EEGdash›NeMAR›NM000170

Iss. 170 · 10 subjects · 90 recordings · CC-BY-4.0

Dataset Brief · BNCI 2025-002 Continuous 2D Trajectory Decoding dataset

NM000170: eeg dataset, 10 subjects#

BNCI 2025-002 Continuous 2D Trajectory Decoding dataset

Access recordings and metadata through EEGDash.

Citation: Hannah S Pulferer, Brynja Ásgeirsdóttir, Valeria Mondini, Andreea I Sburlea, Gernot R Müller-Putz (2022). BNCI 2025-002 Continuous 2D Trajectory Decoding dataset. 10.82901/nemar.nm000170

Modality: eeg Subjects: 10 Recordings: 90 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — BNCI 2025-002 Continuous 2D Trajectory Decoding dataset.

Data & curation Hannah S Pulferer · Brynja Ásgeirsdóttir · Valeria Mondini · Andreea I Sburlea · Gernot R Müller-Putz
Year 2022 · Distributed via NeMAR
Funding European Research Council ERC-CoG 2015 681231 'Feel Your Reach' · NTU-TUG joint PhD program

EEG · 60 ch200 HzBIDS 1.9.0Task · imagery3 sessionsOtherVisualMotor

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000170

dataset = NM000170(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 = NM000170(cache_dir="./data", subject="01")

Advanced query

dataset = NM000170(
    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{nm000170,
  title = {BNCI 2025-002 Continuous 2D Trajectory Decoding dataset},
  author = {Hannah S Pulferer and Brynja Ásgeirsdóttir and Valeria Mondini and Andreea I Sburlea and Gernot R Müller-Putz},
  doi = {10.82901/nemar.nm000170},
  url = {https://doi.org/10.82901/nemar.nm000170},
}

§ 02Study · The README

About This Dataset#

BNCI 2025-002 Continuous 2D Trajectory Decoding dataset.

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

DOI

BNCI 2025-002 Continuous 2D Trajectory Decoding dataset

snakerun

View full README

DOI

BNCI 2025-002 Continuous 2D Trajectory Decoding dataset

snakerun

     ├─ Experiment-structure
     └─ Label/snakerun

freerun

     ├─ Experiment-structure
     └─ Label/freerun

eyerun

├─ Experiment-structure
└─ Label/eyerun

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: attempted arm/hand movement (2D continuous trajectory)

Data Structure

Trials: {‘calibration_eyeruns’: 38, ‘calibration_snakeruns’: 48, ‘50%_EEG_feedback_snakeruns’: 36, ‘100%_EEG_feedback_snakeruns’: 36, ‘freeruns’: 9}
Trials context: per_paradigm_type

Preprocessing

Data state: preprocessed
Preprocessing applied: True
Steps: anti-aliasing filter (25 Hz), notch filter (50 Hz), downsampling to 100 Hz, bad channel interpolation, eye artifact subtraction (SGEYESUB algorithm), removal of frontal (AF) row channels, high-pass filter (0.18 Hz), common average re-reference, pops and drifts attenuation (HEAR algorithm), low-pass filter (3 Hz), downsampling to 20 Hz
Highpass filter: 0.18 Hz
Lowpass filter: 3.0 Hz
Notch filter: [50] Hz
Filter type: Not specified
Artifact methods: SGEYESUB (eye artifact subtraction), HEAR (pops and drifts removal)
Re-reference: common average reference
Downsampled to: 20.0 Hz

Signal Processing

Classifiers: PLS regression with UKF smoothing
Feature extraction: Temporal features (7 time points × 55 channels = 385 features), sLORETA (source localization)
Spatial filters: Minimum norm imaging

Cross-Validation

Method: across-session
Evaluation type: within-subject, learning effects over sessions

Performance (Original Study)

Normalized Correlation Mean: 0.31
Normalized Correlation Std: 0.02
Correlation Range Rc: 0.4-0.5
Nrmse Calibration: 0.1
Nrmse 100% Feedback: 0.12

BCI Application

Applications: neuroprosthesis, robotic arm control, upper limb restoration
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy, Spinal cord injury
Modality: Visual
Type: Motor attempt, Continuous decoding

Documentation

Description: Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant
DOI: 10.1088/1741-2552/ac689f
License: CC-BY-4.0
Investigators: Hannah S Pulferer, Brynja Ásgeirsdóttir, Valeria Mondini, Andreea I Sburlea, Gernot R Müller-Putz
Senior author: Gernot R Müller-Putz
Contact: gernot.mueller@tugraz.at
Institution: Institute of Neural Engineering, Graz University of Technology
Address: Stremayrgasse 16/IV, 8010 Graz, Austria
Country: Austria
Repository: GitHub
Data URL: sccn/labstreaminglayer
Publication year: 2022
Funding: European Research Council ERC-CoG 2015 681231 ‘Feel Your Reach’; NTU-TUG joint PhD program
Ethics approval: Medical University of Graz, votum number 32–583 ex 19/20
Keywords: electroencephalography, trajectory decoding, learning effects, source localization, motor control, neuroplasticity, brain-computer interface

References

Kobler, R. J., Almeida, I., Sburlea, A. I., & Muller-Putz, G. R. (2022). Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant. Journal of Neural Engineering, 19(3), 036005. https://doi.org/10.1088/1741-2552/ac689f Notes .. versionadded:: 1.3.0 This dataset is designed for continuous decoding research, specifically for predicting 2D hand movement trajectories from EEG. Unlike classification-based motor imagery datasets, this dataset contains continuous trajectory labels suitable for regression-based decoders.

