NM000105#

discrete_gestures: Discrete Hand Gesture Detection from EMG

Access recordings and metadata through EEGDash.

Citation: Patrick Kaifosh, Thomas R. Reardon, CTRL-labs at Reality Labs (2025). discrete_gestures: Discrete Hand Gesture Detection from EMG. 10.5281/zenodo.17283593

Modality: emg Subjects: 100 Recordings: 100 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 NM000105

dataset = NM000105(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 = NM000105(cache_dir="./data", subject="01")

Advanced query

dataset = NM000105(
    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{nm000105,
  title = {discrete_gestures: Discrete Hand Gesture Detection from EMG},
  author = {Patrick Kaifosh and Thomas R. Reardon and CTRL-labs at Reality Labs},
  doi = {10.5281/zenodo.17283593},
  url = {https://doi.org/10.5281/zenodo.17283593},
}

About This Dataset#

discrete_gestures: Discrete Hand Gesture Detection from EMG

Overview

Dataset: discrete_gestures - Discrete hand gestures from wrist-based surface electromyography Task: Nine discrete hand gestures (pinches and swipes) Participants: 100 subjects Sessions: 100 total (1 per subject)

View full README

discrete_gestures: Discrete Hand Gesture Detection from EMG

Overview

Dataset: discrete_gestures - Discrete hand gestures from wrist-based surface electromyography Task: Nine discrete hand gestures (pinches and swipes) Participants: 100 subjects Sessions: 100 total (1 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 prompted discrete hand gestures for navigation and activation tasks. The goal is to enable gesture-based computer control without cameras or visible hand movements, with applications in AR/VR, mobile interfaces, and accessibility. Key research objectives: - Generic models that work across users without calibration - Discrete gesture classification with high accuracy - Real-time gesture detection for interactive systems - Robustness to electrode placement variability

Dataset Details

Participants

Sample size: 100 participants Demographics: Not available (age, sex, handedness marked as n/a) Recording side: Dominant wrist (assumed right-handed, varies by participant) Sessions: 1 session per participant

Hardware

Device: sEMG Research Device (sEMG-RD) Configuration: Single wristband (dominant wrist) Channels: 16 Sampling rate: 2000 Hz Bit depth: 12 bits Dynamic range: ±6.6 mV Bandwidth: 20-850 Hz Connectivity: Bluetooth Electrode type: Dry gold-plated differential pairs

Gestures

Nine discrete gestures: Thumb swipes (4): - Left swipe - Right swipe - Up swipe - Down swipe

Pinches (4): - Index-to-thumb pinch - Middle-to-thumb pinch - Ring-to-thumb pinch - Pinky-to-thumb pinch

Activation (1): - Thumb tap

Recording Protocol

Participant dons sEMG-RD on dominant wrist
Gesture prompter displays gesture cue (scrolling left-to-right)
Participant performs prompted gesture
Randomized order with randomized inter-gesture intervals
Multiple repetitions of each gesture type

Session duration: Varies by participant Total gestures: 1900 prompted gestures across all participants Stage boundaries: 16 recording stages per session

Data Contents

Files per Session

sub-XXX/ses-XXX/emg/

├── sub-XXX_ses-XXX_task-discretegestures_emg.edf
├── sub-XXX_ses-XXX_task-discretegestures_emg.json
├── sub-XXX_ses-XXX_task-discretegestures_channels.tsv
├── sub-XXX_ses-XXX_task-discretegestures_events.tsv
└── sub-XXX_ses-XXX_electrodes.tsv

Channel Configuration

Total channels: 16 (EMG0-EMG15) Channel naming: Unique identifiers (EMG0-EMG15) Electrode naming: E0-E15 (physical positions) Reference: Bipolar (differential sensing) channels.tsv columns: - name: Channel identifier (EMG0-EMG15) - type: EMG - units: V - signal_electrode: Physical electrode name (E0-E15) - reference: bipolar

electrodes.tsv columns: - name: Electrode identifier (E0-E15) - x, y, z: 3D coordinates (percent units, no decimals)

Events

events.tsv contains: - Gesture prompts: Timestamped prompts for each gesture

type: gesture_X (where X is the gesture name)

latency: Sample index when gesture was prompted

gesture_type: Specific gesture (e.g., “index_pinch”, “thumb_swipe_left”)

Stage boundaries: Recording session phases - type: stage_boundary - stage_name: Stage identifier

