EEGdash›NeMAR›NM000258

Iss. 258 · 15 subjects · 15 recordings · other-open

Dataset Brief · Imagined Speech EEG database — Spanish vowels and commands (P…

NM000258: eeg dataset, 15 subjects#

Name: Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)
License: other-open

Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)

Access recordings and metadata through EEGDash.

Citation: German A. Pressel Coretto, Ivan E. Gareis, Hugo Leonardo Rufiner (20). Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016). 10.82901/nemar.nm000258

Modality: eeg Subjects: 15 Recordings: 15 License: other-open Source: nemar

Metadata: Complete (100%)

15-participant EEG dataset — Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016).

Data & curation German A. Pressel Coretto · Ivan E. Gareis · Hugo Leonardo Rufiner
Senior author Hugo Leonardo Rufiner
Year 20 · Distributed via NeMAR

EEG · 6 ch1024 HzBIDS 1.9.0Task · imagery

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000258

dataset = NM000258(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 = NM000258(cache_dir="./data", subject="01")

Advanced query

dataset = NM000258(
    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{nm000258,
  title = {Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)},
  author = {German A. Pressel Coretto and Ivan E. Gareis and Hugo Leonardo Rufiner},
  doi = {10.82901/nemar.nm000258},
  url = {https://doi.org/10.82901/nemar.nm000258},
}

§ 02Study · The README

About This Dataset#

An open-access EEG dataset comprising recordings from 15 healthy Spanish-speaking subjects during imagined speech tasks. The dataset includes 11 imagery classes: 5 Spanish vowels and 6 directional commands, acquired using 6-channel EEG at 1024 Hz with preprocessed data (bandpass filtered 2-45 Hz). Trials consist of 4-second periods with a 3-second imagery window occurring within each trial. This resource supports brain-computer interface research and motor imagery classification studies.

EEG signals were recorded from 15 healthy subjects (age 24-28 years) using a Grass 8-18-36 amplifier with DataTranslation DT9816 ADC. Six channels (F3, F4, C3, C4, P3, P4) were positioned according to the standard 10-20 montage. Sampling rate was 1024 Hz with online bandpass filtering (2-45 Hz). Subjects performed cue-based imagery tasks of 5 Spanish vowels and 6 directional commands in response to visual stimuli, with trial duration of 4 seconds and imagery period of 3 seconds. Data were preprocessed with bandpass filtering and artifact rejection applied.

DOI

Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)

Overview

How to Access via MOABB

Install MOABB and load this dataset directly:

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000258-blue)](https://doi.org/10.82901/nemar.nm000258) # Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016) ## Overview An open-access EEG dataset comprising recordings from 15 healthy Spanish-speaking subjects during imagined speech tasks. The dataset includes 11 imagery classes: 5 Spanish vowels and 6 directional commands, acquired using 6-channel EEG at 1024 Hz with preprocessed data (bandpass filtered 2-45 Hz). Trials consist of 4-second periods with a 3-second imagery window occurring within each trial. This resource supports brain-computer interface research and motor imagery classification studies. ## Dataset Summary | Property | Value | |---|—| | Subjects | 15 | | Channels | 6 | | Classes | 11 | | Trial length | 4 s | | Sampling frequency | 1024 Hz | | Sessions | 1 | | Total trials | 8670 | | Paradigm | MotorImagery | ## Data Collection Methods EEG signals were recorded from 15 healthy subjects (age 24-28 years) using a Grass 8-18-36 amplifier with DataTranslation DT9816 ADC. Six channels (F3, F4, C3, C4, P3, P4) were positioned according to the standard 10-20 montage. Sampling rate was 1024 Hz with online bandpass filtering (2-45 Hz). Subjects performed cue-based imagery tasks of 5 Spanish vowels and 6 directional commands in response to visual stimuli, with trial duration of 4 seconds and imagery period of 3 seconds. Data were preprocessed with bandpass filtering and artifact rejection applied. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import Pressel2016 from moabb.paradigms import MotorImagery paradigm = MotorImagery() dataset = Pressel2016() X, y, metadata = paradigm.get_data(dataset) ` For more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the [MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Pressel2016.html). ## Citation If you use this dataset please cite the primary publication: > DOI: [10.1117/12.2255697](https://doi.org/10.1117/12.2255697) ## NEMAR / MOABB Benchmark Collection This BIDS-formatted dataset was converted from the original data using the [MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on [NEMAR](https://nemar.org/) as part of the MOABB benchmark collection. The original data and license terms apply — see dataset_description.json for details.

License: other-open

Authors:

German A. Pressel Coretto
Ivan E. Gareis
Hugo Leonardo Rufiner

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000258

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 6 ch (n=15 recordings)

Sampling frequencies: 1024.0 Hz (n=15 recordings)

Total recording duration: 8 h 33 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 6 ch · EEG · 1024 Hz · 15 subjects, 15 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 15 subjects and 15 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 6 sensors — 6 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000258(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000258

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000258(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)

Study:: nm000258 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000258.

Modality: eeg; Subject type: Unknown. Subjects: 15; recordings: 15; 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/nm000258 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000258 DOI: https://doi.org/10.82901/nemar.nm000258

Examples

>>> from eegdash.dataset import NM000258
>>> dataset = NM000258(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: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000258.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000258 to reproduce the tutorial on this dataset.

Citation

German A. Pressel Coretto, Ivan E. Gareis, Hugo Leonardo Rufiner (20). Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016). 10.82901/nemar.nm000258

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000258.

Related & sibling datasets

ON006563EEG · 12 subj ON006695EEG · 19 subj ON004147EEG · 12 subj ON004995EEG · 20 subj ON007406EEG · 10 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

other-open · 10.82901/nemar.nm000258

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000258`
Title	Imagined Speech EEG database — Spanish vowels and commands (Pressel et al. 2016)
Author (year)	—
Canonical	—
Importable as	`NM000258`
Year	20
Authors	German A. Pressel Coretto, Ivan E. Gareis, Hugo Leonardo Rufiner
License	other-open
Citation / DOI	10.82901/nemar.nm000258
Source links	OpenNeuro \| NeMAR \| Source URL

NM000258: eeg dataset, 15 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#