Dataset Viewer
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The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed 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 | 683 |
| Type | Windowed (from Epochs object) |
| Channels | 17 |
| Sampling frequency | 200 Hz |
| Total duration | 4 days, 6:51:14 |
| Windows/samples | 37,046 |
| Size | 12.82 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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