EEGdash›NeMAR›NM000162
Iss. 162 · 20 subjects · 20 recordings · CC-BY-4.0
Dataset Brief · BNCI 2025-001 Motor Kinematics Reaching dataset

NM000162: eeg dataset, 20 subjects#

BNCI 2025-001 Motor Kinematics Reaching dataset

Citation: Nitikorn Srisrisawang, Gernot R Müller-Putz (2024). BNCI 2025-001 Motor Kinematics Reaching dataset. 10.82901/nemar.nm000162

20-participant EEG dataset — BNCI 2025-001 Motor Kinematics Reaching dataset.

Data & curation Nitikorn Srisrisawang · Gernot R Müller-Putz
Year 2024 · Distributed via NeMAR
Funding Royal Thai Government (scholar funding for N.S.) · BioTechMed Graz
EEG · 67 ch500 HzBIDS 1.9.0Task · imageryHealthyVisualMotor
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000162

dataset = NM000162(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000162(cache_dir="./data", subject="01")

Advanced query

dataset = NM000162(
    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{nm000162,
  title = {BNCI 2025-001 Motor Kinematics Reaching dataset},
  author = {Nitikorn Srisrisawang and Gernot R Müller-Putz},
  doi = {10.82901/nemar.nm000162},
  url = {https://doi.org/10.82901/nemar.nm000162},
}
§ 02Study · The README

About This Dataset#

BNCI 2025-001 Motor Kinematics Reaching dataset.

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

DOI

BNCI 2025-001 Motor Kinematics Reaching dataset

up_slow_near
View full README

DOI

BNCI 2025-001 Motor Kinematics Reaching dataset

up_slow_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Upward
           ├─ Label/slow
           └─ Label/near

up_slow_far

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Upward
           ├─ Label/slow
           └─ Label/far

up_fast_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Upward
           ├─ Label/fast
           └─ Label/near

up_fast_far

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Upward
           ├─ Label/fast
           └─ Label/far

down_slow_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Downward
           ├─ Label/slow
           └─ Label/near

down_slow_far

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Downward
           ├─ Label/slow
           └─ Label/far

down_fast_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Downward
           ├─ Label/fast
           └─ Label/near

down_fast_far

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Downward
           ├─ Label/fast
           └─ Label/far

left_slow_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Left
           ├─ Label/slow
           └─ Label/near

left_slow_far

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Left
           ├─ Label/slow
           └─ Label/far

left_fast_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Left
           ├─ Label/fast
           └─ Label/near

left_fast_far

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Left
           ├─ Label/fast
           └─ Label/far

right_slow_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Right
           ├─ Label/slow
           └─ Label/near

right_slow_far

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Right
           ├─ Label/slow
           └─ Label/far

right_fast_near

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Reach
           ├─ Right
           ├─ Label/fast
           └─ Label/near

right_fast_far

├─ Sensory-event, Experimental-stimulus, Visual-presentation
└─ Agent-action
   └─ Reach
      ├─ Right
      ├─ Label/fast
      └─ Label/far

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Number of targets: 4

  • Imagery tasks: right_hand_reaching

Data Structure

  • Trials: 960

  • Trials per class: up=240, down=240, left=240, right=240

  • Blocks per session: 10

  • Block duration: 1200.0 s

  • Trials context: per_participant (before rejection)

Preprocessing

  • Data state: preprocessed with eye artifact correction

  • Preprocessing applied: True

  • Steps: low-pass filter at 100 Hz, notch filter at 50 Hz, downsampling to 200 Hz, bad channel rejection and interpolation, bandpass filter 0.3-80 Hz, eye artifact correction via SGEYESUB, ICA with FastICA algorithm, IC artifact removal, low-pass filter at 3 Hz, downsampling to 10 Hz, bad trial rejection, common average reference

