EEGdash›OpenNeuro›DS006979

Iss. 6979 · 53 subjects · 56 recordings · CC0

Dataset Brief · Examining Perceptual Grouping on Stages of Processing in Visu…

DS006979: eeg dataset, 53 subjects#

Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study

Citation: Hanane Ramzaoui, Melissa Beck (20). Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study. 10.18112/openneuro.ds006979.v1.0.1

53-participant EEG dataset — Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study.

Data & curation Hanane Ramzaoui · Melissa Beck
Year 20 · Distributed via OpenNeuro
Funding n/a

EEG · 69 (53), 72 ch512 Hz · mixedBIDS 1.7.03 tasksHealthyVisualMemory

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 DS006979

dataset = DS006979(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 = DS006979(cache_dir="./data", subject="01")

Advanced query

dataset = DS006979(
    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{ds006979,
  title = {Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study},
  author = {Hanane Ramzaoui and Melissa Beck},
  doi = {10.18112/openneuro.ds006979.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006979.v1.0.1},
}

§ 02Study · The README

About This Dataset#

We present an electrophysiological dataset recorded from fifty-three subjects performing a bilateral change-detection task to investigate how perceptual grouping, based on color repetition, influences Visual Working Memory (VWM) processing efficiency.

The study is designed to temporally isolate and measure the neural correlates of several critical VWM stages: **individuation encoding**, **maintenance**, **initial comparison**, **percept-memory comparison**, and **decision making/late comparison**. This is achieved using specific **Event-Related Potential (ERP) markers** (N2pc, CDA, N2, FN400).

BIDS-EEG Dataset: Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study

Authors: Hanane Ramzaoui, Melissa Beck

1. Description: Project Overview

2. Experimental Task and Conditions

Subjects were cued to encode the colors of 2 or 3 squares in one visual hemifield. After a maintenance period, a single-item probe was presented to determine if its color had changed.

View full README

BIDS-EEG Dataset: Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study

Authors: Hanane Ramzaoui, Melissa Beck

1. Description: Project Overview

2. Experimental Task and Conditions

Subjects were cued to encode the colors of 2 or 3 squares in one visual hemifield. After a maintenance period, a single-item probe was presented to determine if its color had changed.

Key Manipulations

The memory array contained four primary conditions: *\*Unrepeated (UR):* Arrays with 2 or 3 unique colors (2-UR, 3-UR). *\*Repeated (R):* Arrays with 3 items, where two colors were repeated. This condition was further subdivided based on spatial arrangement: * Two repeated colors with strong spatial proximity **(3-RSP)**. * Two repeated colors with weak spatial proximity **(3-RWP)**.

The Probe in Repeated Conditions In the repeated conditions (3-RSP and 3-RWP), the single-item probe could test two different item types for change detection: *\*Repeated Item:* The probe tests one of the two squares that share the same color. *\*Unrepeated Item (Singleton):* The probe tests the single square with the unique color.

3. Primary Neurophysiological Measurements

The study leverages the following ERP components to index different VWM processing stages:

| VWM Stage | ERP Marker | Event Locking |
| :--- | :--- | :--- |
| **Individuation Encoding** | **N2pc** | Stimulus-Locked |
| **Maintenance/Load** | **CDA** (Contralateral Delay Activity) | Stimulus-Locked |
| **Initial Comparison** | **N2pc** | Probe-Locked |
| **Percept-Memory Comparison** | **N2** | Probe-Locked |
| **Decision Making/Late Comparison** | **FN400** | Probe-Locked |

4. Acquisition Details and Structure

Acquisition Parameters

| Parameter | Detail |
| :--- | :--- |
| **Subjects (N)** | 53 (N=39 used for stimulus-locked ERPs, see `participants.tsv` for details) |
| **Electrode System** | BioSemi ActiveTwo System |
| **Number of Channels** | 71 (64 scalp, 3 EOG, 2 Mastoid, 1 CMS/DRL) |
| **Sampling Rate (Acquisition)** | 512 Hz |
| **Total Trials** | 1248 trials |

BIDS Compliance

The data is structured following the Brain Imaging Data Structure (BIDS) standard for EEG. *\*Acquisition Parameters:* Detailed recording specifications (e.g., 512 Hz sampling rate, Sinc filter details) are provided in the task-level BIDS JSON files (task-myexperiment_eeg.json). *\*Methodology:* Comprehensive details on offline preprocessing (e.g., re-referencing to average mastoids, ICA artifact removal, 0.1 Hz high-pass filtering) and the precise analysis plan (e.g., ERP measurement windows, HEOG artifact thresholds, channel clusters) are provided in the stage 1 protocol on OSF (https://doi.org/10.17605/OSF.IO/8ZS96).

