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

Search research articles

Related Experiment Video

Updated: May 25, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Towards wireless emotional valence detection from EEG.

Lindsay Brown¹, Bernard Grundlehner, Julien Penders

¹imec / Holst Centre, Eindhoven, the Netherlands. lindsay.brown@imec-nl.nl

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

|January 19, 2012

Summary

Researchers developed a wireless electroencephalography (EEG) system to monitor emotional valence. This affective computing approach achieved 82% accuracy in classifying positive, negative, and neutral emotions from brain activity, enabling real-world applications.

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

Entrapped in the Cocoon: A Rare Case of Sclerosing Encapsulating Peritonitis in a Patient on Dialysis.

Cureus·2025

Same author

Mutations in RNU4ATAC Are Associated With Chilblain-Like Lesions and Enhanced Type I Interferon Signalling.

European journal of immunology·2025

Same author

Plant-Based Functional Foods from Borneo.

Nutrients·2025

Same author

Impact of HIV exposure without infection on hospital course and mortality among young children in sub-Saharan Africa: a multi-site cohort study.

BMC medicine·2024

Same author

Improving Undernutrition with Microalgae.

Nutrients·2024

Same author

Dual white matter pathology in fetal holoprosencephaly featuring concurrent malformative and destructive features: A case series.

Journal of neuropathology and experimental neurology·2024

Area of Science:

Affective computing
Biomedical engineering
Neuroscience

Background:

Current affective computers have limited transferability and monitor single physiological emotion aspects.
Real-world applications require versatile emotion monitoring systems.

Purpose of the Study:

To investigate the potential of wireless electroencephalography (EEG) for monitoring emotional valence in real-life situations.
To develop an affective computing system with improved transferability and multi-aspect emotion monitoring.

Main Methods:

Utilized a wireless EEG system integrated into a body area network.
Analyzed EEG signals during film clip viewing to capture emotional responses.
Extracted features from both EEG frequency content and temporal changes.

Related Experiment Videos

Last Updated: May 25, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Main Results:

Achieved 82% accuracy in automatically classifying emotional valence (positive, negative, neutral).
Demonstrated the feasibility of using EEG for real-time emotion detection.
Identified specific EEG features indicative of emotional states.

Conclusions:

Wireless EEG systems show significant potential for real-world affective computing applications.
The developed system offers a promising approach for monitoring emotional valence with high accuracy.
Future research can expand this methodology for diverse affective computing scenarios.