DS002034: eeg dataset, 14 subjects#
Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task
Citation: Christoph Schneider, Michael Pereira, Luca Tonin, Jose del R. Millan (2019). Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task. 10.18112/openneuro.ds002034.v1.0.3
14-participant EEG dataset — Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS002034
dataset = DS002034(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS002034(cache_dir="./data", subject="01")
Advanced query
dataset = DS002034(
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{ds002034,
title = {Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task},
author = {Christoph Schneider and Michael Pereira and Luca Tonin and Jose del R. Millan},
doi = {10.18112/openneuro.ds002034.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds002034.v1.0.3},
}
About This Dataset#
This dataset contains the EEG recordings used in the paper: “Real-time EEG Feedback on Alpha Power Lateralization Leads to Behavioral Improvements in a Covert Attention Task” (Schneider, C., Pereira, M., Tonin, L. et al. Brain Topogr (2019). https://doi.org/10.1007/s10548-019-00725-9)
Participants:
Fourteen healthy subjects (seven female, seven male), age 23±1.52 years, with normal or corrected to normal vision took part in the study. All gave informed written consent and received course credits for their participation. The study was covered by the ethical protocol No PB_2017-00295 of the ethics commissions of the cantons of Vaud and Geneva, Switzerland and complied with the standards of the Declaration of Helsinki.
Experimental paradigm:
Each trial started with the presentation of a gray central fixation point at 0.5° visual angle and subjects were instructed to neither move nor blink until the trial was over. After 1 to 2 s (random duration), a cue—corresponding to the task to perform—was presented for 100 ms: half a circle (line width 0.1°, radius 2°) to the left or to the right indicated the side to attend to, a full circle around the fixation point indicated a central fixation trial (no covert attention shift). This was followed by the sustained attention phase—1 to 5 s—where subjects were instructed to covertly attend to the target placeholder indicated by the cue. Target placeholders were circles with an inscribed cross (line width 0.2°, radius 2°, centered at 12° extremity from the center point and at a downward angle of 30° from the horizontal midline; Fig. 1b). The target placeholder at the non-cued side is also called a distractor. To be consistent with the real-time feedback runs where color represented the decoded α-LI (see below), the color of both target placeholders varied randomly between isoluminant red and green (L*a*b color space (CIELAB), L and b constant, a varied between − 80 and 80). A trial ended when the inscribed cross disappeared in the to-attend target (valid cue) or on the opposite side (invalid cue). Subjects were instructed to react to the trial end as fast as possible with a button press using the right index finger. The inter-trial interval was 2–3 s long. In online runs, the min. and max. duration of the sustained attention period was between 2 and 20 s and inter-trial intervals ranged from 4-5 seconds.
Recordings:
The EEG was recorded with an active 64 channel HIamp EEG amplifier (g.tec, Schiedlberg, Austria) at 512Hz and referenced to the linked ears. The electrodes were positioned according to the international 10-10 system with the ground electrode on FCz. For more details please refer to the paper.
The study involved recordings (sessions) on three different days. One recording session lasted approximately 90 min, including the technical setup. Time on task was less than 40 min per session, with breaks after each run (every 9–10 min). On the first recording day subjects practiced for one run to familiarize with the task. Then they performed four offline runs (no feedback, 80 trials each) to calibrate their individual decoder for the real-time feedback (Fig. 1a, “Offline Paradigm”). On day two and three the α-power lateralization index (α-LI) feedback was administered in a single-blinded crossover design. Subjects were randomly assigned to either receive real or sham α-LI feedback on day two and then switched the other feedback group on day three (“Real-time feedback paradigm” and “Sham feedback”). Therefore, both days had the same run structure: they started and ended with one offline run (80 trials each, including catch trials), while the real-time feedback was given during two middle runs (40 long trials each).
Cohort#
Dataset Statistics#
Age distribution by gender (n=14, range 21–27 yr, mean 23.0 yr)
Sex composition
Channel counts: 81 ch (n=167 recordings)
Sampling frequencies: 512.0 Hz (n=167 recordings)
Total recording duration: 35 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-02 · task-offlinecatch · run-01
Showing one representative recording out of
14 subjects and 167 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 · 62 sensors — 62 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 |
Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Christoph Schneider, Michael Pereira, Luca Tonin, Jose del R. Millan |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds002034,
title = {Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task},
author = {Christoph Schneider and Michael Pereira and Luca Tonin and Jose del R. Millan},
doi = {10.18112/openneuro.ds002034.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds002034.v1.0.3},
}
API Reference#
eegdash.datasetEEGDashDatasetDS002034 · Schneider2019eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS002034(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task
- Study:
ds002034(OpenNeuro)- Author (year):
Schneider2019- Canonical:
—
Also importable as:
DS002034,Schneider2019.Modality:
eeg. Subjects: 14; recordings: 167; tasks: 4.- 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/ds002034 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002034 DOI: https://doi.org/10.18112/openneuro.ds002034.v1.0.3 NEMAR citation count: 7
Examples
>>> from eegdash.dataset import DS002034 >>> dataset = DS002034(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.pytorchdatasets.load_dataset("EEGDash/ds002034").huggingface
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 ds002034 to reproduce the tutorial on this dataset.
Citation
Christoph Schneider, Michael Pereira, Luca Tonin, Jose del R. Millan (2019). Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task. 10.18112/openneuro.ds002034.v1.0.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds002034.v1.0.3.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset