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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Related Experiment Video

Updated: Feb 23, 2026

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
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A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study.

Kun Wang1,2, Zhongpeng Wang1,2, Yi Guo1,2

  • 1Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.

Journal of Neuroengineering and Rehabilitation
|September 13, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new brain-computer interface (BCI) using motor imagery (MI) with varying hand-grip force loads to control multiple commands, achieving over 70% accuracy. This advances BCI technology for enhanced user interaction and potential rehabilitation applications.

Keywords:
Brain-computer Interface (BCI)Electroencephalogram (EEG)Event-related Desynchronization (ERD)Force loadMotor imagery

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Motor imagery (MI) based brain-computer interfaces (BCIs) commonly use electroencephalography (EEG) patterns as control signals.
  • Kinetic and kinematic factors influence EEG during motor tasks, but effective online MI-BCIs regulated by kinetic factors are not yet established.
  • This research introduces a novel MI-BCI paradigm utilizing imagined hand-clenching force loads to generate multiple commands.

Purpose of the Study:

  • To investigate the feasibility of an online MI-BCI system controlled by varying force loads applied to the same limb.
  • To demonstrate that different imagined force loads can generate distinct EEG patterns for BCI control.
  • To expand the command set capabilities of MI-BCIs.

Main Methods:

  • Eleven subjects participated in online experiments, imagining clenching their right hand with 30% and 10% maximum voluntary contraction (MVC) force loads.
  • Multi-Common Spatial Patterns (Multi-CSPs) and Support Vector Machines (SVMs) were employed for classifying three commands: high load MI, low load MI, and relaxed state.
  • Electromyography (EMG) monitored to prevent voluntary muscle activity; Event-Related Spectral Perturbation (ERSP) analyzed EEG variations.

Main Results:

  • All subjects successfully operated the BCI system using motor imagery with different force loads.
  • An average online accuracy of 70.9% was achieved, with the highest accuracy reaching 83.3%, significantly surpassing the chance level (33%).
  • Higher intensity and spatial distribution of event-related desynchronization (ERD) were observed during high-load MI compared to low-load MI at electrode C3 (p < 0.05).

Conclusions:

  • The study validates a novel MI-BCI paradigm based on multi-force loads on a single limb.
  • This approach effectively expands the command repertoire for MI-BCIs.
  • The paradigm shows promise for applications in neurorehabilitation for individuals with motor disabilities.