3.16 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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