Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
demo_dataset (alternate split seed=20260726)
Processed EEG demo package matching the layout of USuCgex0122e/demo_dataset,
with subject-disjoint split seed 20260726 (primary release uses 20260725).
Large datasets are sparsely subsampled to approximate original demo sizes.
Per dataset: train.h5 / val.h5 / test.h5 (X, y) + Croissant metadata.
Included datasets
AD65— subjects=88, splits={'train': 4594, 'val': 1229, 'test': 1190}, sparse=FalseBCIC2A— subjects=9, splits={'train': 2098, 'val': 1091, 'test': 1507}, sparse=FalseCHB_MIT— subjects=24, splits={'train': 1520, 'val': 25338, 'test': 23407}, sparse=TrueCIRE— subjects=18, splits={'train': 1105, 'val': 190, 'test': 237}, sparse=FalseDEAP— subjects=32, splits={'train': 5520, 'val': 1920, 'test': 240}, sparse=FalseEEGMAT— subjects=36, splits={'train': 661, 'val': 96, 'test': 96}, sparse=FalseEEG_SVRec— subjects=27, splits={'train': 44797, 'val': 3981, 'test': 25106}, sparse=FalseISRUC_S1— subjects=100, splits={'train': 9125, 'val': 5237, 'test': 4598}, sparse=TrueMDD— subjects=62, splits={'train': 4594, 'val': 1234, 'test': 695}, sparse=FalseMODMA— subjects=53, splits={'train': 557, 'val': 76, 'test': 165}, sparse=FalseMusicEEG— subjects=31, splits={'train': 9280, 'val': 2608, 'test': 3968}, sparse=FalsePhysionet_MI— subjects=109, splits={'train': 6483, 'val': 1440, 'test': 1914}, sparse=FalseSEED_V— subjects=16, splits={'train': 629, 'val': 3564, 'test': 1782}, sparse=TrueSEED_VIG— subjects=21, splits={'train': 14160, 'val': 4425, 'test': 1770}, sparse=FalseSEED_VII— subjects=20, splits={'train': 4480, 'val': 2734, 'test': 2734}, sparse=TrueSHU_MI— subjects=25, splits={'train': 8074, 'val': 1873, 'test': 1252}, sparse=FalseSiena_EEG— subjects=14, splits={'train': 844, 'val': 1804, 'test': 3157}, sparse=TrueSleepEDF— subjects=100, splits={'train': 22790, 'val': 40989, 'test': 43334}, sparse=TrueTUAB— subjects=2329, splits={'train': 10400, 'val': 4967, 'test': 5277}, sparse=TrueTUEV— subjects=291, splits={'train': 8370, 'val': 2496, 'test': 1684}, sparse=FalseTUSL— subjects=38, splits={'train': 231, 'val': 21, 'test': 48}, sparse=FalseTUSZ— subjects=74, splits={'train': 3000, 'val': 1000, 'test': 1000}, sparse=TrueThings_EEG2— subjects=10, splits={'train': 640, 'val': 2000, 'test': 2000}, sparse=TrueWorkload— subjects=12, splits={'train': 7632, 'val': 2862, 'test': 954}, sparse=False
- Downloads last month
- 224