DS003626: eeg dataset, 10 subjects#
Inner Speech
Citation: Nicolas Nieto, Victoria Peterson, Hugo Rufiner, Juan Kamienkowski, Ruben Spies (20). Inner Speech. 10.18112/openneuro.ds003626.v2.0.0
10-participant EEG dataset — Inner Speech.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003626
dataset = DS003626(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003626(cache_dir="./data", subject="01")
Advanced query
dataset = DS003626(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds003626,
title = {Inner Speech},
author = {Nicolas Nieto and Victoria Peterson and Hugo Rufiner and Juan Kamienkowski and Ruben Spies},
doi = {10.18112/openneuro.ds003626.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds003626.v2.0.0},
}
About This Dataset#
Inner Speech Dataset.
Author: Nicolas Nieto
Code available at N-Nieto/Inner_Speech_Dataset Prepreint available at https://www.biorxiv.org/content/10.1101/2021.04.19.440473v1 Abstract:
Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces.
Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the “inner voice” phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a “natural” way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-subjects dataset acquired under this and two others related paradigms, obtain with an acquisition systems of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms. Conditions = Inner Speech, Pronounced Speech, Visualized Condition Classes = “Arriba/Up”, “Abajo/Down”, “Derecha/Right”, “Izquierda/Left” Total Trials = 5640 Please contact us at this e-mail address if you have any doubts: nnieto@sinc.unl.edu.ar
Cohort#
Signal · Electrodes & live trace#
Live trace viewer — sub-08 · ses-02 · task-innerspeech
Showing one representative recording out of
10 subjects and 30 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Inner Speech |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Nicolas Nieto, Victoria Peterson, Hugo Rufiner, Juan Kamienkowski, Ruben Spies |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003626,
title = {Inner Speech},
author = {Nicolas Nieto and Victoria Peterson and Hugo Rufiner and Juan Kamienkowski and Ruben Spies},
doi = {10.18112/openneuro.ds003626.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds003626.v2.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003626 · Nieto2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003626(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Inner Speech
- Study:
ds003626(OpenNeuro)- Author (year):
Nieto2021- Canonical:
—
Also importable as:
DS003626,Nieto2021.Modality:
eeg. Subjects: 10; recordings: 30; tasks: 1.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds003626 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003626 DOI: https://doi.org/10.18112/openneuro.ds003626.v2.0.0 NEMAR citation count: 6
Examples
>>> from eegdash.dataset import DS003626 >>> dataset = DS003626(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds003626").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds003626 to reproduce the tutorial on this dataset.
Citation
Nicolas Nieto, Victoria Peterson, Hugo Rufiner, Juan Kamienkowski, Ruben Spies (20). Inner Speech. 10.18112/openneuro.ds003626.v2.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003626.v2.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset