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
shards: list<item: struct<uri: string, n_traces: int64>>
child 0, item: struct<uri: string, n_traces: int64>
child 0, uri: string
child 1, n_traces: int64
n_shards: int64
n_traces: int64
sfreq: double
mu: double
n_samples: int64
bank_duration_s: double
theta_range: list<item: double>
child 0, item: double
sigma_range: list<item: double>
child 0, item: double
a_values: list<item: double>
child 0, item: double
b_values: list<item: double>
child 0, item: double
n_ou: int64
n_jr: int64
jr_fraction: double
shard_traces: int64
random_state: int64
min_channels: int64
position_montage: string
channel_labels: list<item: string>
child 0, item: string
records_per_kernel: int64
channel_positions: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
spatial_correlation_range: list<item: double>
child 0, item: double
n_records: int64
max_kernels: int64
max_channels: int64
min_kernels: int64
montage: string
to
{'shards': List({'uri': Value('string'), 'n_records': Value('int64')}), 'n_shards': Value('int64'), 'n_records': Value('int64'), 'channel_labels': List(Value('string')), 'sfreq': Value('float64'), 'bank_duration_s': Value('float64'), 'n_samples': Value('int64'), 'min_channels': Value('int64'), 'max_channels': Value('int64'), 'montage': Value('string'), 'position_montage': Value('string'), 'channel_positions': List(List(Value('float64'))), 'min_kernels': Value('int64'), 'max_kernels': Value('int64'), 'spatial_correlation_range': List(Value('float64')), 'records_per_kernel': Value('int64'), 'random_state': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
shards: list<item: struct<uri: string, n_traces: int64>>
child 0, item: struct<uri: string, n_traces: int64>
child 0, uri: string
child 1, n_traces: int64
n_shards: int64
n_traces: int64
sfreq: double
mu: double
n_samples: int64
bank_duration_s: double
theta_range: list<item: double>
child 0, item: double
sigma_range: list<item: double>
child 0, item: double
a_values: list<item: double>
child 0, item: double
b_values: list<item: double>
child 0, item: double
n_ou: int64
n_jr: int64
jr_fraction: double
shard_traces: int64
random_state: int64
min_channels: int64
position_montage: string
channel_labels: list<item: string>
child 0, item: string
records_per_kernel: int64
channel_positions: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
spatial_correlation_range: list<item: double>
child 0, item: double
n_records: int64
max_kernels: int64
max_channels: int64
min_kernels: int64
montage: string
to
{'shards': List({'uri': Value('string'), 'n_records': Value('int64')}), 'n_shards': Value('int64'), 'n_records': Value('int64'), 'channel_labels': List(Value('string')), 'sfreq': Value('float64'), 'bank_duration_s': Value('float64'), 'n_samples': Value('int64'), 'min_channels': Value('int64'), 'max_channels': Value('int64'), 'montage': Value('string'), 'position_montage': Value('string'), 'channel_positions': List(List(Value('float64'))), 'min_kernels': Value('int64'), 'max_kernels': Value('int64'), 'spatial_correlation_range': List(Value('float64')), 'records_per_kernel': Value('int64'), 'random_state': Value('int64')}
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.
Synthetic data for EEG
This repo contains all the new datasets introduced and used for our paper "On the impact of synthetic data for EEG foundation models pretraining". Those synthetic datasets where generated using the code avaialable here [https://github.com/JibrilEl/syntheticdataforeeg]
Dataset Details
This repo contains three synthetic EEG datasets :
- Ornstein-Uhlenbeck noise
- Multivariate GP
- TVB Jansen-Rit
Further information about each of those datasets can be found in our paper [(https://hal.science/view/index/docid/5785447)]
Uses
This dataset was initially developed to pretrain EEG foundation models, but you can do anything you like with them.
Citation
If you find our work useful, please cite us :
@inproceedings{elhassani2026syntheticdataforeeg,
title={Data Augmentation & Synthetic Data for EEG},
author={Jibril El hassani, Ugo Tanielian},
year={2026}
}
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