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Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short distances...
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Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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Rectangular and Triangular Pulse Function01:19

Rectangular and Triangular Pulse Function

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Time-Domain Interpretation of PD Control01:07

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Related Experiment Video

Updated: Jun 5, 2026

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)
10:02

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

Recurrence-based estimation of time-distortion functions for ERP waveform reconstruction.

Matthias Ihrke1, Hecke Schrobsdorff, J Michael Herrmann

  • 1Bernstein Center for Computational Neuroscience Göttingen, Germany. ihrke@nld.ds.mpg.de

International Journal of Neural Systems
|January 19, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a novel algorithm to correct temporal distortions in event-related potential (ERP) data. The method enhances the accuracy of ERP signal analysis by aligning trial data effectively.

Related Experiment Videos

Last Updated: Jun 5, 2026

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)
10:02

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Event-related potential (ERP) research often faces challenges due to temporal distortions in repeated measurements.
  • Accurate alignment of ERP signals is crucial for reliable analysis of neural responses.

Purpose of the Study:

  • To introduce a new algorithm for compensating temporal distortions in ERP data.
  • To improve the precision of ERP signal analysis through advanced time-series methods.

Main Methods:

  • The algorithm combines nonlinear time-series analysis with cross-recurrence plots of phase-space representations.
  • It approximates the optimal multiple-alignment path using hierarchical cluster analysis.
  • Contextual information, such as reaction times, is integrated via a regularization scheme.

Main Results:

  • Simulations on artificial data demonstrated the algorithm's robustness.
  • Analysis of ERPs from a psychophysical study confirmed its applicability.
  • The method effectively guides warping functions using experimental context.

Conclusions:

  • The developed algorithm offers a robust and applicable solution for temporal distortion correction in ERP research.
  • This approach enhances the reliability and accuracy of ERP signal analysis.
  • The method's flexibility in parameter optimization allows for broad applicability across different datasets.