DS004196: eeg dataset, 4 subjects#
Bimodal dataset on Inner speech
Citation: Foteini Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, Sumit Rakesh, Scott Wellington, Holly Wilson, Marcus Liwicki, Johan Eriksson (—). Bimodal dataset on Inner speech. 10.18112/openneuro.ds004196.v2.0.2
4-participant EEG dataset — Bimodal dataset on Inner speech.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004196
dataset = DS004196(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004196(cache_dir="./data", subject="01")
Advanced query
dataset = DS004196(
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{ds004196,
title = {Bimodal dataset on Inner speech},
author = {Foteini Liwicki and Vibha Gupta and Rajkumar Saini and Kanjar De and Nosheen Abid and Sumit Rakesh and Scott Wellington and Holly Wilson and Marcus Liwicki and Johan Eriksson},
doi = {10.18112/openneuro.ds004196.v2.0.2},
url = {https://doi.org/10.18112/openneuro.ds004196.v2.0.2},
}
About This Dataset#
Bimodal dataset on Inner Speech
Code available: LTU-Machine-Learning/Inner_Speech_EEG_FMRI Publication available: https://www.nature.com/articles/s41597-023-02286-w Abstract:
The recognition of inner speech, which could give a \`voice’ to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant.
The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses. Short Dataset description:
The dataset consists of 1280 trials in each modality (EEG, FMRI).
The stimuli contain 8 words, selected from 2 different categories (social, numeric):
Social: child, daughter, father, wife Numeric: four, three, ten, six There are 4 subjects in total: sub-01, sub-02, sub-03, sub-05. Initially, there were 5 participants, however, sub-04 data was rejected due to high fluctuations. Details of valid data are available in the file participants.tsv.
For questions please contact: foteini.liwicki@ltu.se
Cohort#
Dataset Statistics#
Age distribution (n=4, range 33–52 yr, mean 39.8 yr · sex per subject not reported)
Sex composition
Channel counts: 64 ch (n=4 recordings)
Sampling frequencies: 512.0 Hz (n=4 recordings)
Total recording duration: 1 h 30 min
Signal · Electrodes & live trace#
Live trace viewer — sub-01 · ses-EEG · task-inner
Showing one representative recording out of
4 subjects and 4 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 |
Bimodal dataset on Inner speech |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Foteini Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, Sumit Rakesh, Scott Wellington, Holly Wilson, Marcus Liwicki, Johan Eriksson |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004196,
title = {Bimodal dataset on Inner speech},
author = {Foteini Liwicki and Vibha Gupta and Rajkumar Saini and Kanjar De and Nosheen Abid and Sumit Rakesh and Scott Wellington and Holly Wilson and Marcus Liwicki and Johan Eriksson},
doi = {10.18112/openneuro.ds004196.v2.0.2},
url = {https://doi.org/10.18112/openneuro.ds004196.v2.0.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004196 · Liwicki2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004196(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Bimodal dataset on Inner speech
- Study:
ds004196(OpenNeuro)- Author (year):
Liwicki2022- Canonical:
—
Also importable as:
DS004196,Liwicki2022.Modality:
eeg. Subjects: 4; recordings: 4; 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/ds004196 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004196 DOI: https://doi.org/10.18112/openneuro.ds004196.v2.0.2 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004196 >>> dataset = DS004196(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/ds004196").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 ds004196 to reproduce the tutorial on this dataset.
Citation
Foteini Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, … (n.d.). Bimodal dataset on Inner speech. 10.18112/openneuro.ds004196.v2.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004196.v2.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset