TESA user manual
  • Introduction
  • Installation, getting started, and reporting bugs
    • A quick intro to TESA
    • A quick intro to Matlab
    • A quick intro to EEGLAB
    • Reporting bugs
  • Overview of TMS-EEG analysis
  • Find and mark TMS pulses
    • Find TMS pulses
    • Find TMS pulses (alternative)
    • Fix TMS pulse latencies
  • Remove and interpolate TMS pulse artifacts
    • Remove TMS pulse artifact
    • Interpolate removed data
  • Remove TMS-evoked muscle activity and other artifacts
    • FastICA
    • Component classification (TESA)
    • Plot and remove components
    • Enhanced deflation method (EDM)
    • PCA compression
    • PCA suppression
    • Detrend
    • SSP–SIR
    • SOUND
  • Filter data
    • Butterworth filter
    • Median filter
  • Analyse TMS-evoked potentials
    • Extract TEPs
    • Find and analyse TEP peaks
    • Output peak analysis
    • Output peak analysis (group)
  • Plot TMS-evoked potentials
    • Plot data
    • Plot data (group)
  • Example analysis pipelines
  • TESA functions under development
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  • TMS-evoked muscle artifacts
  • Other artifacts

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Remove TMS-evoked muscle activity and other artifacts

PreviousInterpolate removed dataNextFastICA

Last updated 4 years ago

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TMS-evoked muscle artifacts

Perhaps the most challenging issue with TMS-EEG research is recovering neural activity from the large TMS-evoked muscle artifact resulting from stimulation of scalp muscles. Ideally, these artifacts can be avoided by stimulating close to the mid-line away from scalp muscles. However, this is not always possible, especially if the region of interest for TMS stimulation is more lateral. The best methods for accurately recovering neural activity from such artifacts is currently an active area of research. TESA provides a collection of different methods for removing or minimising TMS-evoked muscle activity including: independent component analysis using the algorithm (we also provide a method for using rules to ), an alternative FastICA algorithm called the , and several ways of .

Example 1 - comparison of methods for removing TMS-evoked muscle activity. For FastICA, three components were removed, for EDM three components were removed, and for PCA suppression one component was removed.

Other artifacts

Example 2 - removal of additional artifacts using FastICA and automatic component selection. Of the 59 components, 3 residual TMS-evoked muscle activity artifacts, 3 eye blink/movement, 19 persistent muscle and 10 electrode noise components were removed.

In addition to TMS-evoked artifacts, several other artifacts which are common to regular EEG recordings also distort the TMS-evoked neural signal. Such artifacts include: eye blinks, eye movement, persistent muscle activity and electrode noise. with available in TESA can also be used to remove these artifacts.

FastICA
automatic component classification
FastICA
automatically classify artifact components
enhanced deflation method
principal component analysis
detrending the data