DS004279#

Large Spanish EEG

Access recordings and metadata through EEGDash.

Citation: Carlos Valle Araya, Carolina Mendez-Orellana, Maria Rodriguez-Fernandez (2022). Large Spanish EEG. 10.18112/openneuro.ds004279.v1.1.2

Modality: eeg Subjects: 56 Recordings: 305 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004279

dataset = DS004279(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004279(cache_dir="./data", subject="01")

Advanced query

dataset = DS004279(
    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{ds004279,
  title = {Large Spanish EEG},
  author = {Carlos Valle Araya and Carolina Mendez-Orellana and Maria Rodriguez-Fernandez},
  doi = {10.18112/openneuro.ds004279.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds004279.v1.1.2},
}

About This Dataset#

EEG: silent and perceive speech on 30 Spanish sentences Large Spanish Speech EEG dataset

Authors <ul>

<li>Carlos Valle</li> <li>Carolina Mendez-Orellana</li> <li>María Rodríguez-Fernández</li>

</ul>

View full README

EEG: silent and perceive speech on 30 Spanish sentences Large Spanish Speech EEG dataset

Authors <ul>

<li>Carlos Valle</li> <li>Carolina Mendez-Orellana</li> <li>María Rodríguez-Fernández</li>

</ul>

Resources: <ul>

<li>Code availaible at: cgvalle/Large_Spanish_EEG</li> <li>Publication: Valle, C., Mendez-Orellana, C., Herff, C., & Rodriguez-Fernandez, M. (2024). Identification of perceived sentences using deep neural networks in EEG. Journal of neural engineering, 21(5), 056044. </li>

</ul>

Abstract: Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks have shown great potential for speech decoding applications, but the large data sets required for these models are usually not available for neural recordings of speech impaired subjects. Harnessing data from other participants would thus be ideal to create speech neuroprostheses without the need of patient-specific training data. In this study, we recorded 60 sessions from 56 healthy participants using 64 EEG channels and developed a neural network capable of subject-independent classification of perceived sentences. We found that sentence identity can be decoded from subjects without prior training achieving higher accuracy than mixed-subject models. The development of subject-independent models eliminates the need to collect data from a target subject, reducing time and data collection costs during deployment. These results open new avenues for creating speech neuroprostheses when subjects cannot provide training data.

Experiment Design: We investigated the neural signals recorded using a 64-channel EEG system during speech perception and silent speech production tasks involving 30 daily use sentences in Spanish. The participants were instructed to listen to a spoken sentence from an audio recording and then silently repeat the sentence without any motor action.

The experimental design, a modified version of a previous study (Dash, et al), comprises four segments: rest, perception, preparation, and silent speech production. The rest segment lasted five seconds, presenting a fixation cross (+) before the stimulus onset.

During the perception section, the participants listened to the stimulus. Prior to subject S18, the perception section lasted 4 s, with each sentence being repeated 7 times. From subject S19 onward, the duration of the perception segment was increased to 5 s to match the duration of the silent speech production segment and the number of repetitions per sentence was decreased to 6 in order to maintain the overall length of the experiment. The preparation segment lasted one second and presented a blank screen, serving as a separation marker between the perception and silent speech production tasks. In the silent speech production segment lasting five seconds, subjects were instructed to silently repeat the previously heard stimulus without motor action only once. It is important to note that this study exclusively focuses on the outcomes derived from the speech perception task.

Trials for each block of the 30 stimuli were presented in a randomized order to prevent anticipation and learning biases.

Contact: Please contact us at this e-mail address if you have any question: cgvalle@uc.cl

Dataset Information#

Dataset ID

DS004279

Title

Large Spanish EEG

Year

2022

Authors

Carlos Valle Araya, Carolina Mendez-Orellana, Maria Rodriguez-Fernandez

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004279.v1.1.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004279,
  title = {Large Spanish EEG},
  author = {Carlos Valle Araya and Carolina Mendez-Orellana and Maria Rodriguez-Fernandez},
  doi = {10.18112/openneuro.ds004279.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds004279.v1.1.2},
}

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: 56

  • Recordings: 305

  • Tasks: 1

Channels & sampling rate

  • Channels: 64 (60), 69 (60)

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 25.2 GB

  • File count: 305

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004279.v1.1.2

Provenance

API Reference#

Use the DS004279 class to access this dataset programmatically.

class eegdash.dataset.DS004279(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004279. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 56; recordings: 60; 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/ds004279 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004279

Examples

>>> from eegdash.dataset import DS004279
>>> dataset = DS004279(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

See Also#