Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

ECG Interpretation of Rhythms

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....

Labeling Emotion

Labeling Emotion

Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...

You might also read

Related Articles

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

Sort by

Same author

Monitoring Health Status: Development and Preliminary Validation of a Personal Health Index Using the International Classification of Functioning, Disability and Health.

JMIR human factors·2026

Same author

Internal rotation in the 3rd position as a candidate marker for multi-year shoulder ROM monitoring in collegiate pitchers: a 4-year individual follow-up of six clinical measures.

BMC sports science, medicine & rehabilitation·2026

Same author

Workplace use and outcomes of the dynamic orthosis for lateral epicondylitis: a comparative cohort study.

JSES international·2026

Same author

Prediction of an fMRI-based schizophrenia biomarker from EEG using dynamic functional connectivity: a simultaneous EEG-fMRI study.

Biomedical physics & engineering express·2026

Same author

Effects of combining forearm postexercise muscle ischemia with inspiratory loading on cardiovascular and muscle oxygenation responses.

European journal of applied physiology·2026

Same author

Humero-ulnar external fixation of pediatric supracondylar humeral fractures using an adult wrist external fixator.

Journal of orthopaedic science : official journal of the Japanese Orthopaedic Association·2026

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

Search research articles

Related Experiment Video

Updated: Feb 20, 2026

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

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

Published on: April 26, 2024

Decoding emotional valence from electroencephalographic rhythmic activity.

Hande Celikkanat, Hiroki Moriya, Takeshi Ogawa

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 25, 2017

Summary

This summary is machine-generated.

We can decode emotional valence from brainwaves using a data-driven approach. Parietal brain sources are key, though individual differences in emotional processing exist.

More Related Videos

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Feb 20, 2026

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

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

Published on: April 26, 2024

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

Published on: January 11, 2020

Area of Science:

Neuroscience
Cognitive Science
Psychology

Background:

Decoding emotional valence from brain activity is crucial for understanding human affect.
Electroencephalography (EEG) offers a non-invasive method to study neural correlates of emotion.
Naturalistic settings provide more ecologically valid data for emotion research.

Purpose of the Study:

To decode emotional valence from electroencephalographic (EEG) rhythmic activity in a naturalistic setting.
To identify specific neuronal sources involved in processing emotional valence.
To investigate individual differences in emotional processing using EEG.

Main Methods:

Employed Spectral Linear Discriminant Analysis (SLDA), a data-driven method, to analyze EEG data.
Utilized multiple frequency bands to optimally capture relationships between brain activity and emotional valence.
Investigated independent neuronal sources and their spatial locations across subjects.

Main Results:

Identified consistent involvement of specific neuronal sources, particularly in the parietal lobe, across subjects for emotional valence.
Demonstrated that while source locations are consistent, the degree of valence-related EEG activity varies significantly among individuals.
SLDA effectively decoded emotional valence by optimally utilizing multiple frequency bands.

Conclusions:

Parietal brain sources play a significant role in processing emotional valence.
Individual differences in the magnitude of neural activity within these sources impact emotional processing.
The findings highlight the potential of EEG-based methods for understanding neural mechanisms of emotion.