DS003626#

Inner Speech

Access recordings and metadata through EEGDash.

Citation: Nicolas Nieto, Victoria Peterson, Hugo Rufiner, Juan Kamienkowski, Ruben Spies (2021). Inner Speech. 10.18112/openneuro.ds003626.v2.1.2

Modality: eeg Subjects: 10 Recordings: 33 License: CC0 Source: openneuro Citations: 6.0

Metadata: Good (80%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.v2.1.2},
  url = {https://doi.org/10.18112/openneuro.ds003626.v2.1.2},
}

About This Dataset#

Inner Speech Dataset.

Author: Nicolás Nieto

Code available at: N-Nieto/Inner_Speech_Dataset

Publication available at: https://www.nature.com/articles/s41597-022-01147-2

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

Dataset Information#

Dataset ID	`DS003626`
Title	Inner Speech
Year	2021
Authors	Nicolas Nieto, Victoria Peterson, Hugo Rufiner, Juan Kamienkowski, Ruben Spies
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003626.v2.1.2
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: 10
Recordings: 33
Tasks: —

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): 0.0

API Reference#

Use the DS003626 class to access this dataset programmatically.

class eegdash.dataset.DS003626(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003626. Modality: eeg; Experiment type: Motor; Subject type: Healthy. 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

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/ds003626 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003626

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, 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