Jove
Visualize
Contact Us

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

Exploring the interaction of APOE-ε4and PICALM rs3851179 with dynamic functional connectivity in healthy middle-aged adults at risk for Alzheimer's disease.

Journal of neural engineering·2026
Same author

Evaluating the Impact of Demographic Factors on Subject-Independent EEG-Based Emotion Recognition Approaches.

Diagnostics (Basel, Switzerland)·2026
Same author

Fractal Dimension of Resting-State EEG as a Biomarker for Autonomous Sensory Meridian Response (ASMR).

IEEE journal of biomedical and health informatics·2025
Same author

Using Machine Learning to Model EEG-Derived Brain Activity During Emotion Regulation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

A Novel Approach for the Early Identification of Genetic Risk Factors for Alzheimer's Disease Using EEG and Psychometric Data.

IEEE journal of biomedical and health informatics·2025
Same author

Applied machine learning for nociceptive pain detection using EEG spectral features.

Biomedical physics & engineering express·2025
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles
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: May 24, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K

Dataset-Independent EEG Channel Selection for Emotion Recognition.

Shyamal Y Dharia, Sergio G Camorlinga, Camilo E Valderrama

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a dataset-independent method for selecting electroencephalography (EEG) channels for emotion recognition. This approach enhances the efficiency of EEG devices for monitoring emotions and neurological conditions.

    More Related Videos

    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
    08:31

    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

    Published on: July 31, 2016

    13.0K
    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
    05:51

    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

    Published on: May 15, 2016

    8.9K

    Related Experiment Videos

    Last Updated: May 24, 2025

    Cortical Source Analysis of High-Density EEG Recordings in Children
    09:32

    Cortical Source Analysis of High-Density EEG Recordings in Children

    Published on: June 30, 2014

    21.3K
    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
    08:31

    Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

    Published on: July 31, 2016

    13.0K
    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
    05:51

    Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

    Published on: May 15, 2016

    8.9K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Electroencephalography (EEG) is a noninvasive, cost-effective technique for recording neural activity.
    • EEG has potential applications in identifying neural processes related to human emotions.
    • Effective EEG channel selection is crucial for developing practical emotion recognition systems.

    Purpose of the Study:

    • To investigate the transferability and generalizability of EEG channel selection for emotion recognition.
    • To develop a dataset-independent channel selection method.
    • To improve the efficiency of EEG devices for emotion monitoring and neurological disease applications.

    Main Methods:

    • Utilized Power Spectral Density (PSD) to identify high-contributing EEG channels in the SEED V dataset.
    • Validated the channel selection approach on the independent SEED IV dataset.
    • Employed a Convolutional Neural Network (CNN) model for classification and tested with varying numbers of channels and Differential Entropy (DE) features.

    Main Results:

    • Achieved classification accuracies of up to 77.02% with 62 EEG channels and 310 DE features.
    • Demonstrated the method's effectiveness in eliminating insignificant EEG channels.
    • Evaluated the approach's sensitivity to Gaussian noise, showing robustness.

    Conclusions:

    • The proposed dataset-independent EEG channel selection method is effective for emotion recognition.
    • This approach can lead to more efficient EEG devices for daily emotion monitoring and clinical applications.
    • The method shows promise in improving the generalizability of EEG-based emotion recognition across different datasets.