DS003004#

Imagined Emotion Study

Access recordings and metadata through EEGDash.

Citation: Julie Onton, Scott Makeig (2020). Imagined Emotion Study. 10.18112/openneuro.ds003004.v1.1.1

Modality: eeg Subjects: 34 Recordings: 277 License: CC0 Source: openneuro Citations: 7.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003004

dataset = DS003004(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 = DS003004(cache_dir="./data", subject="01")

Advanced query

dataset = DS003004(
    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{ds003004,
  title = {Imagined Emotion Study},
  author = {Julie Onton and Scott Makeig},
  doi = {10.18112/openneuro.ds003004.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds003004.v1.1.1},
}

About This Dataset#

PARADIGM: The study uses the method of guided imagery to induce resting, eyes-closed participants using voice-guided imagination to enter distinct 15 emotion states during acquisition of high-density EEG data.

During the study, participants listen to 15 voice recordings that each suggest imagining a scenario in which they have experienced – or would experience the named target emotion. Some target emotions have positive valence (e.g., joy, happiness), others negative valence (e.g., sadness, anger). Before and between the 15 emotion imagination periods, participants hear relaxation suggestions (‘Now return to a neutral state by …’).

PROCEDURE: When the participant first begins to feel the target emotion, they are asked to indicate this by pressing a handheld button. Participants are asked to continue feeling the emotion as long as possible. To intensify and lengthen the periods of experienced emotion, participants are asked to interoceptively perceive and attend relevant somatosensory sensations. When the target feeling wanes (typically after 1 and 5 minutes), participants push the button again to leave the emotion imagination period and cue the relaxation instructions.

DATA HANDLING: The raw data have been preprocessed to fix confusing event codes and to remove excessively noisy channels. In addition, a 1-Hz high pass filter was applied to ready the data for ICA decomposition. Note: Unfortunately, the unfiltered data are no longer available.

NOTE: Sub22 was a repeat subject, hence was removed from the dataset.

Dataset Information#

Dataset ID	`DS003004`
Title	Imagined Emotion Study
Year	2020
Authors	Julie Onton, Scott Makeig
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003004.v1.1.1
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: 34
Recordings: 277
Tasks: 1

Channels & sampling rate

Channels: 219 (6), 224 (6), 214 (4), 221 (4), 212 (4), 222 (2), 232 (2), 220 (2), 229 (2), 189 (2), 208 (2), 211 (2), 201 (2), 235 (2), 215 (2), 218 (2), 209 (2), 134 (2), 227 (2), 196 (2), 223 (2), 180 (2), 213 (2), 206 (2), 207 (2), 226 (2), 231 (2)
Sampling rate (Hz): 256.0
Duration (hours): 0.0

API Reference#

Use the DS003004 class to access this dataset programmatically.

class eegdash.dataset.DS003004(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003004. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 34; recordings: 34; 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/ds003004 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003004

Examples

>>> from eegdash.dataset import DS003004
>>> dataset = DS003004(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