NM000113: eeg dataset, 15 subjects#
2020 BCI competition, track 3
Citation: Seong-Whan Lee, Klaus-Robert Müller, José del R. Millán (20). 2020 BCI competition, track 3. 10.82901/nemar.nm000113
15-participant EEG dataset — 2020 BCI competition, track 3.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000113
dataset = NM000113(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000113(cache_dir="./data", subject="01")
Advanced query
dataset = NM000113(
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{nm000113,
title = {2020 BCI competition, track 3},
author = {Seong-Whan Lee and Klaus-Robert Müller and José del R. Millán},
doi = {10.82901/nemar.nm000113},
url = {https://doi.org/10.82901/nemar.nm000113},
}
About This Dataset#
The 2020 BCI Competition Track 3 dataset contains EEG recordings from participants performing imagined speech tasks. This dataset was designed for brain-computer interface research focused on decoding imagined speech from brain signals. The dataset includes recordings from 15 subjects performing five different imagined speech commands: “Hello”, “Help me”, “Stop”, “Thank you”, and “Yes”. The data is divided into training, validation, and test sets to facilitate machine learning approaches to imagined speech classification.
Participants performed imagined speech tasks where they were instructed to mentally articulate five different phrases without producing any audible speech or overt mouth movements. The five imagined speech commands were: “Hello”, “Help me”, “Stop”, “Thank you”, and “Yes”. EEG signals were recorded during these mental articulation tasks. The dataset is split into three sets: Training Set (run-00), Validation Set (run-01), and Test Set (run-02). Each recording session contains multiple trials of imagined speech, with each trial corresponding to one of the five command categories. The EEG data was recorded using a multi-channel EEG system, and the exact number of channels and their montage are preserved in the BIDS format.
2020 BCI competition, track 3
Introduction
Description of the preprocessing if any
The original MATLAB (.mat) files from the BCI Competition have been converted to BIDS-compliant EDF format. For training and validation sets, the data was stored in structured MATLAB arrays with fields for EEG data (‘x’), labels (‘y’), sampling frequency (‘fs’), and channel labels (‘clab’). For the test set, the data was stored in HDF5 format and labels were extracted from the Track3_Answer Sheet_Test.xlsx file. The EEG data has been scaled from the original units to Volts (multiplied by 1e-6). The epoched data structure from the original dataset has been concatenated into continuous recordings for BIDS compliance, with annotations marking the onset and duration of each imagined speech trial. Channel names and montage information from the original ‘mnt’ (montage) structure have been preserved in the BIDS format.
View full README
2020 BCI competition, track 3
Introduction
Description of the preprocessing if any
The original MATLAB (.mat) files from the BCI Competition have been converted to BIDS-compliant EDF format. For training and validation sets, the data was stored in structured MATLAB arrays with fields for EEG data (‘x’), labels (‘y’), sampling frequency (‘fs’), and channel labels (‘clab’). For the test set, the data was stored in HDF5 format and labels were extracted from the Track3_Answer Sheet_Test.xlsx file. The EEG data has been scaled from the original units to Volts (multiplied by 1e-6). The epoched data structure from the original dataset has been concatenated into continuous recordings for BIDS compliance, with annotations marking the onset and duration of each imagined speech trial. Channel names and montage information from the original ‘mnt’ (montage) structure have been preserved in the BIDS format.
Description of the event values if any
The events.tsv files contain annotations for each imagined speech trial. Each event has: - onset: Time in seconds from the beginning of the recording when the imagined speech trial begins - duration: Duration of the trial in seconds (calculated as the number of samples in the epoch divided by the sampling frequency) - value: The imagined speech command label, one of: “Hello”, “Help me”, “Stop”, “Thank you”, or “Yes” - trial_type: Corresponds to the value field
These annotations enable temporal segmentation of the continuous EEG data by imagined speech command type. The labels for the training and validation sets were extracted from the ‘y’ field in the original MATLAB structures (one-hot encoded vectors converted to class indices). For the test set, labels were obtained from the Track3_Answer Sheet_Test.xlsx file provided with the competition data.
Citation
When using this dataset, please cite: 1. The 2020 BCI Competition Track 3: https://osf.io/pq7vb/overview 2. Original competition organizers and data collectors (please refer to the competition website for complete citation information)
Data curators: Pierre Guetschel (BIDS conversion)
Competition co-chairs: Seong-Whan Lee, Klaus-Robert Müller, José del R. Millán
Automatic report
Report automatically generated by ``mne_bids.make_report()``.
The 2020 BCI competition, track 3 dataset was created by Seong-Whan Lee, Klaus-
Robert Müller, and José del R. Millán and conforms to BIDS version 1.7.0. This report was generated with MNE-BIDS (https://doi.org/10.21105/joss.01896). The dataset consists of 15 participants (sex were all unknown; handedness were all unknown; ages all unknown) . Data was recorded using an EEG system sampled at 256.0 Hz with line noise at n/a Hz. There were 45 scans in total. Recording durations ranged from 155.27 to 931.64 seconds (mean = 414.06, std = 365.98), for a total of 18632.64 seconds of data recorded over all scans. For each dataset, there were on average 64.0 (std = 0.0) recording channels per scan, out of which 64.0 (std = 0.0) were used in analysis (0.0 +/- 0.0 were removed from analysis).
Cohort#
Dataset Statistics#
Channel counts: 64 ch (n=45 recordings)
Sampling frequencies: 256.0 Hz (n=45 recordings)
Total recording duration: 5 h 10 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-imaginedSpeech · run-02
Showing one representative recording out of
15 subjects and 45 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 · 64 sensors — 64 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
2020 BCI competition, track 3 |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Seong-Whan Lee, Klaus-Robert Müller, José del R. Millán |
License |
CC-BY-4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000113,
title = {2020 BCI competition, track 3},
author = {Seong-Whan Lee and Klaus-Robert Müller and José del R. Millán},
doi = {10.82901/nemar.nm000113},
url = {https://doi.org/10.82901/nemar.nm000113},
}
API Reference#
eegdash.datasetEEGDashDatasetNM000113 · Lee2020eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.NM000113(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
2020 BCI competition, track 3
- Study:
nm000113(NeMAR)- Author (year):
Lee2020- Canonical:
—
Also importable as:
NM000113,Lee2020.Modality:
eeg. Subjects: 15; recordings: 45; 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
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/nm000113 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000113 DOI: https://doi.org/10.82901/nemar.nm000113
Examples
>>> from eegdash.dataset import NM000113 >>> dataset = NM000113(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 nm000113 to reproduce the tutorial on this dataset.
Citation
Seong-Whan Lee, Klaus-Robert Müller, José del R. Millán (20). 2020 BCI competition, track 3. 10.82901/nemar.nm000113
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000113.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset