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Related Experiment Video

Updated: May 25, 2026

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment
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Published on: August 9, 2024

A non-orthogonal SVD-based decomposition for phase invariant error-related potential estimation.

Ronald Phlypo1, Nisrine Jrad, Sandra Rousseau

  • 1Vision and Brain Signal Processing Research Group at GIPSA-lab, Universities of Grenoble/CNRS UMR 5216, Grenoble, France.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

Estimating the Error Related Potential (ERP) is difficult due to its low amplitude and variable timing. This study introduces a novel Singular Value Decomposition method to accurately estimate ERPs, improving signal analysis.

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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
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Published on: May 25, 2019

Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • The Error Related Potential (ERP) is a crucial neural signal for understanding error processing.
  • Estimating ERPs is challenging due to their low amplitude and inconsistent latency relative to triggers.
  • Existing methods like simple averaging are susceptible to timing discrepancies, reducing accuracy.

Purpose of the Study:

  • To develop a robust method for estimating the Error Related Potential (ERP) waveform.
  • To address the challenge of variable waveform latencies in electroencephalographic (EEG) data.
  • To offer a novel framework for ERP estimation using Singular Value Decomposition (SVD).

Main Methods:

  • Proposed a new method to handle latency discrepancies in ERP waveforms.
  • Developed a framework reducing ERP estimation to Singular Value Decomposition (SVD).
  • Utilized an analytic waveform representation of the observed electroencephalographic signal.

Main Results:

  • The proposed SVD-based method effectively estimates the ERP waveform.
  • The approach successfully accounts for variations in ERP waveform latency.
  • This method explains a higher portion of the observed signal's variance with fewer components.

Conclusions:

  • The developed SVD framework provides a promising approach for accurate ERP estimation.
  • This method offers improved signal analysis by overcoming latency issues.
  • The technique enhances the understanding of neural processes related to errors.