NM000172: eeg dataset, 14 subjects#

High-gamma dataset described in Schirrmeister et al. 2017

Access recordings and metadata through EEGDash.

Citation: Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball (2017). High-gamma dataset described in Schirrmeister et al. 2017.

Modality: eeg Subjects: 14 Recordings: 28 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000172

dataset = NM000172(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000172(cache_dir="./data", subject="01")

Advanced query

dataset = NM000172(
    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{nm000172,
  title = {High-gamma dataset described in Schirrmeister et al. 2017},
  author = {Robin Tibor Schirrmeister and Jost Tobias Springenberg and Lukas Dominique Josef Fiederer and Martin Glasstetter and Katharina Eggensperger and Michael Tangermann and Frank Hutter and Wolfram Burgard and Tonio Ball},
}

About This Dataset#

High-gamma dataset described in Schirrmeister et al. 2017

High-gamma dataset described in Schirrmeister et al. 2017.

Dataset Overview

  • Code: Schirrmeister2017

  • Paradigm: imagery

  • DOI: 10.1002/hbm.23730

View full README

High-gamma dataset described in Schirrmeister et al. 2017

High-gamma dataset described in Schirrmeister et al. 2017.

Dataset Overview

  • Code: Schirrmeister2017

  • Paradigm: imagery

  • DOI: 10.1002/hbm.23730

  • Subjects: 14

  • Sessions per subject: 1

  • Events: right_hand=1, left_hand=2, rest=3, feet=4

  • Trial interval: [0, 4] s

  • Runs per session: 2

  • File format: EDF

Acquisition

  • Sampling rate: 500.0 Hz

  • Number of channels: 128

  • Channel types: eeg=128

  • Channel names: Fp1, Fp2, Fpz, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, M1, T7, C3, Cz, C4, T8, M2, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, POz, O1, Oz, O2, AF7, AF3, AF4, AF8, F5, F1, F2, F6, FC3, FCz, FC4, C5, C1, C2, C6, CP3, CPz, CP4, P5, P1, P2, P6, PO5, PO3, PO4, PO6, FT7, FT8, TP7, TP8, PO7, PO8, FT9, FT10, TPP9h, TPP10h, PO9, PO10, P9, P10, AFF1, AFz, AFF2, FFC5h, FFC3h, FFC4h, FFC6h, FCC5h, FCC3h, FCC4h, FCC6h, CCP5h, CCP3h, CCP4h, CCP6h, CPP5h, CPP3h, CPP4h, CPP6h, PPO1, PPO2, I1, Iz, I2, AFp3h, AFp4h, AFF5h, AFF6h, FFT7h, FFC1h, FFC2h, FFT8h, FTT9h, FTT7h, FCC1h, FCC2h, FTT8h, FTT10h, TTP7h, CCP1h, CCP2h, TTP8h, TPP7h, CPP1h, CPP2h, TPP8h, PPO9h, PPO5h, PPO6h, PPO10h, POO9h, POO3h, POO4h, POO10h, OI1h, OI2h

  • Montage: standard_1005

  • Software: BCI2000

  • Sensor type: EEG

  • Line frequency: 50.0 Hz

Participants

  • Number of subjects: 14

  • Health status: healthy

  • Age: mean=27.2, std=3.6

  • Gender distribution: female=6, male=8

  • Handedness: {‘right’: 12, ‘left’: 2}

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 4

  • Class labels: right_hand, left_hand, rest, feet

  • Trial duration: 4.0 s

  • Study design: Executed movements including left hand (sequential finger-tapping), right hand (sequential finger-tapping), feet (repetitive toe clenching), and rest conditions

  • Stimulus type: visual

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: cue-based

  • Mode: offline

  • Training/test split: True

  • Instructions: Subjects performed repetitive movements at their own pace when arrow was showing

  • Stimulus presentation: type=gray arrow on black background, direction_mapping=downward=feet, leftward=left_hand, rightward=right_hand, upward=rest

HED Event Annotations

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

right_hand

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

left_hand

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

rest

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

feet

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

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: left_hand_finger_tapping, right_hand_finger_tapping, feet_toe_clenching, rest

