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

Facial Feedback Hypothesis

Facial Feedback Hypothesis

Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...

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...

Physiology of Emotion

Physiology of Emotion

The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...

You might also read

Related Articles

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

Sort by

Same author

A Novel Stress State Assessment Method for College Students Based on EEG.

Computational intelligence and neuroscience·2022

Same author

A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals.

Journal of healthcare engineering·2021

Same author

Multimodal MRI Brain Tumor Image Segmentation Using Sparse Subspace Clustering Algorithm.

Computational and mathematical methods in medicine·2020

Same author

Feature selection for gene expression using model-based entropy.

IEEE/ACM transactions on computational biology and bioinformatics·2010

Same author

[Pulmonary function changes after operation in patients with severe scoliosis].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery·2010

Same author

[A comparative study on establishing rabbit intervertebral disc degeneration models by three methods].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery·2010

Search research articles

Related Experiment Video

Updated: Sep 20, 2025

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

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

Published on: December 15, 2023

A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural

Li Liu^1,2, Yunfeng Ji¹, Yun Gao¹

¹Jiangsu Vocational College of Information Technology, Wuxi, Jiangsu 214153, China.

Computational Intelligence and Neuroscience

|June 6, 2022

Summary

This study introduces a novel method for recognizing college students' emotions using electroencephalogram (EEG) signals. The approach achieves over 88% accuracy, offering an objective and efficient tool for student well-being management.

More Related Videos

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Related Experiment Videos

Last Updated: Sep 20, 2025

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

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

Published on: December 15, 2023

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Area of Science:

Neuroscience and Artificial Intelligence
Affective Computing and Machine Learning

Background:

College students face increasing academic, social, and personal pressures, leading to significant emotional fluctuations.
Traditional emotion assessment methods (surveys, interviews) are subjective, time-consuming, and lack data authenticity.
Objective physiological data, specifically electroencephalogram (EEG) signals, offer a more reliable measure of emotional states.

Purpose of the Study:

To develop an accurate and efficient method for recognizing college students' emotions using EEG signals.
To leverage deep neural networks (DNNs) for classifying EEG data and determining emotional states.
To enhance emotion recognition by employing feature extraction and fusion techniques.

Main Methods:

Collected electroencephalogram (EEG) data from college students to capture brain activity related to emotions.
Extracted various features from the EEG signals to comprehensively represent the data.
Utilized autosklearn for feature fusion, integrating multiple feature sets.
Employed a deep neural network (DNN) to classify the fused features and predict emotional states.

Main Results:

The proposed method demonstrated effectiveness on public datasets for emotion recognition.
Achieved a high accuracy rate exceeding 88% in classifying emotional states from EEG data.
The feature fusion technique using autosklearn improved the comprehensiveness of EEG data representation.

Conclusions:

The developed EEG-based emotion recognition system is feasible for real-world application in college student management.
This objective approach overcomes the limitations of traditional subjective methods.
The findings support the use of advanced machine learning techniques for monitoring and supporting student mental well-being.