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Human Emotion Classification from Brain EEG Signal Using Multimodal Approach of Classifier

Published: 26 February 2018 Publication History

Abstract

To deeply understand the brain response under different emotional states can fundamentally advance the computational models for emotion recognition. Various psychophysiology studies have demonstrated the correlations between human emotions and EEG signals. With the quick development of wearable devices and dry electrode techniques it is now possible to implement EEG-based emotion recognition from laboratories to real-world applications. In this paper we have developed EEG-based emotion recognition models for three emotions: positive, neutral and negative. Extracted features are downloaded from seed database to test a classification method. Gamma band is selected as it relates to emotional states more closely than other frequency bands. The linear dynamical system (LDS) is used to smooth the features before classification. The classification accuracy of the proposed system using DE, ASM, DASM, RASM is 97.33, 89.33 and 98.37 for SVM (linear), SVM (rbf sigma value 6) and KNN(n value 3) respectively.

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  • (2023)Co-Design with Myself: A Brain-Computer Interface Design Tool that Predicts Live Emotion to Enhance Metacognitive Monitoring of DesignersExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585701(1-8)Online publication date: 19-Apr-2023
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  1. Human Emotion Classification from Brain EEG Signal Using Multimodal Approach of Classifier

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    cover image ACM Other conferences
    ICIIT '18: Proceedings of the 2018 International Conference on Intelligent Information Technology
    February 2018
    76 pages
    ISBN:9781450363785
    DOI:10.1145/3193063
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    Publication History

    Published: 26 February 2018

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    Author Tags

    1. EEG-based emotion recognition
    2. KNN
    3. LDS
    4. SVM

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    View all
    • (2024)Design with myself: A brain–computer interface design tool that predicts live emotion to enhance metacognitive monitoring of designersInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103229185(103229)Online publication date: May-2024
    • (2024)Emotion recognition with EEG-based brain-computer interfaces: a systematic literature reviewMultimedia Tools and Applications10.1007/s11042-024-18259-z83:33(79647-79694)Online publication date: 1-Mar-2024
    • (2023)Co-Design with Myself: A Brain-Computer Interface Design Tool that Predicts Live Emotion to Enhance Metacognitive Monitoring of DesignersExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585701(1-8)Online publication date: 19-Apr-2023
    • (2022)Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass ClassificationEngineering, Technology & Applied Science Research10.48084/etasr.507312:4(9012-9017)Online publication date: 1-Aug-2022
    • (2022)A Photoplethysmogram Dataset for Emotional AnalysisApplied Sciences10.3390/app1213654412:13(6544)Online publication date: 28-Jun-2022
    • (2021)Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning TechniquesComputers10.3390/computers1003003710:3(37)Online publication date: 19-Mar-2021
    • (2021)Recognizing Emotional States With Wearables While Playing a Serious GameIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2021.305946770(1-12)Online publication date: 2021
    • (2021)Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural NetworkIETE Journal of Research10.1080/03772063.2021.190286869:5(2595-2614)Online publication date: 30-Mar-2021
    • (2021)EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback SystemJournal of Medical and Biological Engineering10.1007/s40846-020-00596-741:2(155-164)Online publication date: 5-Feb-2021
    • (2020)EEG-Based Emotion RecognitionComputational Intelligence and Neuroscience10.1155/2020/88754262020Online publication date: 1-Jan-2020
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