NM000209: eeg dataset, 25 subjects#

Motor imagery + spatial attention dataset from Forenzo & He 2023

Access recordings and metadata through EEGDash.

Citation: Dylan Forenzo, Yixuan Liu, Jeehyun Kim, Yidan Ding, Taehyung Yoon, Bin He (2024). Motor imagery + spatial attention dataset from Forenzo & He 2023.

Modality: eeg Subjects: 25 Recordings: 150 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000209

dataset = NM000209(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 = NM000209(cache_dir="./data", subject="01")

Advanced query

dataset = NM000209(
    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{nm000209,
  title = {Motor imagery + spatial attention dataset from Forenzo & He 2023},
  author = {Dylan Forenzo and Yixuan Liu and Jeehyun Kim and Yidan Ding and Taehyung Yoon and Bin He},
}

About This Dataset#

Motor imagery + spatial attention dataset from Forenzo & He 2023

Motor imagery + spatial attention dataset from Forenzo & He 2023.

Dataset Overview

Code: Forenzo2023
Paradigm: imagery
DOI: 10.1109/TBME.2023.3298957

View full README

Motor imagery + spatial attention dataset from Forenzo & He 2023

Motor imagery + spatial attention dataset from Forenzo & He 2023.

Dataset Overview

Code: Forenzo2023
Paradigm: imagery
DOI: 10.1109/TBME.2023.3298957
Subjects: 25
Sessions per subject: 5
Events: left_hand=1, right_hand=2
Trial interval: [0, 4] s
Runs per session: 3
File format: MAT

Acquisition

Sampling rate: 1000.0 Hz
Number of channels: 64
Channel types: eeg=64
Montage: standard_1005
Hardware: Neuroscan Quik-Cap 64-ch, SynAmps 2/RT
Reference: between Cz and CPz
Sensor type: Ag/AgCl
Line frequency: 60.0 Hz
Online filters: {‘lowpass’: 200, ‘notch_hz’: 60}

Participants

Number of subjects: 25
Health status: healthy
Age: mean=25.5
Gender distribution: female=10, male=15
Handedness: right-handed (24 of 25)
BCI experience: mixed (19 naive, 6 experienced)
Species: human

Experimental Protocol

Paradigm: imagery
Number of classes: 2
Class labels: left_hand, right_hand
Trial duration: 6.0 s
Study design: 5-session BCI study with motor imagery (MI), overt spatial attention (OSA), and combined (MIOSA) tasks
Feedback type: cursor
Stimulus type: continuous pursuit
Stimulus modalities: visual
Primary modality: visual
Synchronicity: synchronous
Mode: online

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: left_hand, right_hand
Imagery duration: 6.0 s

Data Structure

Trials: 1875
Trials context: 25 subjects x 5 sessions x 3 MI runs x 5 trials

Signal Processing

Classifiers: linear_classifier
Feature extraction: AR_spectral_estimation, alpha_bandpower
Frequency bands: alpha=[8.0, 13.0] Hz
Spatial filters: Laplacian

Cross-Validation

Evaluation type: within_subject

BCI Application

Applications: cursor_control
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: Motor
Type: Research

Documentation

DOI: 10.1109/TBME.2023.3298957
License: CC-BY-4.0
Investigators: Dylan Forenzo, Yixuan Liu, Jeehyun Kim, Yidan Ding, Taehyung Yoon, Bin He
Institution: Carnegie Mellon University
Department: Department of Biomedical Engineering
Country: US
Data URL: https://kilthub.cmu.edu/articles/dataset/23677098
Publication year: 2023

References

Forenzo, D., & He, B. (2024). Integrating simultaneous motor imagery and spatial attention for EEG-BCI control. IEEE Trans. Biomed. Eng., 71(1), 282-294. https://doi.org/10.1109/TBME.2023.3298957 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	`NM000209`
Title	Motor imagery + spatial attention dataset from Forenzo & He 2023
Author (year)	`Forenzo2023`
Canonical	—
Importable as	`NM000209`, `Forenzo2023`
Year	2024
Authors	Dylan Forenzo, Yixuan Liu, Jeehyun Kim, Yidan Ding, Taehyung Yoon, Bin He
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: 25
Recordings: 150
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0
Duration (hours): 7.572991388888889

Electrode Layout#

Electrode layout — EEG · 62 sensors — 62 channels

Dataset Statistics#

Age distribution (n=25, range 25–25 yr)

25

Channel counts: 64 ch (n=150 recordings)

Sampling frequencies: 1000.0 Hz (n=150 recordings)

Total recording duration: 7 h 34 min

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 NM000209 class to access this dataset programmatically.

class eegdash.dataset.NM000209(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Motor imagery + spatial attention dataset from Forenzo & He 2023

Study:: nm000209 (NeMAR)
Author (year):: Forenzo2023
Canonical:: —

Also importable as: NM000209, Forenzo2023.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 150; 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/nm000209 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000209

Examples

>>> from eegdash.dataset import NM000209
>>> dataset = NM000209(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)