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.

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

Metadata: Complete (90%)

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},
}

About This Dataset#

SSVEP MAMEM 3 dataset

SSVEP MAMEM 3 dataset.

Dataset Overview

  • Code: MAMEM3

  • Paradigm: ssvep

  • DOI: 10.48550/arXiv.1602.00904

View full README

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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000121

Title

Oikonomou2016 – SSVEP MAMEM 3 dataset

Author (year)

Oikonomou2016_MAMEM3

Canonical

MAMEM3, SSVEP_MAMEM3

Importable as

NM000121, Oikonomou2016_MAMEM3, MAMEM3, SSVEP_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

Unknown

Source links

OpenNeuro | NeMAR | Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 11

  • Recordings: 110

  • Tasks: 1

Channels & sampling rate

  • Channels: 14

  • Sampling rate (Hz): 128.0

  • Duration (hours): 4.597261284722222

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Files & format

  • Size on disk: 120.2 MB

  • File count: 110

  • Format: BIDS

License & citation

  • License: ODC-By-1.0

  • DOI: —

Provenance

API Reference#

Use the NM000121 class to access this dataset programmatically.

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

Bases: EEGDashDataset

Oikonomou2016 – SSVEP MAMEM 3 dataset

Study:

nm000121 (NeMAR)

Author (year):

Oikonomou2016_MAMEM3

Canonical:

MAMEM3, SSVEP_MAMEM3

Also importable as: NM000121, Oikonomou2016_MAMEM3, MAMEM3, SSVEP_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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#