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#

Oikonomou2016 – SSVEP MAMEM 3 dataset

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 11 Recordings: 110 License: ODC-By-1.0 Source: nemar

Metadata: Complete (100%)

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#

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)

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'])

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

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000121-blue)](https://doi.org/10.82901/nemar.nm000121) # SSVEP MAMEM 3 dataset SSVEP MAMEM 3 dataset. ## Dataset Overview - Code: MAMEM3 - Paradigm: ssvep - DOI: 10.48550/arXiv.1602.00904 - Subjects: 11 - Sessions per subject: 1 - Events: 6.66=33029, 7.50=33028, 8.57=33027, 10.00=33026, 12.00=33025 - Trial interval: [1, 4] s - Runs per session: 10 - File format: csv - Data preprocessed: True ## Acquisition - Sampling rate: 128.0 Hz - Number of channels: 14 - Channel types: eeg=14 - Channel names: AF3, AF4, F3, F4, F7, F8, FC5, FC6, O1, O2, P7, P8, T7, T8 - Montage: 10-20 - Hardware: EGI 300 Geodesic EEG System (GES 300) - Software: Microsoft Visual Studio 2010 with OpenGL - Reference: CAR - Sensor type: scalp electrodes - Line frequency: 50.0 Hz - Online filters: 5-48 Hz bandpass, 50 Hz notch - Impedance threshold: 80.0 kOhm - Cap manufacturer: EGI - Cap model: HydroCel Geodesic Sensor Net (HCGSN) - Electrode type: wet - Auxiliary channels: ecg, gsr, ppg ## Participants - Number of subjects: 11 - Health status: healthy - Age: min=24.0, max=39.0 - Gender distribution: male=8, female=3 - Handedness: {‘right’: 10, ‘left’: 1} - BCI experience: naive - Species: human ## Experimental Protocol - Paradigm: ssvep - Number of classes: 5 - Class labels: 6.66, 7.50, 8.57, 10.00, 12.00 - Trial duration: 5.0 s - Study design: Subjects focus attention on a violet box flickering at different frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz) presented at the center of the monitor. Each trial lasts 5 seconds followed by 5 seconds rest. - Feedback type: none - Stimulus type: visual - Stimulus modalities: visual - Primary modality: visual - Synchronicity: synchronous - Mode: offline - Training/test split: False - Instructions: Subjects were instructed to focus attention on the flickering stimulus and minimize artifacts by reducing eye blinks and movements. - Stimulus presentation: display=22 inch LCD monitor, 60 Hz refresh rate, 1680x1080 resolution, background=black, stimulus=violet box flickering at center of screen, graphics=Nvidia GeForce GTX 860M with vertical synchronization enabled ## HED Event Annotations Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser ```

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

License: ODC-By-1.0

Authors:

  • Vangelis P. Oikonomou

  • Georgios Liaros

  • Kostantinos Georgiadis

  • Elisavet Chatzilari

  • Katerina Adam

  • … and 2 more

Versions:

Version

DOI

Released

current

10.82901/nemar.nm000121
§ 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.

NEMAR pipeline statistics — NM000121
HED event descriptors word cloud HED event descriptors word cloud — NM000121
§ 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…
Full dataset metadata table

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
Copy-paste 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},
}
§ 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 FacePre-bundled mirror at EEGDash/nm000121 · pull with datasets.load_dataset("EEGDash/nm000121").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.

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

See Also#