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

Classification of Signals01:30

Classification of Signals

1.4K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.4K
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

938
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
938
Bacterial Transformation01:33

Bacterial Transformation

59.9K
In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
Griffith made an unexpected discovery when he killed the pathogenic strain and mixed its remains with the live, non-pathogenic strain. Not only did the mixture kill host mice, but it also contained living pathogenic bacteria that...
59.9K
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

1.7K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
1.7K
Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

4.2K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
4.2K
Transcription Factors02:16

Transcription Factors

82.7K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
82.7K

You might also read

Related Articles

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

Sort by
Same author

Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.

Scientific reports·2025
Same author

FedVGM: Enhancing Federated Learning Performance on Multi-Dataset Medical Images With XAI.

IEEE journal of biomedical and health informatics·2025
Same author

Cadmium toxicity, health risk and its remediation using low-cost biochar adsorbents.

Open life sciences·2025
Same author

Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance.

Scientific reports·2025
Same author

Development of neutrosophic cubic hesitant fuzzy exponential aggregation operators with application in environmental protection problems.

Scientific reports·2023
Same author

Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Feb 2, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.6K

A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification.

Hadi Ratham Al Ghayab1, Yan Li2, S Siuly3

  • 1Faculty of Health, Engineering and Sciences, University of Southern Queensland, QLD, 4350, Australia; College of Computer Sciences and Mathematics, University of Thi-Qar, 64001, Iraq.

Journal of Neuroscience Methods
|November 24, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using tunable Q-factor wavelet transform (TQWT) and statistical analysis for analyzing electroencephalogram (EEG) signals. The approach effectively extracts discriminative features from brain signals, aiding in the identification of diverse EEG categories.

Keywords:
ClassificationElectroencephalogram (EEG) signalEpilepsyTunable Q-factor wavelet transform

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

499
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.7K

Related Experiment Videos

Last Updated: Feb 2, 2026

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.6K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

499
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.7K

Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG) signals are crucial for brain health monitoring.
  • EEG signals exhibit complex characteristics like non-stationarity, aperiodicity, and nonlinearity.
  • Traditional linear approaches struggle with the transient and oscillatory nature of EEG signals.

Purpose of the Study:

  • To propose a novel scheme for analyzing EEG recordings using a tunable Q-factor wavelet transform (TQWT) and statistical feature extraction.
  • To evaluate the effectiveness of the proposed method in discriminating between different EEG signal categories.
  • To compare the performance of the proposed feature extraction technique with existing methods.

Main Methods:

  • Decomposition of EEG signals into sub-bands using TQWT, parameterized by Q-factor and redundancy.
  • Statistical feature extraction from segmented sub-bands.
  • Classification of extracted features using Bagging Tree (BT), k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM) algorithms.

Main Results:

  • The proposed feature extraction method combined with the k-NN classifier achieved the best performance on two EEG databases (Bonn University and Born University).
  • The TQWT-based feature extraction demonstrated significant potential in extracting discriminative information from brain signals.
  • Experimental results showed superior performance compared to other evaluated classifiers and existing methods in terms of accuracy.

Conclusions:

  • The developed TQWT-based feature extraction technique shows great potential for analyzing complex EEG signals.
  • The proposed method can assist healthcare professionals in identifying various EEG categories.
  • This approach offers a robust tool for brain health monitoring and diagnosis.