Jove
Visualize
Contact Us

Related Concept Videos

Brain Waves01:23

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

You might also read

Related Articles

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

Sort by
Same author

Ultrastructural changes in cryopreserved tracheal grafts of sprague-dawley rats.

ASAIO journal (American Society for Artificial Internal Organs : 1992)·2009
Same author

Facile synthesis of size-tunable micro-octahedra via metal-organic coordination.

Chemical communications (Cambridge, England)·2009
Same author

N-acetyl cysteine and penicillamine induce apoptosis via the ER stress response-signaling pathway.

Molecular carcinogenesis·2009
Same author

Targeting glucosylceramide synthase downregulates expression of the multidrug resistance gene MDR1 and sensitizes breast carcinoma cells to anticancer drugs.

Breast cancer research and treatment·2009
Same author

N-glycosylation of ATF6beta is essential for its proteolytic cleavage and transcriptional repressor function to ATF6alpha.

Journal of cellular biochemistry·2009
Same author

A humanized anti-osteopontin antibody inhibits breast cancer growth and metastasis in vivo.

Cancer immunology, immunotherapy : CII·2009
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 Video

Updated: Jun 8, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

[The continuous analysis of EEG's alpha wave by morlet wavelet transform].

Hao Wang1, Zhihua Chen, Safeng Zou

  • 1Softrare Technology Institute of Dalian Jiaotong University, Dalian 116028, China. iwonhoo@163.com

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|September 17, 2010
PubMed
Summary

This study demonstrates using Complex Morlet wavelets for analyzing electroencephalography (EEG) alpha wave energy and duration changes during mental arithmetic tasks.

More Related Videos

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits
10:25

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

Related Experiment Videos

Last Updated: Jun 8, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits
10:25

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

Area of Science:

  • Signal processing
  • Neuroscience
  • Wavelet analysis

Context:

  • Electroencephalography (EEG) signals are crucial for understanding brain activity.
  • Time-frequency analysis is essential for extracting meaningful features from EEG data.
  • Complex Morlet wavelets are a powerful tool for this analysis.

Purpose:

  • To detail the application of the Morlet wavelet transform for EEG analysis.
  • To illustrate the extraction of alpha wave energy distribution over time.
  • To investigate changes in alpha wave duration using EEG data from a mental arithmetic experiment.

Summary:

  • The Morlet wavelet transform is applied to EEG data to analyze the temporal distribution of alpha wave energy.
  • The study utilizes EEG recordings from a mental arithmetic task to quantify alterations in alpha wave duration.
  • This approach provides insights into the dynamic changes of alpha wave activity during cognitive tasks.

Impact:

  • Provides a methodological framework for analyzing EEG alpha wave dynamics.
  • Enhances understanding of brain activity patterns during cognitive load.
  • Offers a quantitative approach to studying neural oscillations and their temporal characteristics.