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When the heart pumps blood out, arterial elastic fibers play a crucial role in sustaining a high-pressure gradient. They expand to accommodate the received blood and then recoil - a process known as the pulse that can be either manually palpated or electronically quantified. Despite a reduction in its effect with increased distance from the heart, elements of the pulse's systolic and diastolic components persist, observable even at the arteriole level.
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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
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Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
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ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

Wei Wu1,2,3,4, Corey J Keller1,2,3, Nigel C Rogasch5

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, 94305.

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We developed an automated algorithm to remove stimulation artifacts from TMS-EEG data. This method accurately cleans neural signals, improving brain dynamics research.

Keywords:
artifact rejectionelectroencephalogramtranscranial magnetic stimulation

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Concurrent single-pulse transcranial magnetic stimulation-electroencephalography (spTMS-EEG) is a powerful noninvasive technique for studying causal human brain dynamics.
  • spTMS-EEG data are heavily contaminated by stimulation artifacts, necessitating artifact rejection for accurate neural signal analysis.
  • Current artifact rejection methods are often manual, time-consuming, and subjective.

Purpose of the Study:

  • To develop and validate a fully automated algorithm for artifact rejection in spTMS-EEG data.
  • To improve the efficiency, objectivity, and accuracy of spTMS-EEG data processing.
  • To enhance the utility of spTMS-EEG in clinical and basic neuroscience research.

Main Methods:

  • Decomposition of spTMS-EEG data into statistically independent components (ICs).
  • Training a pattern classifier to identify artifact ICs based on spatio-temporal profiles.
  • Validation against expert manual artifact rejection across diverse datasets.

Main Results:

  • The automated algorithm achieved 95% accuracy in classifying artifact components compared to expert performance.
  • Autocleaned data produced group evoked potential waveforms qualitatively similar to hand-cleaned data.
  • The algorithm demonstrated high accuracy across various stimulation sites, subjects, populations, and montages.

Conclusions:

  • The developed algorithm offers an automated, fast, objective, and accurate solution for cleaning spTMS-EEG data.
  • This method significantly reduces the challenges associated with stimulation artifacts in TMS-EEG.
  • The automated approach can broaden the accessibility and application of TMS-EEG in neuroscience.