NM000268: eeg dataset, 29 subjects#

Name: Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification
Published: 2019-01-01
License: GPL-3.0

Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification

Access recordings and metadata through EEGDash.

Citation: Jaeyoung Shin, Alexander von Lühmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Müller (2019). Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification. 10.1109/TNSRE.2016.2628057

Modality: eeg Subjects: 29 Recordings: 174 License: GPL-3.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000268

dataset = NM000268(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 = NM000268(cache_dir="./data", subject="01")

Advanced query

dataset = NM000268(
    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{nm000268,
  title = {Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification},
  author = {Jaeyoung Shin and Alexander von Lühmann and Benjamin Blankertz and Do-Won Kim and Jichai Jeong and Han-Jeong Hwang and Klaus-Robert Müller},
  doi = {10.1109/TNSRE.2016.2628057},
  url = {https://doi.org/10.1109/TNSRE.2016.2628057},
}

About This Dataset#

Shin2017B

Mental Arithmetic Dataset from Shin et al 2017.

Dataset Overview

Code: Shin2017B Paradigm: imagery DOI: 10.1109/TNSRE.2016.2628057

View full README

Shin2017B

Mental Arithmetic Dataset from Shin et al 2017.

Dataset Overview

Code: Shin2017B Paradigm: imagery DOI: 10.1109/TNSRE.2016.2628057 Subjects: 29 Sessions per subject: 6 Events: left_hand=1, right_hand=2, subtraction=3, rest=4 Trial interval: [0, 10] s Session IDs: 1arithmetic, 3arithmetic, 5arithmetic File format: MATLAB Data preprocessed: True

Acquisition

Sampling rate: 200.0 Hz Number of channels: 30 Channel types: eeg=30, eog=2 Channel names: AFF1h, AFF2h, AFF5h, AFF6h, AFp1, AFp2, CCP3h, CCP4h, CCP5h, CCP6h, Cz, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, HEOG, P3, P4, P7, P8, POO1, POO2, PPO1h, PPO2h, Pz, T7, T8, VEOG Montage: 10-5 Hardware: BrainAmp Software: MATLAB R2013b Reference: linked mastoids Ground: Fz Sensor type: active electrodes Line frequency: 50.0 Hz Cap manufacturer: EASYCAP GmbH Cap model: custom-made stretchy fabric cap Auxiliary channels: EOG (4 ch, horizontal, vertical), ecg, respiration

Participants

Number of subjects: 29 Health status: healthy Age: mean=28.5, std=3.7 Gender distribution: male=14, female=15 Handedness: {‘right’: 29, ‘left’: 1} BCI experience: naive to MI experiment Species: human

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: subtraction, rest Trial duration: 10.0 s Trials per class: subtraction=30, rest=30 Study design: Dataset B: mental arithmetic (serial subtraction of one-digit number) versus baseline/rest task Feedback type: none Stimulus type: visual instruction (subtraction problem and fixation cross) Stimulus modalities: visual, auditory Primary modality: visual Synchronicity: cued-synchronous Mode: offline Training/test split: False Instructions: For the MA task, subjects memorized an initial subtraction (three-digit minus one-digit) displayed for 2s, then repeatedly subtracted the one-digit number from each result. For baseline, subjects rested with no specific thought.

HED Event Annotations

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

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

right_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Right, Hand

subtraction

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Think
           └─ Label/subtraction

rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Number of repetitions: 20

Data Structure

Trials: {‘per_session’: 20, ‘per_condition_session’: 10, ‘per_condition_total’: 30} Trials context: Each session: 1 min pre-experiment rest + 20 trials + 1 min post-experiment rest. Trial: 2s visual instruction + 10s task + 15-17s random rest

Preprocessing

Data state: preprocessed Preprocessing applied: True Steps: common average reference, bandpass filtering (0.5-50 Hz), ICA-based EOG rejection, downsampling to 200 Hz Highpass filter: 0.5 Hz Lowpass filter: 50.0 Hz Bandpass filter: [0.5, 50.0] Filter type: Chebyshev type II Filter order: 4 Artifact methods: EOG correction, ICA Re-reference: car Downsampled to: 200.0 Hz

Signal Processing

Classifiers: LDA, Shrinkage LDA Feature extraction: CSP, log-variance Frequency bands: analyzed=[4.0, 35.0] Hz Spatial filters: CSP

