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 Experiment Videos

A robust statistical framework for instantaneous electroencephalogram phase and frequency estimation and analysis.

Reza Sameni, Esmaeil Seraj

    Physiological Measurement
    |October 17, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

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

    Sort by
    Same author

    Automatic registration of generic bone models for X-ray-based biomechanics: Application to 3D rat hindlimb kinematics and sex-specific gait patterns.

    Computers in biology and medicine·2026
    Same author

    Bidirectional Translation Between ECG and PCG.

    ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics·2026
    Same author

    The Harvard-Emory ECG Database.

    Scientific data·2026
    Same author

    Real-time quality feedback on Doppler data for community midwives using edge-AI.

    Machine Learning. Health·2025
    Same author

    Estimating blood pressure from the electrocardiogram: findings of a large-scale negative results study.

    Physiological measurement·2025
    Same author

    Corrigendum to "Decoding depression: Event related potential dynamics and predictive neural signatures of depression severity" [J. Affect. Disord. (2025): 119893].

    Journal of affective disorders·2025
    Same journal

    Continuous tracking of aortic aneurysm diameter with peripheral pulse waves: a computational framework combining sequential Markov chain Monte Carlo with Kalman filtering.

    Physiological measurement·2026
    Same journal

    The 2026 global roadmap for textile-integrated wearable technologies in health.

    Physiological measurement·2026
    Same journal

    Augmenting single-lead ECG interpretation through QRS waveform decomposition and rotation.

    Physiological measurement·2026
    Same journal

    Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

    Physiological measurement·2026
    Same journal

    Dual warm-start fusion versus attention-based fusion in low-label ECG-PCG classification: a controlled ablation study.

    Physiological measurement·2026
    Same journal

    Inter-patient multi-label ECG classification via low-rank adaptation fine-tuned large language models with dynamic graph convolutional network.

    Physiological measurement·2026
    See all related articles

    This study introduces a robust statistical framework for estimating instantaneous phase and frequency in electroencephalogram (EEG) signals. The new method improves accuracy, especially in low signal amplitudes, by accounting for the instantaneous envelope and providing confidence intervals.

    Area of Science:

    • Neuroscience
    • Signal Processing
    • Biomedical Engineering

    Background:

    • Electroencephalogram (EEG) instantaneous phase (IP) and frequency (IF) are crucial for spectral analysis.
    • Current IP/IF estimation methods rely on narrow-band filtering and analytical signal calculation.
    • These methods are sensitive to filter parameters and noise, particularly with low analytical signal amplitudes.

    Purpose of the Study:

    • To develop a robust statistical framework for accurate EEG IP and IF estimation and analysis.
    • To address the susceptibility of current methods to noise and parameter variations.
    • To enhance the reliability of phase and frequency interpretations in EEG signals.

    Main Methods:

    • Proposed a Monte Carlo estimation scheme for robust EEG IP and IF estimation.

    Related Experiment Videos

  • Incorporated infinitesimal variations in algorithmic parameters (filter bandwidth, center frequency, noise level) for confidence intervals.
  • Derived analytical probability density functions for instantaneous envelope (IE) and IP using a stochastic EEG model.

    • Main Results:

    • Demonstrated the critical impact of the instantaneous analytical envelope (IE) on EEG phase content accuracy.
    • Rigorously showed that IP/IF estimation quality is highly dependent on IE.
    • Established that phase/frequency interpretations in low IE are statistically unreliable and necessitate hypothesis testing.

    Conclusions:

    • The proposed method has significant implications for prior studies using time-domain phase synchrony, phase resetting, phase locking value, and phase amplitude coupling.
    • Findings suggest new standards for EEG phase and frequency estimation and analysis techniques.
    • The robust framework enhances the reliability and interpretability of EEG phase and frequency dynamics.