5. Event Codes/Triggers

The following table maps the trigger codes recorded in the EEG data to the specific experimental events. *\*Acronym Key:* UR = Unrepeated; RWP = Repeated Weak Proximity; RSP = Repeated Strong Proximity.

| Trigger Code | Event Description |
| :---: | :--- |
| "11" | Stimulus: 2-UR \| Left Cue \| Change \| Unrepeated Probe |
| "12" | Stimulus: 3-UR \| Left Cue \| Change \| Unrepeated Probe |
| "13" | Stimulus: 3-RWP \| Left Cue \| Change \| Unrepeated Probe |
| "14" | Stimulus: 3-RSP \| Left Cue \| Change \| Unrepeated Probe |
| "17" | Stimulus: 3-RWP \| Left Cue \| Change \| Repeated Probe |
| "18" | Stimulus: 3-RSP \| Left Cue \| Change \| Repeated Probe |
| "19" | Stimulus: 2-UR \| Left Cue \| No-Change \| Unrepeated Probe |
| "20" | Stimulus: 3-UR \| Left Cue \| No-Change \| Unrepeated Probe |
| "21" | Stimulus: 3-RWP \| Left Cue \| No-Change \| Unrepeated Probe |
| "22" | Stimulus: 3-RSP \| Left Cue \| No-Change \| Unrepeated Probe |
| "25" | Stimulus: 3-RWP \| Left Cue \| No-Change \| Repeated Probe |
| "26" | Stimulus: 3-RSP \| Left Cue \| No-Change \| Repeated Probe |
| "27" | Stimulus: 2-UR \| Right Cue \| Change \| Unrepeated Probe |
| "28" | Stimulus: 3-UR \| Right Cue \| Change \| Unrepeated Probe |
| "29" | Stimulus: 3-RWP \| Right Cue \| Change \| Unrepeated Probe |
| "30" | Stimulus: 3-RSP \| Right Cue \| Change \| Unrepeated Probe |
| "33" | Stimulus: 3-RWP \| Right Cue \| Change \| Repeated Probe |
| "34" | Stimulus: 3-RSP \| Right Cue \| Change \| Repeated Probe |
| "35" | Stimulus: 2-UR \| Right Cue \| No-Change \| Unrepeated Probe |
| "36" | Stimulus: 3-UR \| Right Cue \| No-Change \| Unrepeated Probe |
| "37" | Stimulus: 3-RWP \| Right Cue \| No-Change \| Unrepeated Probe |
| "38" | Stimulus: 3-RSP \| Right Cue \| No-Change \| Unrepeated Probe |
| "41" | Stimulus: 3-RWP \| Right Cue \| No-Change \| Repeated Probe |
| "42" | Stimulus: 3-RSP \| Right Cue \| No-Change \| Repeated Probe |
| "51" | Probe Onset event: 2-UR \| Left Cue |
| "52" | Probe Onset event: 3-UR \| Left Cue |
| "53" | Probe Onset event: 3-RWP \| Left Cue |
| "54" | Probe Onset event: 3-RSP \| Left Cue |
| "55" | Probe Onset event: 2-UR \| Right Cue |
| "56" | Probe Onset event: 3-UR \| Right Cue |
| "57" | Probe Onset event: 3-RWP \| Right Cue |
| "58" | Probe Onset event: 3-RSP \| Right Cue |
| "120" | Manual Response: Correct. |
| "121" | Manual Response: Incorrect. |

6. Protocol Registration and Reference

For this dataset project, the approved Stage 1 protocol (registered report) can be found at this OSF link (2024, October 15): https://doi.org/10.17605/OSF.IO/8ZS96

7. Contact and Ethics

*\*Affiliation:* Louisiana State University *\*Ethical Approval:* Institutional Review Board of Louisiana State University (IRBAM-23-0273 from March 1, 2023) *\*Contact:* hramzaoui@lsu.edu

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=53, range 18–33 yr)

15202530

Female · 31Male · 21Other · 1

Channel counts (ch)

6972

Sampling frequencies (Hz)

500512

Total recording duration: 81 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 69 (53), 72 ch · EEG · 512 Hz · mixed · 53 subjects, 56 recordings

Live trace viewer — sub-13 · task-visualworkingmemorytask

Showing one representative recording out of 53 subjects and 56 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.DS006979(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Ramzaoui2025

Canonical—

Importable asDS006979 · Ramzaoui2025

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

class eegdash.dataset.DS006979(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study

Study:: ds006979 (OpenNeuro)
Author (year):: Ramzaoui2025
Canonical:: —

Also importable as: DS006979, Ramzaoui2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 53; recordings: 56; tasks: 3.

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/ds006979 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006979 DOI: https://doi.org/10.18112/openneuro.ds006979.v1.0.1

Examples

>>> from eegdash.dataset import DS006979
>>> dataset = DS006979(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/ds006979 · pull with datasets.load_dataset("EEGDash/ds006979").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006979.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 ds006979 to reproduce the tutorial on this dataset.

Citation

Hanane Ramzaoui, Melissa Beck (20). Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study. 10.18112/openneuro.ds006979.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006979.v1.0.1.

Related & sibling datasets

DS005697EEG · 51 subj DS006367EEG · 52 subj DS006370EEG · 56 subj DS007431EEG · 47 subj DS004520EEG · 33 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006979.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006979`
Title	Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study
Author (year)	`Ramzaoui2025`
Canonical	—
Importable as	`DS006979`, `Ramzaoui2025`
Year	20
Authors	Hanane Ramzaoui, Melissa Beck
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006979.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS006979: eeg dataset, 53 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#