EEGdash›OpenNeuro›DS006142

Iss. 6142 · 27 subjects · 27 recordings · CC0

Dataset Brief · Essex EEG Movie Memory dataset

DS006142: eeg dataset, 27 subjects#

Essex EEG Movie Memory dataset

Citation: Ana Matran-Fernandez, Sebastian Halder (2019). Essex EEG Movie Memory dataset. 10.18112/openneuro.ds006142.v1.0.2

27-participant EEG dataset — Essex EEG Movie Memory dataset.

Data & curation Ana Matran-Fernandez · Sebastian Halder
Year 2019 · Distributed via OpenNeuro

EEG · 65 ch2048 HzBIDS 1.7.0Task · MovieMemoryHealthyVisualMemory

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 DS006142

dataset = DS006142(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 = DS006142(cache_dir="./data", subject="01")

Advanced query

dataset = DS006142(
    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{ds006142,
  title = {Essex EEG Movie Memory dataset},
  author = {Ana Matran-Fernandez and Sebastian Halder},
  doi = {10.18112/openneuro.ds006142.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds006142.v1.0.2},
}

§ 02Study · The README

About This Dataset#

Authors: Ana Matran-Fernandez and Sebastian Halder

This dataset contains raw electroencephalography (EEG) signals recorded from 27 participants while watching 10-second long clips

extracted from movies that they had previously watched. For each clip, participants were asked whether they recognised the movie it belonged to, and if so, whether they remembered having watched it previously or not.

Essex EEG Movie Memory Dataset

If a participant reported recognising or remembering a clip, it was shown a second time to capture (via a mouse click) time annotations of the instants that prompted this recognition.

EEG

EEG data were acquired with a BioSemi ActiveTwo system with 64 electrodes positioned according to the international 10-20 system.

The sampling rate was 2048 Hz.

View full README

Essex EEG Movie Memory Dataset

If a participant reported recognising or remembering a clip, it was shown a second time to capture (via a mouse click) time annotations of the instants that prompted this recognition.

EEG

EEG data were acquired with a BioSemi ActiveTwo system with 64 electrodes positioned according to the international 10-20 system.

The sampling rate was 2048 Hz.

Stimuli

The clips used in the study were originally annotated in terms of their memorability by Cohendet et al (see References).

This dataset can be requested from the authors.

Example code

We have prepared an example script to demonstrate how to load the EEG data into Python using MNE and MNE-BIDS packages.

This script is located in the ‘code’ directory.

References

Romain Cohendet, Karthik Yadati, Ngoc Q. K. Duong, and Claire-Hélène Demarty. 2018. Annotating, Understanding, and Predicting Long-term Video Memorability. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval (ICMR ‘18). Association for Computing Machinery, New York, NY, USA, 178–186. https://doi.org/10.1145/3206025.3206056

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=27, range 21–47 yr, mean 27.9 yr)

20253045

Female · 8Male · 19

Sex composition

Female
8
Male
19
F : M ratio
0.42 : 1

30% female · n = 27 subjects with reported sex.

HandednessRight · 25Left · 2

Channel counts: 65 ch (n=27 recordings)

Sampling frequencies: 2048.0 Hz (n=27 recordings)

Total recording duration: 27 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 2048 Hz · 27 subjects, 27 recordings

Live trace viewer — sub-021 · task-MovieMemory

Showing one representative recording out of 27 subjects and 27 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

§ 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.DS006142(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)MatranFernandez2025

Canonical—

Importable asDS006142 · MatranFernandez2025

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

class eegdash.dataset.DS006142(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Essex EEG Movie Memory dataset

Study:: ds006142 (OpenNeuro)
Author (year):: MatranFernandez2025
Canonical:: —

Also importable as: DS006142, MatranFernandez2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 27; recordings: 27; 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/ds006142 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006142 DOI: https://doi.org/10.18112/openneuro.ds006142.v1.0.2

Examples

>>> from eegdash.dataset import DS006142
>>> dataset = DS006142(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 FacePre-bundled mirror at EEGDash/ds006142 · pull with datasets.load_dataset("EEGDash/ds006142").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006142.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 ds006142 to reproduce the tutorial on this dataset.

Citation

Ana Matran-Fernandez, Sebastian Halder (2019). Essex EEG Movie Memory dataset. 10.18112/openneuro.ds006142.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006142.v1.0.2.

Related & sibling datasets

DS006848EEG · 30 subj DS006963EEG · 32 subj DS004706EEG · 34 subj DS007081EEG · 41 subj DS005672EEG · 3 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006142.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006142`
Title	Essex EEG Movie Memory dataset
Author (year)	`MatranFernandez2025`
Canonical	—
Importable as	`DS006142`, `MatranFernandez2025`
Year	2019
Authors	Ana Matran-Fernandez, Sebastian Halder
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006142.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS006142: eeg dataset, 27 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#