Cross-Validation

Method: 10x5-fold Folds: 5 Evaluation type: within_subject

Performance (Original Study)

Ma Eeg Max Accuracy: 75.9 Ma Hbr Max Accuracy: 80.7 Ma Hbo Max Accuracy: 83.6

BCI Application

Applications: hybrid_bci_research Environment: laboratory Online feedback: False

Tags

Pathology: Healthy Modality: Cognitive Type: Cognitive

Documentation

Description: Open access dataset for hybrid brain-computer interfaces using EEG and NIRS with motor imagery and mental arithmetic tasks DOI: 10.1109/TNSRE.2016.2628057 License: GPL-3.0 Investigators: Jaeyoung Shin, Alexander von Lühmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Müller Senior author: Klaus-Robert Müller Contact: h2j@kumoh.ac.kr; klaus-robert.mueller@tuberlin.de Institution: Berlin Institute of Technology Department: Department of Computer Science, Machine Learning Group Address: 10587 Berlin, Germany Country: DE Repository: GitHub Data URL: http://doc.ml.tu-berlin.de/hBCI Publication year: 2017 Funding: Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A6A3A03057524); Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037032); Brain Korea 21 PLUS Program through the NRF funded by the Ministry of Education; Korea University Grant; BMBF (#01GQ0850, Bernstein Focus: Neurotechnology) Ethics approval: Ethics Committee of the Institute of Psychology and Ergonomics, Technical University of Berlin (approval number: SH_01_20150330) Keywords: Brain-computer interface, BCI, electroencephalography, EEG, hybrid BCI, mental arithmetic, motor imagery, near-infrared spectroscopy, NIRS, open access dataset

Abstract

Open access dataset for hybrid brain-computer interfaces using EEG and NIRS. Includes two experiments: (1) left vs right hand motor imagery, (2) mental arithmetic vs resting state. Dataset validated using baseline signal analysis showing hybrid approach enhances discrimination of mental states. Also includes motion artifacts and physiological data for wide range of validation approaches.

Methodology

Thirty subjects performed 6 sessions alternating between motor imagery (dataset A: left/right hand) and mental arithmetic (dataset B: MA vs rest). Each session: 20 trials with 2s cue, 10s task, 15-17s rest. EEG recorded at 1000 Hz with 30 channels, downsampled to 200 Hz. Preprocessing: CAR, 0.5-50 Hz bandpass (4th order Chebyshev II), ICA-based EOG rejection. Feature extraction: CSP with log-variance of first/last 3 components using 3s moving window (1s step). Classification: shrinkage LDA with 10x5-fold CV. Hybrid analysis combines EEG and NIRS outputs using meta-classifier.

References

Shin, J., von Lühmann, A., Blankertz, B., Kim, D.W., Jeong, J., Hwang, H.J. and Müller, K.R., 2017. Open access dataset for EEG+NIRS single-trial classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), pp.1735-1745. GNU General Public License, Version 3 https://www.gnu.org/licenses/gpl-3.0.txt 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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000268`
Title	Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification
Author (year)	`Shin2017_Shin2017B`
Canonical	—
Importable as	`NM000268`, `Shin2017_Shin2017B`
Year	2019
Authors	Jaeyoung Shin, Alexander von Lühmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Müller
License	GPL-3.0
Citation / DOI	doi:10.1109/TNSRE.2016.2628057
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: 29
Recordings: 174
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 200.0
Duration (hours): 29.03336944444445

Electrode Layout#

Electrode layout — EEG · 30 sensors — 30 channels

Dataset Statistics#

Age distribution (n=29, range 28–28 yr)

25

Channel counts: 32 ch (n=174 recordings)

Sampling frequencies: 200.0 Hz (n=174 recordings)

Total recording duration: 29 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000268 class to access this dataset programmatically.

class eegdash.dataset.NM000268(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Shin et al. 2017 (Experiment B) — Open Access Dataset for EEG+NIRS Single-Trial Classification

Study:: nm000268 (NeMAR)
Author (year):: Shin2017_Shin2017B
Canonical:: —

Also importable as: NM000268, Shin2017_Shin2017B.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 29; recordings: 174; 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/nm000268 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000268 DOI: https://doi.org/10.1109/TNSRE.2016.2628057

Examples

>>> from eegdash.dataset import NM000268
>>> dataset = NM000268(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)