  • Highpass filter: 0.3 Hz

  • Lowpass filter: 100.0 Hz

  • Bandpass filter: {‘low_cutoff_hz’: 0.3, ‘high_cutoff_hz’: 80.0}

  • Notch filter: [50] Hz

  • Filter type: Butterworth

  • Filter order: 2

  • Artifact methods: ICA, SGEYESUB (Sparse Generalized Eye Artifact Subspace Subtraction), IClabel plugin

  • Re-reference: common average

  • Downsampled to: 200.0 Hz

  • Epoch window: [-3.0, 4.0]

  • Notes: Frontal channels (AF7, AF3, AFz, AF4, AF8) and EOG removed prior to CAR to reduce residual eye artifacts. Final analysis used 55 channels. Eye blocks recorded separately for SGEYESUB model training. Bad trials rejected based on amplitude >200 µV or standard deviation >5SD. Movement-related bad trials rejected for incorrect direction, no movement, duration <0.2s or >4s, or movement initiated <0.5s after cue stop.

Signal Processing

  • Classifiers: sLDA (shrinkage Linear Discriminant Analysis)

  • Feature extraction: Low-frequency EEG (0.3-3 Hz), Source localization (sLORETA), ICA, ROI-based features

  • Frequency bands: delta=[0.3, 3.0] Hz; analyzed=[0.3, 100.0] Hz

  • Spatial filters: Common Average Reference, Source-space projection

Cross-Validation

  • Method: stratified k-fold

  • Folds: 10

  • Evaluation type: within_session

Performance (Original Study)

  • Direction Accuracy Cstp: 39.75

  • Direction Accuracy Mon: 42.42

  • Speed Accuracy Cstp: 66.03

  • Speed Accuracy Mon: 70.49

  • Distance Accuracy Cstp: 60.83

  • Distance Accuracy Mon: 55.41

  • Quick Direction Accuracy Cstp: 44.12

  • Quick Direction Accuracy Mon: 49.67

  • Slow Direction Accuracy Cstp: 37.42

  • Slow Direction Accuracy Mon: 35.89

BCI Application

  • Applications: motor_control, rehabilitation

  • Environment: laboratory

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Documentation

  • Description: EEG dataset investigating simultaneous encoding of speed, distance, and direction in discrete hand reaching movements using a four-direction center-out task

  • DOI: 10.1088/1741-2552/ada0ea

  • License: CC-BY-4.0

  • Investigators: Nitikorn Srisrisawang, Gernot R Müller-Putz

  • Senior author: Gernot R Müller-Putz

  • Contact: gernot.mueller@tugraz.at

  • Institution: Institute of Neural Engineering, Graz University of Technology

  • Department: Institute of Neural Engineering

  • Address: Stremayrgasse 16/IV, 8010 Graz, Austria

  • Country: Austria

  • Repository: GitHub

  • Data URL: rkobler/eyeartifactcorrection

  • Publication year: 2024

  • Funding: Royal Thai Government (scholar funding for N.S.); BioTechMed Graz

  • Ethics approval: Ethical committee at the Graz University of Technology (EK-28/2024); Declaration of Helsinki

  • Acknowledgements: Members of the Graz BCI team, especially Markus Crell for providing motion capture software

  • Keywords: electroencephalography, brain–computer interface, source localization, discrete reaching, center-out task

Abstract

Objective. The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored. Approach. We utilized electroencephalography (EEG) signals and hand position recorded during a four-direction center-out reaching task with either quick or slow speed, near and far distance. Linear models were employed in two modes: decoding and encoding. First, to test the discriminability of speed, distance, and direction. Second, to find the contribution of the cortical sources via the source localization. Additionally, we compared the decoding accuracy when using features obtained from EEG signals and source-localized EEG signals based on the results from the encoding model. Main results. Speed, distance, and direction can be classified better than chance. The accuracy of the speed was also higher than the distance, indicating a stronger representation of the speed than the distance. The speed and distance showed similar significant sources in the central regions related to the movement initiation, while the direction indicated significant sources in the parieto-occipital regions related to the movement preparation. The combination of the features from EEG and source localized signals improved the classification. Significance. Directional and non-directional information are represented in two separate networks. The quick movement resulted in improvement in the direction classification. Our results enhance our understanding of hand movement in the brain and help us make informed decisions when designing an improved paradigm in the future.

