DS004196#

Bimodal dataset on Inner speech

Access recordings and metadata through EEGDash.

Citation: Foteini Liwicki, Vibha Gupta, Rajkumar Saini, Kanjar De, Nosheen Abid, Sumit Rakesh, Scott Wellington, Holly Wilson, Marcus Liwicki, Johan Eriksson (2022). Bimodal dataset on Inner speech. 10.18112/openneuro.ds004196.v2.0.2

Modality: eeg Subjects: 4 Recordings: 109 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Dataset Information#

Dataset ID	`DS004196`
Title	Bimodal dataset on Inner speech
Year	2022
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	doi:10.18112/openneuro.ds004196.v2.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 4
Recordings: 109
Tasks: 2

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS004196 class to access this dataset programmatically.

class eegdash.dataset.DS004196(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004196. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None