Total events: 1916 (1900 gesture prompts + 16 stage boundaries)

Coordinate System

Single coordinate system (no space entity):

EMGCoordinateSystem: Other
EMGCoordinateUnits: percent
X: USP → RSP (0-100%)
Y: Right-hand rule perpendicular (0-100%)
Z: Radial offset (constant 10%)

Anatomical landmarks: - RSP: Radial Styloid Process - USP: Ulnar Styloid Process

Note: Right-handed coordinate system for dominant wrist

Signal Processing

Preprocessing Applied

High-pass filtering: 40 Hz cutoff
Clock drift correction: Time synchronization
Irregular sampling handling: Resampling when deviation >1% (up to 9290% deviation detected)

Signal Characteristics

Gesture patterns: - Patterned activity across channels corresponding to flexor/extensor muscles - Fine differences across gesture instances - Channel activity correlates with muscle positions (Fig. 1 in paper)

Baseline Performance

Published Results (Kaifosh et al., 2025)

Offline Classification (held-out participants): - Accuracy: >90% for gesture classification - False-negative rate improves with more training data - Generic models trained on hundreds of participants

Closed-loop Performance (n=24 naive test users): - First-hit probability: Median improvement from 0.74 (practice) to 0.82 (evaluation block 2) - Gesture completion rate: Median 0.88 gestures/second (evaluation block 2) - Baseline comparison: Gaming controller achieves 1.45 completions/second

Model architecture: 1D convolution → LSTM layers Learning effects: Participants improve from practice to evaluation blocks

Representation Analysis

Network learns: - First layer filters resemble motor unit action potentials (MUAPs) - Deeper layers progressively separate gesture categories - Invariance to nuisance variables (participant ID, electrode placement, signal power)

Confusion Matrix

Common confusions (from paper): - Index and middle holds sometimes released too early - Similar gestures (e.g., adjacent finger pinches) occasionally confused - Swipe directions generally well-separated

Note: Some errors are behavioral (wrong gesture performed) not just decoding errors

Use Cases

Machine Learning

Time series classification: Discrete event detection
Generic modeling: Out-of-the-box cross-user generalization
Representation learning: Physiologically-grounded features
Real-time prediction: Low-latency gesture detection

Applications

Grid navigation: Discrete movement in 2D space
Menu selection: Activation gestures for UI elements
Game control: Gesture-based game inputs
AR/VR interfaces: Hands-free navigation
Accessibility: Alternative input modality

Known Issues and Limitations

By Design

Single wrist: Dominant hand only (not bilateral)
Handedness unknown: Assumed right-handed, varies by participant
Gesture novelty: Users needed coaching to learn effective gestures
No demographic data: Age, sex, handedness not collected

Technical

Electrode placement: Single session per user (less cross-session data than emg2qwerty)
Signal amplitude: Varies with gesture force
Hardware unavailable: sEMG-RD not commercially available

Data Quality

Irregular sampling: High deviation detected (up to 9290%), resampling applied
Behavioral errors: Not all errors are decoder errors (some user mistakes)

Comparison to Baselines

Nintendo Joy-Con controller: - Median: 1.45 completions/second - sEMG decoder: 0.88 completions/second (66% slower)

However: sEMG doesn’t require hand-encumbering device

BIDS Format

Pernet, C.R., et al. (2019). EEG-BIDS, an extension to the brain
imaging data structure for electroencephalography.
Scientific Data, 6(1), 103.

Access and Contact

Original data: Part of Meta Reality Labs neuromotor interface research BIDS conversion: Custom MATLAB tools using EEGLAB BIDS plugin Data curator: Yahya Shirazi, SCCN (Swartz Center for Computational Neuroscience), INC (Institute for Neural Computation), UCSD Contact: See Nature paper for corresponding authors

License

Research and educational use. See original publication.

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	`NM000105`
Title	discrete_gestures: Discrete Hand Gesture Detection 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.17283593
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: 100
Tasks: 1

Channels & sampling rate

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

API Reference#

Use the NM000105 class to access this dataset programmatically.

class eegdash.dataset.NM000105(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset nm000105. Modality: emg; Experiment type: Motor; Subject type: Healthy. Subjects: 100; recordings: 100; 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/nm000105 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000105

Examples

>>> from eegdash.dataset import NM000105
>>> dataset = NM000105(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