NM000106#

handwriting: Handwriting Recognition from EMG

Access recordings and metadata through EEGDash.

Citation: Patrick Kaifosh, Thomas R. Reardon, CTRL-labs at Reality Labs (2025). handwriting: Handwriting Recognition from EMG. 10.5281/zenodo.17283865

Modality: emg Subjects: 100 Recordings: 807 License: CC-BY-NC 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000106

dataset = NM000106(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 = NM000106(cache_dir="./data", subject="01")

Advanced query

dataset = NM000106(
    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{nm000106,
  title = {handwriting: Handwriting Recognition from EMG},
  author = {Patrick Kaifosh and Thomas R. Reardon and CTRL-labs at Reality Labs},
  doi = {10.5281/zenodo.17283865},
  url = {https://doi.org/10.5281/zenodo.17283865},
}

About This Dataset#

handwriting: Handwriting Recognition from EMG

Overview

Dataset: handwriting - Imagined handwriting from wrist-based surface electromyography Task: Air-writing (imagined handwriting without pen) Participants: 100 subjects Sessions: ~700 total (~7 per subject)

View full README

handwriting: Handwriting Recognition from EMG

Overview

Dataset: handwriting - Imagined handwriting from wrist-based surface electromyography Task: Air-writing (imagined handwriting without pen) Participants: 100 subjects Sessions: ~700 total (~7 per subject) Publication: Kaifosh et al., 2025 - “A generic non-invasive neuromotor interface for human-computer interaction” (Nature)

Purpose

This dataset captures wrist-based sEMG signals during imagined handwriting motions for text entry. Participants “write” prompted text with fingers together (as if holding an invisible pen) without any physical writing surface. Applications include AR/VR text input, mobile computing, and hands-free communication.

Dataset Details

Participants

Sample size: 100 participants
Demographics: Not available (marked as n/a)
Recording side: Dominant wrist
Sessions: Average 7 per participant

Hardware

Device: sEMG-RD (single wristband)
Channels: 16 (EMG0-EMG15)
Sampling rate: 2000 Hz
Reference: Bipolar differential

Recording Protocol

Participant holds fingers together (as if holding pen)
Prompted text appears on screen
Participant “writes” the text in air
Session duration: ~11 minutes
Prompts per session: 96 phrases

Data Contents

Files per Session

sub-XXX/ses-XXX/emg/

├── sub-XXX_ses-XXX_task-handwriting_emg.edf
├── sub-XXX_ses-XXX_task-handwriting_emg.json
├── sub-XXX_ses-XXX_task-handwriting_channels.tsv
├── sub-XXX_ses-XXX_task-handwriting_events.tsv
└── sub-XXX_ses-XXX_electrodes.tsv

Events

Handwriting prompts: Text to be written - prompt_text: Displayed phrase
Stage boundaries: Posture changes (sitting/standing), session phases

Coordinate System

Single coordinate system at root (dominant wrist, percent units, no decimals)

Baseline Performance

Published Results (Kaifosh et al., 2025)

Generic Model (6,527 training participants): - Offline CER: >90% classification accuracy on held-out participants - Online performance: 20.9 words per minute (WPM) - Online CER: Median improvement from ~35% (practice) to ~25% (evaluation)

Personalized Model (20 min fine-tuning): - 16% improvement over generic model - Better performance for users with higher generic CER - Diminishing returns with more pretraining data

Comparison: - Open-loop handwriting (no pen): 25.1 WPM - sEMG handwriting: 20.9 WPM (83% of baseline) - Mobile phone keyboard: 36 WPM

Model architecture: MPF features + Conformer (attention mechanism)

Use Cases

Keyboard-free text entry: AR/VR, mobile devices
Silent communication: Private text input in public spaces
Personalization research: Few-shot learning, transfer learning
Sequence modeling: Character-level prediction with attention

Known Limitations

Single wrist (dominant hand only)
Handedness not recorded
Learning curve: Users improve with practice/coaching
Lower WPM than physical writing or typing

Citation

Kaifosh, P., Reardon, T.R., & CTRL-labs at Reality Labs. (2025).
A generic non-invasive neuromotor interface for human-computer interaction.
Nature, 645(8081), 702-711. https://doi.org/10.1038/s41586-025-09255-w

Data Curator

Yahya Shirazi SCCN (Swartz Center for Computational Neuroscience) INC (Institute for Neural Computation) University of California San Diego

Version History

v1.0 (2025-10-01): Initial BIDS conversion

BIDS Version: 1.11 | EMG-BIDS: BEP-042 | Updated: Oct 1, 2025

Dataset Information#

Dataset ID	`NM000106`
Title	handwriting: Handwriting Recognition from EMG
Year	2025
Authors	Patrick Kaifosh, Thomas R. Reardon, CTRL-labs at Reality Labs
License	CC-BY-NC 4.0
Citation / DOI	10.5281/zenodo.17283865
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: 100
Recordings: 807
Tasks: 1

Channels & sampling rate

Channels: 16
Sampling rate (Hz): 2000.0
Duration (hours): 0.0

API Reference#

Use the NM000106 class to access this dataset programmatically.

class eegdash.dataset.NM000106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset nm000106. Modality: emg; Experiment type: Motor; Subject type: Healthy. Subjects: 100; recordings: 807; 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/nm000106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000106

Examples

>>> from eegdash.dataset import NM000106
>>> dataset = NM000106(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