Methodology

Participants performed discrete reaching movements in four directions (up, down, left, right) with two speeds (quick: 0.4-0.8s cue duration, slow: 1.2-2.4s cue duration) and two distances (near: ~5cm/8.7cm actual, far: ~10cm/15.6cm actual). Each trial consisted of outward and inward movements. Visual cue moved from center to target position. Participants waited ≥1s after cue stop before mimicking movement with eyes fixated on cue. Hand position tracked via camera with pink marker on right index finger. 32 conditions (2 speed × 2 distance × 4 direction × 2 inward/outward) with 30 trials per class = 960 trials total per participant. After rejection, ~852 trials remained. EEG processed with EEGLAB on MATLAB R2019b. Signals epoched in two alignments: cue stop aligned (CStp: -3 to 4s) and movement onset aligned (MOn: -3 to 3s). Analysis included MRCP analysis, point-wise classification with instantaneous and windowed (500ms) features, encoding model using GLM, source localization using BEM with ICBM152 template and sLORETA inverse solution via Brainstorm, and source-space classification using data-driven ROIs derived from encoding model. Classification performed with shrinkage LDA. Permutation testing (1000 repetitions) used for significance. FDR controlled using Benjamini-Hochberg procedures.

References

Srisrisawang, N., & Muller-Putz, G. R. (2024). Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study. Journal of Neural Engineering, 21(6). https://doi.org/10.1088/1741-2552/ada0ea Notes .. versionadded:: 1.3.0 This dataset is notable for its multi-parameter kinematic design, enabling study of how multiple movement parameters are represented simultaneously in EEG activity. The paradigm uses movement execution rather than motor imagery, making it complementary to MI datasets.

The data is compatible with the MOABB motor imagery paradigm for processing purposes, though the underlying task is movement execution. 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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=20, range 26–26 yr, mean 26.0 yr)

25
Other · 20

Channel counts: 67 ch (n=20 recordings)

Sampling frequencies: 500.0 Hz (n=20 recordings)

Total recording duration: 44 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 67 ch · EEG · 500 Hz · 20 subjects, 20 recordings
Live trace viewer — sub-13 · ses-0 · task-imagery · run-0

Showing one representative recording out of 20 subjects and 20 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 60 sensors — 60 channels

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.

NEMAR pipeline statistics — NM000162
HED event descriptors word cloud HED event descriptors word cloud — NM000162
§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

NM000162

Title

BNCI 2025-001 Motor Kinematics Reaching dataset

Author (year)

Srisrisawang2025

Canonical

—

Importable as

NM000162, Srisrisawang2025

Year

2024

Authors

Nitikorn Srisrisawang, Gernot R Müller-Putz

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000162

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000162,
  title = {BNCI 2025-001 Motor Kinematics Reaching dataset},
  author = {Nitikorn Srisrisawang and Gernot R Müller-Putz},
  doi = {10.82901/nemar.nm000162},
  url = {https://doi.org/10.82901/nemar.nm000162},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000162(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Srisrisawang2025
Canonical—
Importable asNM000162 · Srisrisawang2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000162(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2025-001 Motor Kinematics Reaching dataset

Study:

nm000162 (NeMAR)

Author (year):

Srisrisawang2025

Canonical:

—

Also importable as: NM000162, Srisrisawang2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 20; 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/nm000162 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000162 DOI: https://doi.org/10.82901/nemar.nm000162

Examples

>>> from eegdash.dataset import NM000162
>>> dataset = NM000162(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)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — NM000162.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.Load one EEG recordingResolve a single record to an MNE Raw with channels and events.EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000162 to reproduce the tutorial on this dataset.

Citation

Nitikorn Srisrisawang, Gernot R Müller-Putz (2024). BNCI 2025-001 Motor Kinematics Reaching dataset. 10.82901/nemar.nm000162

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000162.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000162
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#