NM000119: eeg dataset, 11 subjects#

Name: Oikonomou2016 – SSVEP MAMEM 1 dataset
Published: 2016
License: ODC-By-1.0

Oikonomou2016 – SSVEP MAMEM 1 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 1 dataset.

Modality: eeg Subjects: 11 Recordings: 47 License: ODC-By-1.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000119

dataset = NM000119(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 = NM000119(cache_dir="./data", subject="01")

Advanced query

dataset = NM000119(
    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{nm000119,
  title = {Oikonomou2016 – SSVEP MAMEM 1 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 1 dataset

SSVEP MAMEM 1 dataset.

Dataset Overview

Code: MAMEM1
Paradigm: ssvep
DOI: 10.48550/arXiv.1602.00904

View full README

SSVEP MAMEM 1 dataset

SSVEP MAMEM 1 dataset.

Dataset Overview

Code: MAMEM1
Paradigm: ssvep
DOI: 10.48550/arXiv.1602.00904
Subjects: 11
Sessions per subject: 1
Events: 6.66=1, 7.50=2, 8.57=3, 10.00=4, 12.00=5
Trial interval: [1, 4] s
File format: MATLAB .mat

Acquisition

Sampling rate: 250.0 Hz
Number of channels: 256
Channel types: eeg=256
Channel names: E1, E10, E100, E101, E102, E103, E104, E105, E106, E107, E108, E109, E11, E110, E111, E112, E113, E114, E115, E116, E117, E118, E119, E12, E120, E121, E122, E123, E124, E125, E126, E127, E128, E129, E13, E130, E131, E132, E133, E134, E135, E136, E137, E138, E139, E14, E140, E141, E142, E143, E144, E145, E146, E147, E148, E149, E15, E150, E151, E152, E153, E154, E155, E156, E157, E158, E159, E16, E160, E161, E162, E163, E164, E165, E166, E167, E168, E169, E17, E170, E171, E172, E173, E174, E175, E176, E177, E178, E179, E18, E180, E181, E182, E183, E184, E185, E186, E187, E188, E189, E19, E190, E191, E192, E193, E194, E195, E196, E197, E198, E199, E2, E20, E200, E201, E202, E203, E204, E205, E206, E207, E208, E209, E21, E210, E211, E212, E213, E214, E215, E216, E217, E218, E219, E22, E220, E221, E222, E223, E224, E225, E226, E227, E228, E229, E23, E230, E231, E232, E233, E234, E235, E236, E237, E238, E239, E24, E240, E241, E242, E243, E244, E245, E246, E247, E248, E249, E25, E250, E251, E252, E253, E254, E255, E256, E26, E27, E28, E29, E3, E30, E31, E32, E33, E34, E35, E36, E37, E38, E39, E4, E40, E41, E42, E43, E44, E45, E46, E47, E48, E49, E5, E50, E51, E52, E53, E54, E55, E56, E57, E58, E59, E6, E60, E61, E62, E63, E64, E65, E66, E67, E68, E69, E7, E70, E71, E72, E73, E74, E75, E76, E77, E78, E79, E8, E80, E81, E82, E83, E84, E85, E86, E87, E88, E89, E9, E90, E91, E92, E93, E94, E95, E96, E97, E98, E99
Montage: GSN-HydroCel-256
Hardware: EGI 300 Geodesic EEG System (GES 300)
Line frequency: 50.0 Hz
Impedance threshold: 80.0 kOhm
Cap manufacturer: EGI
Cap model: HydroCel Geodesic Sensor Net (HCGSN)

Participants

Number of subjects: 11
Health status: healthy
Clinical population: able-bodied subjects without any known neuro-muscular or mental disorders
Age: min=24, max=39
Gender distribution: male=8, female=3
Handedness: {‘right’: 10, ‘left’: 1}
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 single violet box flickering at different frequencies (6.66, 7.50, 8.57, 10.00, 12.00 Hz) presented sequentially. Each frequency is presented for 5 seconds (trial) followed by 5 seconds rest, repeated 3 times per frequency, with 30 seconds rest between different frequencies.
Feedback type: none
Stimulus type: flickering box
Stimulus modalities: visual
Primary modality: visual
Synchronicity: synchronous
Mode: offline
Instructions: Subjects were instructed to focus attention on the flickering box, limit movements, and avoid swallowing or blinking during visual stimulation
Stimulus presentation: SoftwareName=Microsoft Visual Studio 2010 with OpenGL, monitor=22 inch LCD monitor, refresh_rate=60 Hz, resolution=1680x1080 pixels

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
Number of repetitions: 3

Data Structure

Trials: 1104
Trials context: Total 1104 trials across all subjects. Each session includes 23 trials (8 adaptation + 15 main). S001: 3 sessions, S003 and S004: 4 sessions, others: 5 sessions. Some sessions excluded due to technical issues.

Preprocessing

Data state: raw
Preprocessing applied: False

Signal Processing

Classifiers: LDA, SVM, Random Forest, kNN, Naive Bayes, CCA, AdaBoost, Decision Trees
Feature extraction: Periodogram, Welch Spectrum, Goertzel algorithm, Yule-AR Spectrum, FFT, PSD, Discrete Wavelet Transform
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)

Default Accuracy: 72.47
Optimal Accuracy: 79.47

BCI Application

Applications: communication
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
DOI: 10.6084/m9.figshare.2068677.v1
Associated paper DOI: 10.48550/arXiv.1602.00904
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: GR
Repository: Figshare
Data URL: https://dx.doi.org/10.6084/m9.figshare.2068677.v1
Publication year: 2016
Funding: H2020-ICT-2014-644780
Ethics approval: Centre for Research and Technology Hellas ethics committee, dated 3/7/2015, grant H2020-ICT-2014-644780
Keywords: SSVEP, BCI, EEG, brain-computer interface, comparative evaluation, state-of-the-art algorithms

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 SSVEP-based BCIs and performs a comparative evaluation of the most promising algorithms. A dataset of 256-channel EEG signals from 11 subjects is provided, along with a processing toolbox for reproducing results and supporting further experimentation.

Methodology

Empirical approach where each signal processing parameter (filtering, artifact removal, feature extraction, feature selection, classification) is studied independently by keeping all other parameters fixed. Leave-one-subject-out cross-validation used to evaluate system without subject-specific training. Multiple algorithms compared for each processing stage to obtain state-of-the-art baseline.

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_I_256_channels_11_subjects_5_frequencies_/2068677?file=3793738 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	`NM000119`
Title	Oikonomou2016 – SSVEP MAMEM 1 dataset
Author (year)	`Oikonomou2016_MAMEM1`
Canonical	`Oikonomou2016`
Importable as	`NM000119`, `Oikonomou2016_MAMEM1`, `Oikonomou2016`
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: 47
Tasks: 1

Channels & sampling rate

Channels: 256
Sampling rate (Hz): 250.0
Duration (hours): 6.22372

API Reference#

Use the NM000119 class to access this dataset programmatically.

class eegdash.dataset.NM000119(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Oikonomou2016 – SSVEP MAMEM 1 dataset

Study:: nm000119 (NeMAR)
Author (year):: Oikonomou2016_MAMEM1
Canonical:: Oikonomou2016

Also importable as: NM000119, Oikonomou2016_MAMEM1, Oikonomou2016.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 11; recordings: 47; 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/nm000119 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000119

Examples

>>> from eegdash.dataset import NM000119
>>> dataset = NM000119(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