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EEG Emotion Recognition Based on the Dimensional Models of Emotions

Marini Othman, Abdul Wahab, Izzah Karim, Mariam Adawiah Dzulkifli, Imad Fakhri Taha Alshaikli
2013 Procedia - Social and Behavioral Sciences  
Features were extracted using KSDE and MFCC and classified using MLP. Results show that EEG emotion recognition using the 12-PAC model gives the highest accuracy for both feature extraction methods.  ...  In this paper, we propose a method for EEG emotion recognition which is tested based on 2 dimensional models of emotions, (1) the rSASM, and (2) the 12-PAC model.  ...  O. thanks Rica Frances Talon from the National Autism Society of Malaysia (NASOM) for some preliminary feedbacks on the emotional stimuli and the families of the participants of this study.  ... 
doi:10.1016/j.sbspro.2013.10.201 fatcat:zhtciln2czc3tmw7jfd24tcy4y

Emotion Recognition With Audio, Video, EEG, and EMG: A Dataset and Baseline Approaches

Jin Chen, Tony Ro, Zhigang Zhu
2022 IEEE Access  
RECOGNITION ACCURACIES (MEAN ± STD (%)) FOR AUDIO MFCC FEATURES ON 20MS WINDOW USING ENSEMBLE LSTM NETWORK recognize the emotion with EEG data.  ...  Comparisons of a few baseline machine learning methods (KNN, SVM, Random Forest, MLP, CNN, LSTM) in classifying emotions with optimal features; and 4) A comprehensive survey of emotion recognition in  ...  Her research interests include computer vision, indoor localization and navigation, and assistive technology. Tony  ... 
doi:10.1109/access.2022.3146729 fatcat:scb2rb6drvblxgfonkf2cwzclu

Extracting Features Using Computational Cerebellar Model for Emotion Classification

Hamwira Yaacob, Wahab Abdul, Norhaslinda Kamaruddin
2013 2013 International Conference on Advanced Computer Science Applications and Technologies  
Such techniques are not constructed based on the understanding of EEG and brain functions, neither inspired by the understanding of emotional dynamics.  ...  Several feature extraction techniques have been employed to extract features from EEG signals for classifying emotions.  ...  Technology for the Muslim World, ICT4M 2016 Inform me when this document is cited in Scopus: Related documents , , EEG-based emotion recognition in the investment activities Razi, N.I.M  ... 
doi:10.1109/acsat.2013.79 fatcat:2zsqc2macffqvl3d6vescqaj2q

Identifying Stress Level Among Gamers Using Electroencephalogram Signals

Norzaliza MNor, Sheik Dawood Mohamed Rafi, Muhammad Arif Othman
2020 International Journal on Perceptive and Cognitive Computing  
This data will be analysed using Mel Frequency Cepstral Coefficients (MFCC) as feature extraction, and multilayer perceptron (MLP) as classifier.  ...  Calm, Fear, Sad) and the emotion while playing the games.  ...  This data will be analysed using MFCC (Mel Frequency Cepstral Coefficients) as feature extraction, and MLP (multilayer perceptron) as classifier.  ... 
doi:10.31436/ijpcc.v6i2.155 fatcat:fzf4cmvv5zduthp77llkdd33v4

Techniques in Pattern Recognition for School Bullying Prevention: Review and Outlook

Liang Ye, Hany Ferdinando, Esko Alasaarela
2014 Journal of Pattern Recognition Research  
With the development of sensor technology and pattern recognition algorithms, several approaches for detecting school bullying have been developed, namely speech emotion recognition, mental stress recognition  ...  , and activity recognition.  ...  The base-level classifier consisted of GMM, SVM, and MLP, and the average accuracies of these were 73%, 78% and 72%, respectively.  ... 
doi:10.13176/11.586 fatcat:hx3jyckdqrez7d46iki7nmwxnu

A Review on EEG Signals Based Emotion Recognition

Morteza Zangeneh Soroush, Keivan Maghooli, Seyed Kamaledin Setarehdan, Ali Motie Nasrabadi
2017 International Clinical Neuroscience Journal  
Acknowledgment We would like to thank Science and Research Branch, Islamic Azad University due to their support.  ...  Lee et al 54 proposed an emotion recognition system based on fuzzy logic. They used video clips to elicit emotions and recorded EEG signals from 12 participants.  ...  database) EEG signals from 5 children KSDE (Kernel Density Estimation), MFCC (Mel-Frequency Cepstral Coefficients), MLP MFCC-rSASM has lower MSE vs MFCC-12PAC and KSDE-12-PAC was lower than KSDE-rSASM  ... 
doi:10.15171/icnj.2017.01 fatcat:3pgofdneerdb5msxb5zfyvh3ke

Spoken emotion recognition using hierarchical classifiers

Enrique M. Albornoz, Diego H. Milone, Hugo L. Rufiner
2011 Computer Speech and Language  
In this paper, the spectral characteristics of emotional signals are used in order to group emotions based on acoustic rather than psychological considerations.  ...  The proposed multiple feature hierarchical method for seven emotions, based on spectral and prosodic information, improves the performance over the standard classifiers and the fixed features.  ...  with PID 61111-2 and 6107-2) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) from Argentina, for their support.  ... 
doi:10.1016/j.csl.2010.10.001 fatcat:5uvnq63cabagdf5guzutjuec54

