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

You might also read

Related Articles

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

Sort by

Same author

Prefrontal Speaker-Listener Neural Coupling Supports Speech-in-Noise Comprehension in Normal-Hearing Older Adults: An fNIRS Study.

The European journal of neuroscience·2026

Same author

"Awe-scillations": EEG spectral and complexity representations of awe.

bioRxiv : the preprint server for biology·2025

Same author

Utilizing Tympanic Membrane Temperature for Earphone-Based Emotion Recognition.

Sensors (Basel, Switzerland)·2025

Same author

A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding.

Scientific data·2025

Same author

Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts.

Sensors (Basel, Switzerland)·2025

Same author

Depression Recognition Using Daily Wearable-Derived Physiological Data.

Sensors (Basel, Switzerland)·2025

Same journal

A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals.

Scientific data·2026

Same journal

BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations.

Scientific data·2026

Same journal

Chromosomal-level genome assembly of Tetraponera attenuata (Hymenoptera: Formicidae).

Scientific data·2026

Same journal

High quality Chromosome-scale Genome Assembly of Phlebotomus perniciosus, a Vector of Zoonotic Leishmaniasis.

Scientific data·2026

Same journal

Characterisation Data of common pharmaceutical excipient Powders and Tablets for Formulation Development.

Scientific data·2026

Same journal

Chinese Electric Vehicle Policy Database: A Dataset of Policy Goals, Instruments, and Supply Chain Stages.

Scientific data·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 12, 2025

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

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

Published on: July 31, 2016

A Large Finer-grained Affective Computing EEG Dataset.

Jingjing Chen^1,2, Xiaobin Wang^1,2, Chen Huang^1,2

¹Dept. of Psychology, School of Social Sciences, Tsinghua University, Beijing, China.

Scientific Data

|October 25, 2023

Summary

This summary is machine-generated.

This study introduces the FACED dataset, featuring electroencephalogram (EEG) data from 123 subjects for affective computing. It enables robust emotion recognition across individuals, advancing human-computer interaction applications.

More Related Videos

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Published on: November 16, 2017

Related Experiment Videos

Last Updated: Jul 12, 2025

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

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

Published on: July 31, 2016

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Published on: November 16, 2017

Area of Science:

Neuroscience
Computer Science
Psychology

Background:

Affective computing utilizes electroencephalogram (EEG) for objective emotion measurement.
Existing EEG datasets often lack sufficient data on positive emotions and have small sample sizes, hindering cross-subject analysis.
There is a need for comprehensive EEG datasets that balance positive and negative emotions and support robust cross-subject affective computing.

Purpose of the Study:

To introduce the Finer-grained Affective Computing EEG Dataset (FACED) to address limitations in existing emotion-related EEG datasets.
To provide a large-scale, fine-grained, and balanced dataset for both positive and negative emotions.
To facilitate research in cross-subject affective computing using EEG signals.

Main Methods:

Recorded 32-channel EEG signals from 123 subjects.
Utilized 28 emotion-eliciting video clips categorized into nine distinct emotion types (amusement, inspiration, joy, tenderness, anger, fear, disgust, sadness, neutral).
Ensured a fine-grained and balanced emotional categorization across positive and negative valence.

Main Results:

Demonstrated effective recognition of emotion categories from EEG signals at both intra-subject and cross-subject levels.
Validated the utility of the FACED dataset for training and testing affective computing models.
Showcased the potential for accurate emotion classification with a balanced and diverse dataset.

Conclusions:

The FACED dataset provides a valuable resource for advancing EEG-based affective computing.
The dataset supports the development of more generalizable emotion recognition algorithms applicable across different individuals.
FACED is expected to significantly contribute to real-world applications of affective computing, particularly in human-computer interaction.