DS006104#

EEG dataset for speech decoding

Access recordings and metadata through EEGDash.

Citation: João Pedro Carvalho Moreira, Vinícius Rezende Carvalho, Eduardo Mazoni Andrade Marçal Mendes, Ariah Fallah, Terrence J. Sejnowski, Claudia Lainscsek, Lindy Comstock (2025). EEG dataset for speech decoding. 10.18112/openneuro.ds006104.v1.0.1

Modality: eeg Subjects: 24 Recordings: 309 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006104

dataset = DS006104(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006104(cache_dir="./data", subject="01")

Advanced query

dataset = DS006104(
    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{ds006104,
  title = {EEG dataset for speech decoding},
  author = {João Pedro Carvalho Moreira and Vinícius Rezende Carvalho and Eduardo Mazoni Andrade Marçal Mendes and Ariah Fallah and Terrence J. Sejnowski and Claudia Lainscsek and Lindy Comstock},
  doi = {10.18112/openneuro.ds006104.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006104.v1.0.1},
}

About This Dataset#

EEG dataset for speech decoding

Dataset Overview

This dataset contains EEG recordings from a phoneme discrimination task with TMS. The data were collected during two related studies in 2019 and 2021.

View full README

EEG dataset for speech decoding

Dataset Overview

This dataset contains EEG recordings from a phoneme discrimination task with TMS. The data were collected during two related studies in 2019 and 2021.

Study 1 (2019, Session 01): - 8 participants (P01-P08) - Focus on CV and VC phoneme pairs - 2 blocks: CV pairs and VC pairs - TMS targeted to LipM1 (-56, -8, 46) and TongueM1 (-60, -10, 25)

Study 2 (2021, Session 02): - 16 participants (S01-S16) - Expanded to include single phonemes and phoneme triplets - 4 blocks: single phonemes, CV pairs, real words, and pseudowords - Additional TMS targets included Broca’s area (BA 44: -51, 7, 23) and verbal memory region (BA 6: -46, 1, 41)

Task Description

Participants listened to speech sounds and identified stimuli with a button-press response. The stimuli included: 1. Single phonemes - Consonants (/b/, /p/, /d/, /t/, /s/, /z/) and vowels (/i/, /E/, /A/, /u/, /oU/) 2. Phoneme pairs - CV and VC combinations of the phonemes 3. Phoneme triplets - Real and pseudowords constructed of CVC sequences

TMS Methodology

Detailed information about TMS parameters can be found in the sourcedata/tms_metadata/tms_parameters.json file. TMS was applied using a Magstim Super Rapid Plus1 stimulator with a figure-of-eight 40 mm coil. Stimulation was delivered at 110% of resting motor threshold as paired pulses with 50ms interpulse interval.

Detailed information about the methodology and results can be found in the associated publication: Moreira et al. “An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation”

Directory Structure

The dataset follows BIDS convention with the following structure: /sub-[subject]/ses-[session]/eeg/ Where subject is P01-P08 for Study 1 and S01-S16 for Study 2. Session is 01 for Study 1 and 02 for Study 2.

Contact Information

For questions about this dataset, please contact Lindy Comstock at lbcomstock@ucla.edu

Dataset Information#

Dataset ID

DS006104

Title

EEG dataset for speech decoding

Year

2025

Authors

João Pedro Carvalho Moreira, Vinícius Rezende Carvalho, Eduardo Mazoni Andrade Marçal Mendes, Ariah Fallah, Terrence J. Sejnowski, Claudia Lainscsek, Lindy Comstock

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006104.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL

Copy-paste BibTeX
@dataset{ds006104,
  title = {EEG dataset for speech decoding},
  author = {João Pedro Carvalho Moreira and Vinícius Rezende Carvalho and Eduardo Mazoni Andrade Marçal Mendes and Ariah Fallah and Terrence J. Sejnowski and Claudia Lainscsek and Lindy Comstock},
  doi = {10.18112/openneuro.ds006104.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006104.v1.0.1},
}

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: 24

  • Recordings: 309

  • Tasks: 3

Channels & sampling rate
  • Channels: 61 (106), 83 (6)

  • Sampling rate (Hz): 2000.0

  • Duration (hours): 0.0

Tags
  • Pathology: Healthy

  • Modality: Auditory

  • Type: Perception

Files & format
  • Size on disk: 43.0 GB

  • File count: 309

  • Format: BIDS

License & citation
  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006104.v1.0.1

Provenance

API Reference#

Use the DS006104 class to access this dataset programmatically.

class eegdash.dataset.DS006104(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006104. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 56; tasks: 3.

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/ds006104 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006104

Examples

>>> from eegdash.dataset import DS006104
>>> dataset = DS006104(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#