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

DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation.

Frontiers in physiology·2026
Same author

Deep ensemble of multi-head attention CNNs for histopathological image-based of lung and colon cancer diagnosis.

Digital health·2026
Same author

A hybrid CNN-spectral architecture for non-contact respiratory rate estimation using multi-region optical-flow analysis.

PloS one·2026
Same author

A Novel Pipeline for Object Recognition Utilising Multi-Sensory Tactile Fusion.

IEEE transactions on haptics·2026
Same author

Benefit of long-wave infrared camera in the measurement of respiratory rate: A pilot study.

The Journal of international medical research·2025
Same author

A systematic review of contactless respiratory rate measurement using RGB cameras.

Physiological measurement·2025

Related Experiment Video

Updated: Jan 11, 2026

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

5.2K

A machine learning-based EEG signal analysis framework to enhance emotional state detection.

Md Amir Abdal Sobhani1, Sreya Deb Srestha1, Md Maimoon Hossain Shomoy1

  • 1Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh.

Cognitive Neurodynamics
|November 11, 2025
PubMed
Summary

This study introduces a machine learning approach for detecting emotions from Electroencephalogram (EEG) signals. The Multilayer Perceptron (MLP) model achieved 98.8% accuracy, demonstrating the effectiveness of synthetic data in enhancing emotion detection.

Keywords:
BrainwaveCNNEEGEmotional stateGenerative adversarial networkMachine learningMulti-layer perceptron

More Related Videos

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

3.0K
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

14.4K

Related Experiment Videos

Last Updated: Jan 11, 2026

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

5.2K
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

3.0K
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

14.4K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Electroencephalogram (EEG) signals are crucial for understanding brain activity and emotional states.
  • Accurate detection of emotional states from EEG is challenging due to signal complexity and data variability.
  • Machine learning offers promising avenues for analyzing complex biological signals like EEG.

Purpose of the Study:

  • To develop and evaluate a machine learning-based approach for detecting emotional states using EEG signals.
  • To compare the performance of nine different machine learning models for EEG-based emotion detection.
  • To investigate the impact of synthetic data generation techniques on model accuracy and efficiency.

Main Methods:

  • Employed nine machine learning models: Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Support Vector Machine, Light Gradient Boosting Machine, Adaptive Boosting, Multilayer Perceptron (MLP), and 1D Convolutional Neural Network (1D CNN).
  • Utilized a real dataset of EEG signals from 300 patients and generated synthetic datasets using Generative Adversarial Network (GAN), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN).
  • Trained, tested, and validated models on a combination of real and synthetic datasets, evaluating performance across five distinct data configurations.

Main Results:

  • The Multilayer Perceptron (MLP) model demonstrated superior performance across all tested datasets, achieving the highest accuracy and efficiency.
  • MLP achieved a testing accuracy of 98.8% with a low latency ranging from 1.8ms to 4.8ms.
  • The integration of synthetic data significantly enhanced the accuracy and efficiency of machine learning and deep learning models for emotion detection.

Conclusions:

  • The MLP model, enhanced by synthetic data, presents a highly accurate and efficient method for detecting emotional states from EEG signals.
  • This approach holds significant potential for improving diagnostic tools and therapeutic interventions in clinical neuroscience and mental healthcare.
  • The study highlights the value of synthetic data generation in overcoming data limitations and improving the robustness of AI models in biomedical applications.