The paradigm “imagery” is used for compatibility with MOABB’s motor imagery processing pipelines, though the actual task involves attempted (rather than imagined) movements. See Also BNCI2014_001 : 4-class motor imagery dataset BNCI2014_004 : 2-class motor imagery dataset 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

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000170-blue)](https://doi.org/10.82901/nemar.nm000170) # BNCI 2025-002 Continuous 2D Trajectory Decoding dataset BNCI 2025-002 Continuous 2D Trajectory Decoding dataset. ## Dataset Overview - Code: BNCI2025-002 - Paradigm: imagery - DOI: 10.1088/1741-2552/ac689f - Subjects: 10 - Sessions per subject: 3 - Events: snakerun=1, freerun=2, eyerun=3 - Trial interval: [0, 8] s - Runs per session: 3 - File format: gdf - Data preprocessed: True ## Acquisition - Sampling rate: 200.0 Hz - Number of channels: 60 - Channel types: eeg=60, eog=4 - Channel names: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fz, HEOG1, HEOG2, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, PPO1h, PPO2h, Pz, T7, T8, TP7, TP8, VEOG1, VEOG2 - Montage: af7 af3 afz af4 af8 f7 f5 f3 f1 fz f2 f4 f6 f8 ft7 fc5 fc3 fc1 fcz fc2 fc4 fc6 ft8 t7 c5 c3 c1 cz c2 c4 c6 t8 tp7 cp5 cp3 cp1 cpz cp2 cp4 cp6 tp8 p7 p5 p3 p1 pz p2 p4 p6 p8 ppo1h ppo2h po7 po3 poz po4 po8 o1 oz o2 - Hardware: actiCAP, Brain Products GmbH - Software: MATLAB 2015b, Psychtoolbox, EEGLAB - Reference: right mastoid - Ground: Fpz - Sensor type: EEG - Line frequency: 50.0 Hz - Online filters: anti-aliasing 25 Hz, notch 50 Hz - Auxiliary channels: EOG (4 ch, horizontal, vertical) ## Participants - Number of subjects: 10 - Health status: patients - Clinical population: Healthy (able-bodied participants) + 1 SCI participant - Age: mean=24.0, std=5.0 - Gender distribution: male=5, female=5 - Handedness: {‘right’: 10} - BCI experience: naive BCI users in terms of motor decoding; 4 had previous EEG experience - Species: human ## Experimental Protocol - Paradigm: imagery - Task type: continuous 2D trajectory decoding - Number of classes: 3 - Class labels: snakerun, freerun, eyerun - Trial duration: 23.0 s - Study design: Attempted movement paradigm: participants instructed to attempt lower arm movement as if wielding a computer mouse while arm was strapped to armrest. Two task types: snakeruns (target tracking) and freeruns (self-paced shape tracing). Offline calibration followed by online feedback in 50% and 100% EEG feedback conditions. - Feedback type: visual (green dot showing EEG-decoded trajectory position) - Stimulus type: visual targets (white snake/shapes on black screen) - Stimulus modalities: visual - Primary modality: visual - Synchronicity: continuous - Mode: attempted movement - Training/test split: True - Instructions: Track snake with gaze and simultaneously attempt movement of strapped lower arm/hand as if wielding computer mouse; for freeruns: trace static shapes at own pace ## HED Event Annotations Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser ```

