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

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

908
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
908
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
Ion Channels01:19

Ion Channels

91.4K
The movement of ions like sodium, potassium, and calcium into and out of the cell is essential to maintain the electrochemical gradient in living cells. The ion channels—a class of membrane transport proteins—help maintain this ionic gradient for the smooth functioning of physiological activities such as maintaining cell size and volume, conducting nerve impulses, and gas and nutrient exchange.
Ion channels are specialized integral membrane proteins on the plasma membrane that allow...
91.4K
Anatomical Movements00:51

Anatomical Movements

15.7K
Anatomical movements refer to the various actions or motions that can be performed by the body's joints and muscles. These movements are described using specific terms to provide a standardized way of discussing and understanding the range of motion at different joints.
Here are some common anatomical movements:
Flexion and extension motions are in the sagittal (anterior–posterior) plane of motion. These movements take place at the shoulder, hip, elbow, knee, wrist,...
15.7K
Muscles of the Forearm that Move the Hand and Fingers01:16

Muscles of the Forearm that Move the Hand and Fingers

2.6K
The muscles of the forearm that move the wrist, hand, and digits are numerous and diverse. They can be classified into two groups based on their location and function — the anterior and posterior compartment muscles.
Anterior Compartment
The anterior compartment muscles originate from the humerus. They primarily function as flexors and are also known as flexor muscles. They typically insert on the carpals, metacarpals, and phalanges. The superficial layer includes the flexor carpi...
2.6K
Transformers01:26

Transformers

1.8K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.8K

You might also read

Related Articles

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

Sort by
Same author

A Speech-Segregation Algorithm for Spatial Hearing Aids to Operate With Multiple Sound Sources.

Journal of speech, language, and hearing research : JSLHR·2026
Same author

Large Peritoneal Macrophages Promote the Resolution of Inflammation in Injured Endometrium.

Inflammation·2025
Same author

Development and validation of deep learning- and ensemble learning-based biological ages in the NHANES study.

Frontiers in aging neuroscience·2025
Same author

Environmental Impact of Online Versus in-Person Critical Care Education Through the Carbon Footprint Analysis of the CERTAIN Program: Cross-Sectional Study.

JMIR formative research·2025
Same author

Association of altitude and eosinophil with the one-year mortality of acute exacerbation of chronic obstructive pulmonary disease: a cohort study.

BMC pulmonary medicine·2025
Same author

Magnoflorine alleviates colitis-induced anxiety-like behaviors by regulating gut microbiota and microglia-mediated neuroinflammation.

Microbiome·2025

Related Experiment Video

Updated: Feb 3, 2026

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
12:07

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Published on: July 29, 2009

18.4K

Continuous Wavelet Transform for Decoding Finger Movements From Single-Channel EEG.

Jacob B Salyers, Yue Dong, Yan Gai

    IEEE Transactions on Bio-Medical Engineering
    |October 19, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Decoding finger movements using electroencephalography (EEG) is improved by analyzing slow cortical potentials (SCPs) and sensory motor rhythms (Mu waves) with time-frequency analysis, revealing complementary information for brain-computer interfaces.

    More Related Videos

    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
    08:08

    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

    Published on: May 10, 2017

    15.2K
    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    44.1K

    Related Experiment Videos

    Last Updated: Feb 3, 2026

    Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
    12:07

    Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

    Published on: July 29, 2009

    18.4K
    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
    08:08

    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

    Published on: May 10, 2017

    15.2K
    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    44.1K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Human body movements generate brain signals detectable via electroencephalography (EEG).
    • Key motor-related signals include sensory motor rhythms (Mu waves, 8-13 Hz) and slow cortical potentials (SCPs, 0.1-5 Hz).
    • Decoding individual finger movements from EEG is crucial for advanced brain-computer interfaces.

    Purpose of the Study:

    • To compare the efficacy of Mu waves and SCPs in decoding individual finger movements.
    • To investigate the utility of time-frequency analysis for enhancing EEG-based movement decoding.
    • To determine if SCPs and Mu waves provide complementary information for finger movement classification.

    Main Methods:

    • Participants performed individual finger lifts of the right hand.
    • EEG was recorded using a bipolar montage, with electromyograms (EMG) for movement onset detection.
    • Linear Discriminant Analysis (LDA) and Continuous Wavelet Transform (CWT) were applied to analyze SCPs and Mu waves, followed by template matching.

    Main Results:

    • LDA achieved significant decoding with SCPs but not Mu waves.
    • CWT analysis of both SCPs and Mu waves, preserving phase information, yielded significant classification accuracies (>50%).
    • Index finger decoding was better with Mu waves, while ring finger decoding was superior with SCPs; CWT outperformed LDA for thumb decoding.

    Conclusions:

    • Time-frequency characteristics of single-channel EEG, particularly when phase is preserved, contain critical information for decoding finger movements.
    • SCPs and Mu waves offer independent yet complementary data for finger movement decoding.
    • Advanced signal processing techniques like CWT enhance the decoding capabilities of EEG for fine motor control.