EEGdash›NeMAR›NM000120

Iss. 120 · 11 subjects · 55 recordings · ODC-By-1.0

Dataset Brief · Oikonomou2016 – SSVEP MAMEM 2 dataset

NM000120: eeg dataset, 11 subjects#

Name: Oikonomou2016 – SSVEP MAMEM 2 dataset
Published: 2016-01-01
License: ODC-By-1.0

Oikonomou2016 – SSVEP MAMEM 2 dataset

Citation: Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris (2016). Oikonomou2016 – SSVEP MAMEM 2 dataset. 10.82901/nemar.nm000120

11-participant EEG dataset — Oikonomou2016 – SSVEP MAMEM 2 dataset.

Data & curation Vangelis P. Oikonomou · Georgios Liaros · Kostantinos Georgiadis · Elisavet Chatzilari · Katerina Adam · Spiros Nikolopoulos · …
Year 2016 · Distributed via NeMAR
Funding H2020-ICT-2014-644780

EEG · 256 ch250 HzBIDS 1.9.0Task · ssvepHealthyVisualAttention

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 NM000120

dataset = NM000120(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 = NM000120(cache_dir="./data", subject="01")

Advanced query

dataset = NM000120(
    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{nm000120,
  title = {Oikonomou2016 – SSVEP MAMEM 2 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.nm000120},
  url = {https://doi.org/10.82901/nemar.nm000120},
}

§ 02Study · The README

About This Dataset#

SSVEP MAMEM 2 dataset.

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

DOI

SSVEP MAMEM 2 dataset

6.66

View full README

DOI

SSVEP MAMEM 2 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
Number of repetitions: 3

Data Structure

Trials: 1104
Trials context: Each session includes 23 trials (8 adaptation trials excluded from analysis). 5 sessions per subject (with exceptions: S001=3 sessions, S003=4 sessions, S004=4 sessions). Total: 1104 trials of 5 seconds each.

Preprocessing

Data state: raw
Preprocessing applied: False

Signal Processing

Classifiers: LDA, SVM, Random Forest, kNN, Naive Bayes, AdaBoost, Decision Trees, CCA
Feature extraction: PWelch, Periodogram, FFT, Goertzel, PYULEAR (Yule-AR), STFT, DWT, PSD, Wavelet, Spectrogram
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: 74.42%
Mean Accuracy Default Config: 72.47
Mean Accuracy Optimal Config: 74.42
Processing Time Msec: 68

BCI Application

Applications: command_selection
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Visual
Type: Research

Documentation

DOI: 10.48550/arXiv.1602.00904
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
Institution: Centre for Research and Technology Hellas (CERTH)
Country: GR
Repository: GitHub
Data URL: https://figshare.com/articles/dataset/3153409
Publication year: 2016
Funding: H2020-ICT-2014-644780
Ethics approval: Approved by ethics committee of Centre for Research and Technology Hellas, date 3/7/2015, grant H2020-ICT-2014-644780
Keywords: SSVEP, BCI, brain-computer interface, EEG, visual evoked potentials, signal processing, feature extraction, classification

Abstract

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. This study focuses on SSVEP-based BCIs and performs a comparative evaluation of state-of-the-art algorithms for filtering, artifact removal, feature extraction, feature selection and classification. Dataset consists of 256-channel EEG signals from 11 subjects with 5 flickering frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz).

Methodology

Leave-one-subject-out cross-validation was used to evaluate a general-purpose BCI system without subject-specific training. Systematic comparison of algorithms across all signal processing stages: (1) Signal filtering: FIR vs IIR filters; (2) Artifact removal: AMUSE vs FastICA; (3) Feature extraction: PWelch, Periodogram, PYULEAR, DWT, STFT, Goertzel; (4) Feature selection: entropy-based methods and PCA/SVD; (5) Classification: SVM, LDA, KNN, Naive Bayes, Random Forest, AdaBoost. Optimal configuration achieved 74.42% mean accuracy using IIR-Elliptic filter, AMUSE artifact removal, PWelch feature extraction with nfft=512, segment length=350, overlap=0.75, and channel-138.

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_II_256_channels_11_subjects_5_frequencies_presented_simultaneously_/3153409?file=4911931 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#

Channel counts: 256 ch (n=55 recordings)

Sampling frequencies: 250.0 Hz (n=55 recordings)

Total recording duration: 5 h 6 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 256 ch · EEG · 250 Hz · 11 subjects, 55 recordings

Live trace viewer — sub-6 · ses-0 · task-ssvep · run-4

Showing one representative recording out of 11 subjects and 55 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 · 256 sensors — 256 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.NM000120(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Oikonomou2016_MAMEM2

Canonical—

Importable asNM000120 · Oikonomou2016_MAMEM2

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

class eegdash.dataset.NM000120(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Oikonomou2016 – SSVEP MAMEM 2 dataset

Study:: nm000120 (NeMAR)
Author (year):: Oikonomou2016_MAMEM2
Canonical:: —

Also importable as: NM000120, Oikonomou2016_MAMEM2.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 11; recordings: 55; 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/nm000120 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000120 DOI: https://doi.org/10.82901/nemar.nm000120

Examples

>>> from eegdash.dataset import NM000120
>>> dataset = NM000120(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 — NM000120.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 nm000120 to reproduce the tutorial on this dataset.

Citation

Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, … (2016). Oikonomou2016 – SSVEP MAMEM 2 dataset. 10.82901/nemar.nm000120

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000120.

Related & sibling datasets

NM000198EEG · 13 subj NM000259EEG · 10 subj NM000249EEG · 13 subj NM000194EEG · 12 subj NM000188EEG · 10 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.nm000120

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000120`
Title	Oikonomou2016 – SSVEP MAMEM 2 dataset
Author (year)	`Oikonomou2016_MAMEM2`
Canonical	—
Importable as	`NM000120`, `Oikonomou2016_MAMEM2`
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.nm000120
Source links	OpenNeuro \| NeMAR \| Source URL

NM000120: 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#