snakerun
├─ Experiment-structure └─ Label/snakerun

freerun
├─ Experiment-structure └─ Label/freerun

eyerun
├─ Experiment-structure └─ Label/eyerun

``` ## Paradigm-Specific Parameters - Detected paradigm: motor_imagery - Imagery tasks: attempted arm/hand movement (2D continuous trajectory) ## Data Structure - Trials: {‘calibration_eyeruns’: 38, ‘calibration_snakeruns’: 48, ‘50%_EEG_feedback_snakeruns’: 36, ‘100%_EEG_feedback_snakeruns’: 36, ‘freeruns’: 9} - Trials context: per_paradigm_type ## Preprocessing - Data state: preprocessed - Preprocessing applied: True - Steps: anti-aliasing filter (25 Hz), notch filter (50 Hz), downsampling to 100 Hz, bad channel interpolation, eye artifact subtraction (SGEYESUB algorithm), removal of frontal (AF) row channels, high-pass filter (0.18 Hz), common average re-reference, pops and drifts attenuation (HEAR algorithm), low-pass filter (3 Hz), downsampling to 20 Hz - Highpass filter: 0.18 Hz - Lowpass filter: 3.0 Hz - Notch filter: [50] Hz - Filter type: Not specified - Artifact methods: SGEYESUB (eye artifact subtraction), HEAR (pops and drifts removal) - Re-reference: common average reference - Downsampled to: 20.0 Hz ## Signal Processing - Classifiers: PLS regression with UKF smoothing - Feature extraction: Temporal features (7 time points × 55 channels = 385 features), sLORETA (source localization) - Spatial filters: Minimum norm imaging ## Cross-Validation - Method: across-session - Evaluation type: within-subject, learning effects over sessions ## Performance (Original Study) - Normalized Correlation Mean: 0.31 - Normalized Correlation Std: 0.02 - Correlation Range Rc: 0.4-0.5 - Nrmse Calibration: 0.1 - Nrmse 100% Feedback: 0.12 ## BCI Application - Applications: neuroprosthesis, robotic arm control, upper limb restoration - Environment: laboratory - Online feedback: True ## Tags - Pathology: Healthy, Spinal cord injury - Modality: Visual - Type: Motor attempt, Continuous decoding ## Documentation - Description: Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant - DOI: 10.1088/1741-2552/ac689f - License: CC-BY-4.0 - Investigators: Hannah S Pulferer, Brynja Ásgeirsdóttir, Valeria Mondini, Andreea I Sburlea, Gernot R Müller-Putz - Senior author: Gernot R Müller-Putz - Contact: gernot.mueller@tugraz.at - Institution: Institute of Neural Engineering, Graz University of Technology - Address: Stremayrgasse 16/IV, 8010 Graz, Austria - Country: Austria - Repository: GitHub - Data URL: sccn/labstreaminglayer - Publication year: 2022 - Funding: European Research Council ERC-CoG 2015 681231 ‘Feel Your Reach’; NTU-TUG joint PhD program - Ethics approval: Medical University of Graz, votum number 32–583 ex 19/20 - Keywords: electroencephalography, trajectory decoding, learning effects, source localization, motor control, neuroplasticity, brain-computer interface ## References Kobler, R. J., Almeida, I., Sburlea, A. I., & Muller-Putz, G. R. (2022). Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant. Journal of Neural Engineering, 19(3), 036005. https://doi.org/10.1088/1741-2552/ac689f Notes .. versionadded:: 1.3.0 This dataset is designed for continuous decoding research, specifically for predicting 2D hand movement trajectories from EEG. Unlike classification-based motor imagery datasets, this dataset contains continuous trajectory labels suitable for regression-based decoders. The paradigm “imagery” is used for compatibility with MOABB’s motor imagery processing pipelines, though the actual task involves attempted (rather than imagined) movements. See Also BNCI2014_001 : 4-class motor imagery dataset BNCI2014_004 : 2-class motor imagery dataset 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

License: CC-BY-4.0

Authors:

Hannah S Pulferer
Brynja Ásgeirsdóttir
Valeria Mondini
Andreea I Sburlea
Gernot R Müller-Putz

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000170

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=10, range 24–24 yr, mean 24.0 yr)

20

Other · 10

Channel counts: 60 ch (n=180 recordings)

Sampling frequencies: 200.0 Hz (n=180 recordings)

Total recording duration: 56 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 60 ch · EEG · 200 Hz · 10 subjects, 90 recordings

Live trace viewer — sub-6 · ses-1ses2 · task-imagery · run-0

Showing one representative recording out of 10 subjects and 90 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.

HED event descriptors word cloud

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000170(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Pulferer2025

Canonical—

Importable asNM000170 · Pulferer2025

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000170(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2025-002 Continuous 2D Trajectory Decoding dataset

Study:: nm000170 (NeMAR)
Author (year):: Pulferer2025
Canonical:: —

Also importable as: NM000170, Pulferer2025.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 10; recordings: 90; 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/nm000170 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000170 DOI: https://doi.org/10.82901/nemar.nm000170

Examples

>>> from eegdash.dataset import NM000170
>>> dataset = NM000170(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 FacePre-bundled mirror at EEGDash/nm000170 · pull with datasets.load_dataset("EEGDash/nm000170").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000170.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 nm000170 to reproduce the tutorial on this dataset.

Citation

Hannah S Pulferer, Brynja Ásgeirsdóttir, Valeria Mondini, Andreea I Sburlea, Gernot R Müller-Putz (2022). BNCI 2025-002 Continuous 2D Trajectory Decoding dataset. 10.82901/nemar.nm000170

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000170.

Related & sibling datasets

NM000149EEG · 10 subj NM000144EEG · 9 subj NM000230EEG · 30 subj DS004022EEG · 7 subj NM000146EEG · 10 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000170

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace · Paper

Dataset ID	`NM000170`
Title	BNCI 2025-002 Continuous 2D Trajectory Decoding dataset
Author (year)	`Pulferer2025`
Canonical	—
Importable as	`NM000170`, `Pulferer2025`
Year	2022
Authors	Hannah S Pulferer, Brynja Ásgeirsdóttir, Valeria Mondini, Andreea I Sburlea, Gernot R Müller-Putz
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000170
Source links	OpenNeuro \| NeMAR

NM000170: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#