Embracing the Future to Open New Frontiers

First EMG decomposition with needle electrode

1982

First EMG decomposition with quadrifilar needle electrode

dEMG System: First non-invasive surface EMG decomposition

2006

dEMG System: First non-invasive surface EMG decomposition

Trigno™ Galileo: First wireless EMG decomposition system

2018

Trigno™ Galileo: First wireless EMG decomposition system

Trigno™ Maize: First wireless EMG grid

COMING SOON

Trigno™ Maize: First wireless grid for EMG decomposition

Ask Us A Question

Ask our R&D team about validation, technical details, and practical use of our dEMG technology.

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Frequently Asked Questions

Read common inquiries we’ve received about our dEMG technology.

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Our Approach

See why our approach to EMG decomposition is superior to other approaches.

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Clinical Examples

Watch how easily our new Galileo Sensor can be used in a practical setting.

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Your Technical Questions

Ask our R&D team about validation, technical details, and practical use of our dEMG technology.

Frequently Asked Questions

Each Galileo Sensor occupies 4 of the 16 available channels of your Trigno system, therefore you can use up to 4 total Galileo Sensors at a time per system. If you’re using less than 4 Galileo Sensors, you can use any other Trigno sensor to occupy any remaining available channels. For instance, you could use 2 Galileo Sensors (occupying 8 channels) + 8 additional Trigno Avanti Sensors.
For the user, easy; simply load your EMG files and click the “Run Decomposition” button. Your data will process automatically.

Behind this button, however, are thousands of processes and decades of effort. EMG decomposition is a highly challenging problem which we have tackled through innovative research and exhaustive engineering. We have created this technology so that researchers in the movement sciences can do what they do best; explore the blueprint behind human movement.

We use pattern-recognition and template-based analysis approaches to identify individual motor unit action potentials within the EMG signal. Our approach is data-driven, meaning it makes no assumptions about motor unit firing behavior or action potential shape. You can read more about our approach in the Methodology section, and in our 2015 publication.
Yes! Galileo Analytics software is designed to walk you through the basic steps of understanding motor unit data. It includes tutorials, guided tours of the software, and descriptions of the visualization and analysis options. Advanced users can jump right into the statistical and regression analysis options and start creating + exporting figures.
Yes! Our dEMG technology has a long history of validation throughout development, including independent verification; validation is also performed automatically each time an EMG signal is decomposed, to provide you with accuracy metrics for each identified motor unit.
Yes, motor unit behavior provides a fundamental perspective to understanding how muscles respond to exercise, clinical interventions, injury and disease. In fact, in just the past few years, motor unit studies have helped researchers better understand:

  • Muscle weakness in stroke patients
  • Mechanisms of muscle fatigue
  • Noninvasive biomarkers of muscle hypertrophy + atrophy
  • Motor deficits with aging
  • Response to proprioceptive interventions for knee pain

Our Approach & Clinical Examples

Our EMG decomposition solution employs pattern-recognition and template-based analysis techniques to identify motor unit firings within the surface EMG signal.

Similar approaches are widely used throughout the biomedical field to detect cancer, image brain networks, measure human movement and more.

You can read more about our unique approach to EMG decomposition and its advantages on our website.

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Correlate the activation of knee flexors and extensors with power and recovery phases of cycling.

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Track changes in motor unit firing behavior across strength training repetitions in the upper-limb curls.

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Measure motor unit activation of the gastrocnemius during take-off and landing of plyometric jumps.

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Investigate relationships between motor unit firing properties and different phases of gait.

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Assess motor unit control of concentric and eccentric activity during lower-limb squats.

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