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

You might also read

Related Articles

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

Sort by
Same author

Emerging claudin-18.2 antagonists in the treatment of biliary tract cancer.

Expert opinion on emerging drugs·2026
Same author

Efficacy and Safety of CSF Shunting for Idiopathic Normal Pressure Hydrocephalus: A Systematic Review and Meta-Analysis.

Cureus·2026
Same author

SkinECG: An orthogonal remote powering wearable skin-like sensor.

Science advances·2026
Same author

Clinical Predictors of Drug-Resistant Epilepsy in Adults: An Analytical Observational Study.

Cureus·2026
Same author

Explicitly modeling genetic ancestry to improve polygenic prediction accuracy for height in a large, admixed cohort of US Latinos: Findings from HCHS/SOL.

HGG advances·2026
Same author

Enlarged Perivascular Spaces Among Hispanic and Latino Adults in SOL-INCA-MRI.

Journal of the American Heart Association·2026

Related Experiment Video

Updated: Nov 2, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.5K

Hardware Acceleration of EEG-Based Emotion Classification Systems: A Comprehensive Survey.

Hector Gonzalez, Richard George, Shahzad Muzaffar

    IEEE Transactions on Biomedical Circuits and Systems
    |June 14, 2021
    PubMed
    Summary

    Wearable emotion classifiers using electroencephalography (EEG) show promise for monitoring neurological disorders like ALS and Alzheimer's. This review critically examines their hardware, identifying research opportunities for improved neuro-medicine applications.

    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

    14.0K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.6K

    Related Experiment Videos

    Last Updated: Nov 2, 2025

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

    Published on: April 26, 2024

    2.5K
    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

    14.0K
    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    4.6K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Computer Science

    Background:

    • Growing interest in electroencephalography (EEG)-based wearable emotion classifiers for real-time patient monitoring.
    • Potential applications in neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), and Alzheimer's disease.
    • Need for improved healthcare outcomes and social integration for patients through emotion classification technology.

    Purpose of the Study:

    • To present the first hardware-focused critical review of EEG-based wearable emotion classifiers.
    • To survey implementation perspectives, algorithmic foundations, and feature extraction methodologies.
    • To provide a neuroscience-based analysis of current hardware accelerators for emotion classifiers.

    Main Methods:

    • Critical review of existing hardware platforms for EEG-based emotion classification.
    • Survey of implementation perspectives, algorithms, and feature extraction techniques.
    • Neuroscience-based analysis of hardware accelerators and identification of research gaps.

    Main Results:

    • Identified gaps in current hardware platforms for emotion classification in healthcare.
    • Surveyed diverse approaches to implementation, algorithms, and feature extraction.
    • Provided a neuroscience-based analysis highlighting areas for hardware accelerator development.

    Conclusions:

    • Several research opportunities exist for advancing EEG-based wearable emotion classifiers.
    • Future directions include multi-modal hardware platforms and robust accelerators.
    • Development of pre-processing libraries for universal EEG datasets is crucial.