Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Upsampling01:22

Upsampling

740
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
740
Downsampling01:20

Downsampling

858
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
858
Aliasing01:18

Aliasing

923
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
923
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

905
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...
905
Sampling Theorem01:15

Sampling Theorem

1.7K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.7K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

905
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
905

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fragile memories for fleeting percepts.

Consciousness and cognition·2026
Same author

Dynamic excitatory-inhibitory differences in autistic and non-autistic adults: Evidence from a pharmacological challenge with arbaclofen.

Journal of psychopharmacology (Oxford, England)·2026
Same author

Breakthrough percepts of familiar faces.

Brain informatics·2026
Same author

Reframing the Expected Free Energy: Four Formulations and a Unification.

Neural computation·2026
Same author

Cutting-edge optimized multi-source data fusion for trusted execution and management of blockchain transactions on the internet of medical things (IoMT) with machine learning.

Scientific reports·2025
Same author

Headache-specific hyperexcitation sensitises and habituates on different time scales: An event related potential study of pattern-glare.

Neuroimage. Reports·2025
Same journal

Neural Sensitivity to Conversational Inter-Speaker Gaps in the Broad Autism Phenotype.

Psychophysiology·2026
Same journal

Open Communication Can Lead to Equivalent EEG Data Quality for Black Women: Multilevel Modeling Interindividual Differences on Emotional Scene and Face Perception.

Psychophysiology·2026
Same journal

What's in a Mean? Comparing Interbeat Interval Averaging Methods Across Variability Levels and Window Lengths.

Psychophysiology·2026
Same journal

Model-Free and Model-Based Learning in Human Fear Conditioning.

Psychophysiology·2026
Same journal

Examining the Impact of Acute Exercise and Arousal Reappraisal on Stressor-Evoked Psychological and Cardiovascular Responses.

Psychophysiology·2026
Same journal

Respiratory Sinus Arrhythmia and Hierarchical Dimensions of Psychopathology.

Psychophysiology·2026
See all related articles

Related Experiment Video

Updated: Apr 22, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.3K

Resampling the peak, some dos and don'ts.

Alexia Zoumpoulaki1, Abdulmajeed Alsufyani, Howard Bowman

  • 1School of Computing, University of Kent, Canterbury, Kent, UK.

Psychophysiology
|October 14, 2014
PubMed
Summary
This summary is machine-generated.

Bootstrap resampling methods may introduce statistical bias when analyzing event-related potentials (ERPs), particularly in deception detection. Permutation tests offer a more statistically sound alternative for significance testing in ERP research.

Keywords:
BootstrapDeception detectionEEG/ERPPeakPermutationResampling techniquesSignificance testing

More Related Videos

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.8K
Sampling and Analysis of Animal Scent Signals
14:59

Sampling and Analysis of Animal Scent Signals

Published on: February 13, 2021

4.6K

Related Experiment Videos

Last Updated: Apr 22, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.3K
Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.8K
Sampling and Analysis of Animal Scent Signals
14:59

Sampling and Analysis of Animal Scent Signals

Published on: February 13, 2021

4.6K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Psychology
  • Statistics

Background:

  • Resampling techniques are prevalent in the event-related potential (ERP) research community for statistical significance assessment.
  • These methods are particularly common in the specialized field of deception detection using neurophysiological measures.
  • Existing practices often employ techniques like bootstrapping without fully addressing potential statistical biases.

Purpose of the Study:

  • To critically evaluate the suitability of bootstrap resampling in conjunction with specific ERP analysis methods, such as peak-to-peak measurements.
  • To identify and explain the statistical biases inherent in using bootstrap with certain ERP analysis techniques.
  • To propose and advocate for a more appropriate statistical alternative for significance testing in ERP research.

Main Methods:

  • The study focuses on a theoretical critique of statistical methodologies rather than empirical data collection.
  • It involves analyzing the statistical properties of bootstrap resampling when applied to ERP data, specifically concerning peak-to-peak amplitude analysis.
  • Comparison of bootstrap methods with permutation tests in the context of ERP significance testing.

Main Results:

  • Bootstrap resampling, when combined with methods like peak-to-peak analysis in ERP studies, is shown to be susceptible to statistical bias.
  • This bias can potentially lead to inaccurate assessments of statistical significance.
  • Permutation tests are identified as a statistically more robust and appropriate method for significance testing in this context.

Conclusions:

  • The use of bootstrap resampling in combination with peak-to-peak measurements for ERP analysis is discouraged due to inherent statistical biases.
  • Permutation tests represent a superior alternative for assessing statistical significance in ERP research, including deception detection.
  • Researchers in the ERP community, especially in deception detection, should consider adopting permutation tests for more reliable statistical inferences.