VETA

VETA (Visualize EMG TMS Analyze) is a MATLAB-based toolbox that facilitates the acquisition, visualization, and analysis of electromyography (EMG) data in combination with transcranial magnetic stimulation (TMS). This powerful, noninvasive approach is used to investigate corticospinal excitability in humans and animals.

The toolbox addresses several technical challenges associated with EMG-TMS data collection and analysis, providing a standardized study replication and comparison solution. VETA offers features unavailable in existing software toolboxes, such as the ability to perform online EMG data visualization and offline analysis while directly interfacing with peripheral EMG and TMS equipment.

Key features of VETA include:

1. Simultaneous recording of EMG, timed administration of TMS, and presentation of behavioral stimuli from a single computer.
2. Real-time EMG data visualization during acquisition.
3. Automated offline processing and analysis of EMG data.
4. Interactive data visualization for streamlined analysis.
5. A standard EMG data format to facilitate data sharing and open science.

Topic

Zoology;Neurobiology;Data acquisition

Detail

  • Operation: Visualisation

  • Software interface: Command-line user interface

  • Language: MATLAB

  • License: Not stated

  • Cost: Free of charge

  • Version name: v1.0-alpha

  • Credit: The NIH National Center for Advancing Translational Science.

  • Input: -

  • Output: -

  • Contact: Ian Greenhouse img@uoregon.edu

  • Collection: -

  • Maturity: -

Publications

  • VETA: An Open-Source Matlab-Based Toolbox for the Collection and Analysis of Electromyography Combined With Transcranial Magnetic Stimulation.
  • Jackson N and Greenhouse I. VETA: An Open-Source Matlab-Based Toolbox for the Collection and Analysis of Electromyography Combined With Transcranial Magnetic Stimulation. VETA: An Open-Source Matlab-Based Toolbox for the Collection and Analysis of Electromyography Combined With Transcranial Magnetic Stimulation. 2019; 13:975. doi: 10.3389/fnins.2019.00975
  • https://doi.org/10.3389/FNINS.2019.00975
  • PMID: 31572120
  • PMC: PMC6753167

Download and documentation


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