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Emotion Recognition from EEG Signals using Hierarchical Bayesian Network with Privileged Information

Authors:

Zhen Gao,

Shangfei WangAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 579 - 582

https://doi.org/10.1145/2671188.2749364

Published: 22 June 2015 Publication History

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Abstract

Current work of emotion recognition from electroencephalogram (EEG) signals mainly focuses on the generality among users, ignoring users' specificity. However, users' emotion is a subjective phenomenon with both common and specific characteristics. Therefore, we propose a novel emotion recognition method using hierarchical Bayesian network to handle generality and specificity of emotions simultaneously. Specifically, by modeling the prior distributions of parameters for each subject, the classifier for a subject is learned together with those of others, with a shared representation. In addition, by marginalizing over the node of subjects, the subject information is used as privileged information, which is only required during training to build a better classifier. Experimental results on the MAHNOB-HCI and DEAP databases demonstrate that our model with the subject id as privileged information can improve the emotion recognition performance.

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Li JBarma SPun SVai MMak P(2023)Emotion Recognition Based on EEG Brain Rhythm Sequencing TechniqueIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.314995315:1(163-174)Online publication date: Mar-2023
https://doi.org/10.1109/TCDS.2022.3149953
Vempati RSharma L(2023)EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifierJournal of Neuroscience Methods10.1016/j.jneumeth.2023.109879393(109879)Online publication date: Jun-2023
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Published In

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 22 June 2015

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SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

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ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Li JBarma SPun SVai MMak P(2023)Emotion Recognition Based on EEG Brain Rhythm Sequencing TechniqueIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.314995315:1(163-174)Online publication date: Mar-2023
https://doi.org/10.1109/TCDS.2022.3149953
Vempati RSharma L(2023)EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifierJournal of Neuroscience Methods10.1016/j.jneumeth.2023.109879393(109879)Online publication date: Jun-2023
https://doi.org/10.1016/j.jneumeth.2023.109879
de Queiroz Cavalcanti DMelo FSilva TFalcão MCavalcanti MBecker V(2023)Research on Brain-Computer Interfaces in the Entertainment FieldHuman-Computer Interaction10.1007/978-3-031-35596-7_26(404-415)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35596-7_26
Cavalcanti MMelo FSilva TFalcão Mde Queiroz Cavalcanti DBecker V(2023)Incorporating Eye Tracking into an EEG-Based Brainwave Visualization SystemHuman-Computer Interaction10.1007/978-3-031-35596-7_25(392-403)Online publication date: 9-Jul-2023
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Rivas JOrihuela-Espina FSucar LBianchi-Berthouze N(2021)Dealing with a Missing Sensor in a Multilabel and Multimodal Automatic Affective States Recognition System2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)10.1109/ACII52823.2021.9597463(1-8)Online publication date: 28-Sep-2021
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Li PLiu HSi YLi CLi FZhu XHuang XZeng YYao DZhang YXu P(2019)EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local ActivationsIEEE Transactions on Biomedical Engineering10.1109/TBME.2019.289765166:10(2869-2881)Online publication date: Oct-2019
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Zhang KZhang HLi SYang CSun LAizawa KLew MSatoh S(2018)The PMEmo Dataset for Music Emotion RecognitionProceedings of the 2018 ACM on International Conference on Multimedia Retrieval10.1145/3206025.3206037(135-142)Online publication date: 5-Jun-2018
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Emotion Recognition from EEG Signals Enhanced by User's Profile

Emotion Classification Using EEG Signals

Spectral subtraction based emotion recognition using EEG

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