Data Structure

  • Trials: {‘total_per_subject’: 963, ‘training_set’: 880, ‘test_set’: 160}

  • Trials per class: per_class_per_subject=260

  • Blocks per session: 13

  • Trials context: 13 runs per subject, 80 trials per run (4 seconds each), 3-4 seconds inter-trial interval, pseudo-randomized presentation with all 4 classes shown every 4 trials

Signal Processing

  • Classifiers: Deep ConvNet, Shallow ConvNet, ResNet, FBCSP with LDA

  • Feature extraction: FBCSP, CSP, Bandpower, Spectral power modulations

  • Frequency bands: alpha=[7.0, 13.0] Hz; beta=[13.0, 30.0] Hz; gamma=[30.0, 100.0] Hz

  • Spatial filters: CSP

Cross-Validation

  • Method: holdout

  • Evaluation type: within_subject

Performance (Original Study)

  • Fbcsp Accuracy: 91.2

  • Deep Convnet Accuracy: 89.3

  • Shallow Convnet Accuracy: 92.5

BCI Application

  • Applications: motor_control

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Motor Imagery, Motor Execution

Documentation

  • DOI: 10.1002/hbm.23730

  • License: CC-BY-4.0

  • Investigators: Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball

  • Senior author: Tonio Ball

  • Contact: robin.schirrmeister@uniklinik-freiburg.de

  • Institution: University of Freiburg

  • Department: Translational Neurotechnology Lab, Epilepsy Center, Medical Center

  • Address: Engelberger Str. 21, Freiburg 79106, Germany

  • Country: DE

  • Repository: GitHub

  • Data URL: https://web.gin.g-node.org/robintibor/high-gamma-dataset/

  • Publication year: 2017

  • Funding: BrainLinks-BrainTools Cluster of Excellence (DFG) EXC1086; Federal Ministry of Education and Research (BMBF) Motor-BIC 13GW0053D

  • Ethics approval: Approved by the ethical committee of the University of Freiburg

  • Acknowledgements: Funded by BrainLinks-BrainTools Cluster of Excellence (DFG, EXC1086) and the Federal Ministry of Education and Research (BMBF, Motor-BIC 13GW0053D).

  • How to acknowledge: Please cite: Schirrmeister et al. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 38(11), 5391-5420. https://doi.org/10.1002/hbm.23730

  • Keywords: electroencephalography, EEG analysis, machine learning, end-to-end learning, brain-machine interface, brain-computer interface, model interpretability, brain mapping

Abstract

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning. This study investigates deep ConvNets for end-to-end EEG decoding of imagined or executed movements from raw EEG. Results show that recent advances including batch normalization and exponential linear units, together with a cropped training strategy, boosted decoding performance to match or exceed FBCSP (82.1% FBCSP vs 84.0% deep ConvNets). Novel visualization methods demonstrated that ConvNets learned to use spectral power modulations in alpha, beta, and high gamma frequencies with meaningful spatial distributions.

Methodology

End-to-end deep learning approach comparing shallow ConvNets, deep ConvNets, and ResNets against FBCSP baseline. Evaluated design choices including batch normalization, exponential linear units, dropout, and cropped training strategies. Novel visualization techniques developed to understand learned features and verify that ConvNets use spectral power modulations in task-relevant frequency bands.

References

Schirrmeister, Robin Tibor, et al. “Deep learning with convolutional neural networks for EEG decoding and visualization.” Human brain mapping 38.11 (2017): 5391-5420. 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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000172

Title

High-gamma dataset described in Schirrmeister et al. 2017

Author (year)

Schirrmeister2017

Canonical

—

Importable as

NM000172, Schirrmeister2017

Year

2017

Authors

Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball

License

CC-BY-4.0

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: 14

  • Recordings: 28

  • Tasks: 1

Channels & sampling rate

  • Channels: 128

  • Sampling rate (Hz): 500.0

  • Duration (hours): 28.695817777777776

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 18.5 GB

  • File count: 28

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000172 class to access this dataset programmatically.

class eegdash.dataset.NM000172(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

High-gamma dataset described in Schirrmeister et al. 2017

Study:

nm000172 (NeMAR)

Author (year):

Schirrmeister2017

Canonical:

—

Also importable as: NM000172, Schirrmeister2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 14; recordings: 28; 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/nm000172 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000172

Examples

>>> from eegdash.dataset import NM000172
>>> dataset = NM000172(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#