DS002034#

Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 14 Recordings: 882 License: CC0 Source: openneuro Citations: 7.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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).

Dataset Information#

Dataset ID	`DS002034`
Title	Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task
Year	2019
Authors	Christoph Schneider, Michael Pereira, Luca Tonin, Jose del R. Millan
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds002034.v1.0.3
Source links	OpenNeuro \| NeMAR \| Source URL

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: 14
Recordings: 882
Tasks: 4

Channels & sampling rate

Channels: 81 (167), 64 (167)
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS002034 class to access this dataset programmatically.

class eegdash.dataset.DS002034(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002034. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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/ds002034 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002034

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, 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