EEGdash›NeMAR›NM000121

Iss. 121 · 11 subjects · 110 recordings · ODC-By-1.0

Dataset Brief · Oikonomou2016 – SSVEP MAMEM 3 dataset

NM000121: eeg dataset, 11 subjects#

Name: Oikonomou2016 – SSVEP MAMEM 3 dataset
Published: 2016-01-01
License: ODC-By-1.0

Oikonomou2016 – SSVEP MAMEM 3 dataset

Citation: Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris (2016). Oikonomou2016 – SSVEP MAMEM 3 dataset. 10.82901/nemar.nm000121

11-participant EEG dataset — Oikonomou2016 – SSVEP MAMEM 3 dataset.

Data & curation Vangelis P. Oikonomou · Georgios Liaros · Kostantinos Georgiadis · Elisavet Chatzilari · Katerina Adam · Spiros Nikolopoulos · …
Year 2016 · Distributed via NeMAR

EEG · 14 ch128 HzBIDS 1.9.0Task · ssvepHealthyVisualPerception

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 NM000121

dataset = NM000121(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 = NM000121(cache_dir="./data", subject="01")

Advanced query

dataset = NM000121(
    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{nm000121,
  title = {Oikonomou2016 – SSVEP MAMEM 3 dataset},
  author = {Vangelis P. Oikonomou and Georgios Liaros and Kostantinos Georgiadis and Elisavet Chatzilari and Katerina Adam and Spiros Nikolopoulos and Ioannis Kompatsiaris},
  doi = {10.82901/nemar.nm000121},
  url = {https://doi.org/10.82901/nemar.nm000121},
}

§ 02Study · The README

About This Dataset#

SSVEP MAMEM 3 dataset.

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

DOI

SSVEP MAMEM 3 dataset

6.66

View full README

DOI

SSVEP MAMEM 3 dataset

6.66

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/6_66

7.50

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/7_50

8.57

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/8_57

10.00

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/10_00

12.00

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/12_00

Paradigm-Specific Parameters

Detected paradigm: ssvep
Stimulus frequencies: [6.66, 7.5, 8.57, 10.0, 12.0] Hz
Number of targets: 5

Data Structure

Trials: 1104
Trials context: Total of 1104 trials (5 seconds each) across all subjects and sessions. Subject S001: 3 sessions, S003 and S004: 4 sessions each, all others: 5 sessions. Each session includes 23 trials (8 adaptation + 15 experimental).

Preprocessing

Preprocessing applied: True
Steps: bandpass filtering (5-48 Hz), notch filtering (50 Hz), artifact removal (AMUSE, ICA), Common Average Reference (CAR)
Highpass filter: 5.0 Hz
Lowpass filter: 48.0 Hz
Bandpass filter: {‘low_cutoff_hz’: 5.0, ‘high_cutoff_hz’: 48.0}
Notch filter: 50.0 Hz
Filter type: IIR (Chebyshev, Elliptic)
Artifact methods: AMUSE, ICA, FastICA
Re-reference: CAR

Signal Processing

Classifiers: LDA, SVM, Random Forest, kNN, Naive Bayes, CCA, ELM, Decision Trees
Feature extraction: Periodogram, Welch, Goertzel, Yule-AR, STFT, Discrete Wavelet Transform, PSD, CSP, ICA
Frequency bands: analyzed=[5.0, 48.0] Hz
Spatial filters: CAR, CSP, Minimum Energy

Cross-Validation

Method: leave-one-subject-out
Evaluation type: cross_subject

Performance (Original Study)

Accuracy: 72.47%
Default Config Accuracy: 72.47
Optimal Config Accuracy: 79.47
Best Electrode Accuracy: 74.42
Execution Time Ms: 5.0

BCI Application

Applications: research, comparative_study
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Visual
Type: Perception

