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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Related Experiment Video

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Cortical potential imaging using L-curve and GCV method to choose the regularisation parameter.

Narayan P Subramaniyam1, Outi Rm Väisänen, Katrina E Wendel

  • 1Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland. narayan.puthanmadamsubramaniyam@tut.fi.

Nonlinear Biomedical Physics
|June 5, 2010
PubMed
Summary
This summary is machine-generated.

Generalised cross validation (GCV) is a robust method for improving electroencephalography (EEG) spatial resolution. This study demonstrates its effectiveness in cortical potential imaging for visually evoked potential (VEP) data.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalography (EEG) offers excellent temporal resolution but suffers from poor spatial resolution due to the skull's low conductivity.
  • Reconstructing brain activity from scalp EEG is challenging but crucial for understanding neural processes.
  • Visually Evoked Potential (VEP) data, specifically the N170 component, is utilized to assess brain responses to visual stimuli.

Purpose of the Study:

  • To compute the potential distribution over the cortex from EEG scalp potentials.
  • To compare L-curve and Generalized Cross-Validation (GCV) methods for determining the regularization parameter.
  • To evaluate the feasibility of these techniques for N170 component analysis in VEP data.

Main Methods:

  • A finite difference method (FDM) head model was created using the Visible Human Man (VHM) dataset.
  • A forward transfer matrix was established to relate cortical potential to scalp potential.
  • Tikhonov regularization was applied to obtain the cortical potential distribution.

Main Results:

  • Cortical potential distributions were successfully computed for three subjects using both L-curve and GCV methods.
  • A total of 18 cortical potential distributions were generated, corresponding to three stimuli (fearful face, neutral face, control objects) per subject.
  • The GCV method demonstrated superior robustness in identifying the optimal regularization parameter compared to the L-curve method.

Conclusions:

  • The Generalized Cross-Validation (GCV) method is more robust than the L-curve for selecting the optimal regularization parameter.
  • Cortical potential imaging is a reliable technique for reconstructing cortical potential distributions from VEP data.
  • This approach enhances the spatial resolution of EEG, providing more accurate insights into brain activity.