wrist: Wrist Movement Control from EMG

Overview

Dataset: wrist - Wrist posture and movement from wrist-based surface electromyography Task: 1D continuous cursor control via wrist flexion/extension 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 wrist movements for continuous cursor control. Motion capture provides ground-truth wrist angles. The goal is to enable gesture-free control through wrist posture alone, demonstrating sEMG's ability to decode motor intent before visible movement occurs.

Dataset Details

Participants

• Sample size: 100 participants
• Demographics: Not available (marked as n/a)
• Recording side: Dominant wrist
• Sessions: 1 per participant

Hardware

• Device: sEMG-RD (single wristband)
• Channels: 16 (EMG0-EMG15)
• Sampling rate: 2000 Hz
• Reference: Bipolar differential
• Ground truth: Motion capture wrist angles

Recording Protocol

1. Participant wears sEMG-RD on dominant wrist
2. Motion capture tracks wrist angles in real-time
3. Participant controls horizontal cursor position with wrist flexion/extension
4. Target acquisition task: Navigate to targets and hold for 500ms

Data Contents

Files per Session

sub-XXX/ses-XXX/emg/
├── sub-XXX_ses-XXX_task-wrist_emg.edf
├── sub-XXX_ses-XXX_task-wrist_emg.json
├── sub-XXX_ses-XXX_task-wrist_channels.tsv
├── sub-XXX_ses-XXX_task-wrist_events.tsv
└── sub-XXX_ses-XXX_electrodes.tsv

Events

• Stage boundaries: Task phases and movement trials

Coordinate System

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

Signal Processing

Note: This dataset has significant data quality issues:

• Duplicate timestamps found in many sessions (up to 88% duplicates)
• Irregular sampling requiring resampling (up to 916% deviation)
• Post-processing: Duplicate removal followed by resampling to regular 2000 Hz

Baseline Performance

Published Results (Kaifosh et al., 2025)

Offline Evaluation:

• Wrist angle velocity error: <13°/s
• Error decreases with more training participants

Closed-loop Performance (n=17 naive test users):

• Target acquisition time: Median 1.51s (sEMG decoder)
• Dial-in time: Time to re-acquire after premature exit
• Learning effects: Improvement from practice to evaluation blocks

Comparison:

• Motion capture ground truth: 0.96s
• MacBook trackpad: 0.68s
• sEMG decoder: 1.51s (2.2× slower than trackpad)

Model architecture: MPF features + LSTM

Key Findings

• Predictive signals: sEMG precedes movement by tens of milliseconds
• Generic models work: Out-of-the-box cross-user generalization
• Continuous control: Demonstrates feasibility of gesture-free interfaces
• Room for improvement: Performance gap vs traditional inputs

Use Cases

• Continuous control: Cursor/pointer movement
• AR/VR navigation: Hands-free interface
• Low-effort control: Minimal visible movement required
• Predictive decoding: Intent detection before motion completion

Known Limitations

• Single degree of freedom (1D control only)
• Single wrist (dominant hand)
• Duplicate timestamps (data quality issue)
• Performance below traditional inputs
• Extension to 2D control not demonstrated

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

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