Documentation

Description: Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. Dataset includes 256-channel EEG signals from 11 subjects performing SSVEP tasks with 5 different flickering frequencies.
DOI: 10.6084/m9.figshare.2068677.v1
Associated paper DOI: arXiv:1602.00904v2
License: ODC-By-1.0
Investigators: Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris
Senior author: Ioannis Kompatsiaris
Institution: Centre for Research and Technology Hellas (CERTH)
Country: Greece
Repository: Figshare
Data URL: https://dx.doi.org/10.6084/m9.figshare.2068677.v1
Publication year: 2016
Ethics approval: Ethics committee of the Centre for Research and Technology Hellas, approved 3/7/2015
Keywords: SSVEP, BCI, brain-computer interface, EEG, visual evoked potentials, comparative evaluation, signal processing

Abstract

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. This report focuses on EEG-based BCIs that rely on Steady-State-Visual-Evoked Potentials (SSVEPs) and performs a comparative evaluation of state-of-the-art algorithms for filtering, artifact removal, feature extraction, feature selection and classification. The dataset consists of 256-channel EEG signals from 11 subjects, along with a processing toolbox for reproducing results.

Methodology

Comparative evaluation of SSVEP-based BCI algorithms using leave-one-subject-out cross-validation. The study examines filtering methods (IIR, FIR), artifact removal (AMUSE, ICA), feature extraction (Periodogram, Welch, Goertzel, Yule-AR, STFT, DWT), feature selection (Shannon entropy, PCA, ICA), and classification (LDA, SVM, kNN, Naive Bayes, Random Forest, CCA, ELM, Decision Trees). Each parameter is studied independently while keeping others fixed to identify optimal configurations.

References

Oikonomou, V. P., Liaros, G., Georgiadis, K., Chatzilari, E., Adam, K., Nikolopoulos, S., & Kompatsiaris, I. (2016). Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. arXiv preprint arXiv:1602.00904. MAMEM Steady State Visually Evoked Potential EEG Database https://archive.physionet.org/physiobank/database/mssvepdb/ S. Nikolopoulos, 2016, DataAcquisitionDetails.pdf https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_III_14_channels_11_subjects_5_frequencies_presented_simultaneously_/3413851 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, 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 Generated by MOABB 1.4.3 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=11, range 24–39 yr, mean 30.4 yr)

20253035

Other · 11

Channel counts: 14 ch (n=110 recordings)

Sampling frequencies: 128.0 Hz (n=110 recordings)

Total recording duration: 4 h 35 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 14 ch · EEG · 128 Hz · 11 subjects, 110 recordings

Live trace viewer — sub-6 · ses-0 · task-ssvep · run-6

Showing one representative recording out of 11 subjects and 110 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 · 14 sensors — 14 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.NM000121(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Oikonomou2016_MAMEM3

Canonical—

Importable asNM000121 · Oikonomou2016_MAMEM3

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

class eegdash.dataset.NM000121(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Oikonomou2016 – SSVEP MAMEM 3 dataset

Study:: nm000121 (NeMAR)
Author (year):: Oikonomou2016_MAMEM3
Canonical:: —

Also importable as: NM000121, Oikonomou2016_MAMEM3.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 11; recordings: 110; 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/nm000121 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000121 DOI: https://doi.org/10.82901/nemar.nm000121

Examples

>>> from eegdash.dataset import NM000121
>>> dataset = NM000121(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000121.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 nm000121 to reproduce the tutorial on this dataset.

Citation

Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, … (2016). Oikonomou2016 – SSVEP MAMEM 3 dataset. 10.82901/nemar.nm000121

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000121.

Related & sibling datasets

NM000176EEG · 5 subj NM000123EEG · 12 subj NM000118EEG · 9 subj NM000125EEG · 23 subj NM000122EEG · 12 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

ODC-By-1.0 · 10.82901/nemar.nm000121

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000121`
Title	Oikonomou2016 – SSVEP MAMEM 3 dataset
Author (year)	`Oikonomou2016_MAMEM3`
Canonical	—
Importable as	`NM000121`, `Oikonomou2016_MAMEM3`
Year	2016
Authors	Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris
License	ODC-By-1.0
Citation / DOI	10.82901/nemar.nm000121
Source links	OpenNeuro \| NeMAR \| Source URL

NM000121: eeg dataset, 11 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#