Feature extraction based on bio-inspired model for robust emotion recognition

Enrique M. Albornoz, Diego H. Milone, Hugo L. Rufiner
2016 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions.  ...  Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system.  ...  Acknowledgements The authors wish to thank to Agencia Nacional de Promoción Científica y Tecnológica and Universidad Nacional de Litoral (with PAE 37122, PACT 2011 #58, CAI+D 2011 #58-511) and Consejo  ... 
doi:10.1007/s00500-016-2110-5 fatcat:czenyscwanb4rj3lzx57qm7xs4

Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals

Firgan Feradov, Iosif Mporas, Todor Ganchev
2020 Computers  
)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task.  ...  In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli.  ...  Energy and LFCC * SVM 75.7% Liu et al. [34] LFCC * kNN 90.9% Wahab et al. [35] Statistical time domain features/ MFCC RVM, SVM, MLP, DT, BN, EFuNN 97.8% Othman et al. [36] MFCC, KDE MLP NN 0.05 (MSE) Othman  ... 
doi:10.3390/computers9020033 doaj:7915dfb87c1544569509787fe9854f83 fatcat:66y2lq6rg5fbrj6fqjveed7xty

Emotion detection from EEG signals: correlating cerebral cortex activity with musicEMOTION

Erim Yurci, Rafael Ramírez
2014 Zenodo  
This master project aims to study music evoked emotion using electroencephalography (EEG) techniques.  ...  The EEG signals have been analyzed in order to extract features, some of them emotionally related, and these features have been used to predict the type of music to which the subject is listening.  ...  In 2000 Choppin proposed to use EEG signals for classifying six emotions based on emotional valence and arousal [7] .  ... 
doi:10.5281/zenodo.3755450 fatcat:ekpbtl54z5ftzlili7d6lh7nri

EEG Affective Modelling for Dysphoria Understanding

Norhaslinda Kamaruddin, Mohd HafizMohd Nasir, Abdul Wahab Abdul Rahman
2018 2018 12th International Symposium on Medical Information and Communication Technology (ISMICT)  
To complicate matters, pre-cursor emotion and pre-emotion of the participants can result in biasness of the DASS report. Hence, a  ...  It is a state of feeling unwell in relation to mental and emotional discomfort. If this state is not carefully handled, it may lead to depression, anxiety, and stress.  ...  EEG-based emotion recognition algorithm Cited 33 times http://springerlink.com/content/0302-9743/copyright/2005/ Measuring emotion: The self-assessment manikin and the semantic differential Cited 3446  ... 
doi:10.1109/ismict.2018.8573716 dblp:conf/ismict/KamaruddinNR18 fatcat:72wjyca4fveirgjmaozromk3aq

Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning [article]

Samarth Tripathi, Sarthak Tripathi, Homayoon Beigi
2019 arXiv   pre-print
In this paper we attempt to exploit this effectiveness of Neural networks to enable us to perform multimodal Emotion recognition on IEMOCAP dataset using data from Speech, Text, and Motion capture data  ...  With the advancement of technology our understanding of emotions are advancing, there is a growing need for automatic emotion recognition systems.  ...  Emotion recognition has been studied widely using speech [3] [4] [5] , text [6] , facial cues [7] , and EEG based brain waves [8] individually.  ... 
arXiv:1804.05788v3 fatcat:5bxu2yszsjcjbli5ec3ju3lwky

On the influence of affect in EEG-based subject identification

Pablo Arnau-Gonzalez, Miguel Arevalillo-Herraez, Stamos Katsigiannis, Naeem Ramzan
2018 IEEE Transactions on Affective Computing  
This improvement holds independently of the features and classification algorithm used, and it is generally above 10% under a rigorous setting, when the training and validation datasets do not share data  ...  Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person.  ...  ACKNOWLEDGMENTS This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through project TIN2014-59641-C2-1-P and by the University of the West Scotland Vice Principal's  ... 
doi:10.1109/taffc.2018.2877986 fatcat:mfm3ilt6rbgpjmctlrm63n7ivm

Toward Design and Enhancement of Emotion Recognition System Through Speech Signals of Autism Spectrum Disorder Children for Tamil Language Using Multi-Support Vector Machine [chapter]

C. Sunitha Ram, R. Ponnusamy
2017 Proceedings of International Conference on Computational Intelligence and Data Engineering  
In speech emotion recognition discipline Support Vector Machine was used for classification method to classify various databases like Berlin Database, speaker dependent Tamil emotion databases of Autism  ...  Discrete Wavelet Transform and Mel Frequency Cepstral Coefficient are the feature extraction method which is used.  ...  The SVM is used to test and train based on MFCC and DWT feature vectors.  ... 
doi:10.1007/978-981-10-6319-0_13 fatcat:ve5uytxgezgbpkt7gk5yzxjhki

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
., emotion recognition and sentiment analysis).  ...  Affective computing is realized based on unimodal or multimodal data, primarily consisting of physical information (e.g., textual, audio, and visual data) and physiological signals (e.g., EEG and ECG signals  ...  [307] used low-level posture descriptions and feature analysis using non-acted body gestures to implement MLP-based emotion and dimension recognition. Volkova et al.  ... 
arXiv:2203.06935v3 fatcat:h4t3omkzjvcejn2kpvxns7n2qe
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