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 |
|
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 |
|
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!
Technical Details#
Subjects: 24
Recordings: 309
Tasks: 3
Channels: 61 (106), 83 (6)
Sampling rate (Hz): 2000.0
Duration (hours): 0.0
Pathology: Healthy
Modality: Auditory
Type: Perception
Size on disk: 43.0 GB
File count: 309
Format: BIDS
License: CC0
DOI: doi:10.18112/openneuro.ds006104.v1.0.1
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:
EEGDashDatasetOpenNeuro 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.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/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()
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset