[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122117-en":3,"doc-seo-122117-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122117,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","EEG-based Affect Classification with Machine Learning Algorithms","The paper investigates EEG-based emotion recognition using machine learning. Emotions are grouped by a clustering approach, followed by binary classification along the arousal and valence dimensions. Two feature extraction pipelines are compared: wavelet transform features versus nonlinear dynamics features derived from approximate entropy and sample entropy. Five feature-reduction methods are evaluated, and four classifiers—KNN, Naive Bayes, SVM, and Random Forest—are compared. Experiments on the DEAP dataset show that kernel spectral regression with random forest yields the best binary emotion classification, with EEG gamma rhythm strongly linked to emotional variation.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nIFAC PapersOnLine 56-2 (2023) 11627–11632  \nEEG-based Affect Classification with Machine Learning Algorithms  \nJianhua Zhang*, Zhong Yin**, and Peng Chen***  \n*AI Lab, Department of Computer Science, Oslo Metropolitan University, 0166 Oslo, Norway (Tel: +47-67 23 66 91; [e-mail: j](e-mail: jianhuaz@oslomet.no)[ianhuaz@oslomet.no](e-mail: jianhuaz@oslomet.no))  \n**School of Optical-Electrical and Computer Eng., University of Shanghai for Science and Technology, Shanghai 200093, P.R. China ([e-mail: y](e-mail: yinzhong@usst.edu.cn)[inzhong@usst.edu.cn](e-mail: yinzhong@usst.edu.cn))  \n***School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, P.R. China (e-mail: [chenpeng0538@qq.com](chenpeng0538@qq.com))  \nAbstract: In this paper, we aim to study the EEG-based emotion recognition problem. First, we use clustering algorithm to determine the target class of emotions and perform binary classification of emotion along its arousal and valence dimension. Then we compare two different feature extraction methods, i.e., wavelet transform (resulting in wavelet-based features) and nonlinear dynamics analysis (leading to features of approximate entropy and sample entropy) . Five feature reduction algorithms are compared in terms of emotion classification accuracy. Furthermore, four types of machine learning classifiers, including k-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVM) and random forest (RF), are also compared. The results on the DEAP physiological data show that the combination of kernel spectral regression (KSR) and random forest leads to the best binary classification of emotions and that the EEG gamma rhythm is closely correlated to variations in emotions.  \nCopyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/))  \nKeywords: Emotion recognition; Affective computing; Electroencephalogram (EEG); Nonlinear dynamics; Wavelets; Kernel Spectral Regression (KSR); Machine learning.  \n1. INTRODUCTION  \nAffective computing has a wide range of applications. For instance, in human-computer interaction (HCI), if the computer can rapidly and accurately estimate the user's emotional state, the interaction would become more userfriendly and smarter. The application to enhancement of user experience of a product allows manufacturer to monitor in real time the emotional state of its user. In aerospace and defense applications, the risky mental/psychological state of astronauts and soldiers may be detected in real time. In the applications to driving safety, the driver's emotional state can be monitored in real time in order to prevent potential dangers or accidents due to extreme emotional state of the driver during driving.  \nEmotion recognition is an essential component of affective computing. Human emotions can be identified through the use of facial expressions (video or image), speech (audio), behavior, or physiological signals (Petrushin, 1999; Anderson and McOwan, 2006; Pantic and Rothkrantz, 2000; Zhong et al., 2017; Zhang et al., 2020 ) . However, the first three methods may fail when subjects deliberately conceal their true emotions. In contrast, the physiological signals are more reliable and objective (Wang, Nie and Lu, 2014) . EEG signals respond to emotion changes more rapidly than other types of peripheral neural signals. It was shown that EEG signals are rich in features of emotional states (Li et al., 2009; Petrantonakis and Hadjileontiadis, 2011) . In recent years, there is an increasing need for intelligent HCI. The current studies on emotion recognition focus on:  \n(i) correlation between physiological signals and emotions;  \n(ii) different stimulation materials used to evoke various emotion responses; ","cbCaiaJobzgMbEXb","https://ap.wps.com/l/cbCaiaJobzgMbEXb","pdf",1181812,1,6,"English","en",105,"# Introduction\n# Dataset, Affect Recognition Framework, and EEG Data Preprocessing\n## Dataset and affect recognition framework\n## EEG data preprocessing","[{\"question\":\"How does the paper structure EEG-based emotion recognition?\",\"answer\":\"It first uses clustering to determine emotion target classes, then performs binary classification along arousal and valence dimensions.\"},{\"question\":\"What feature extraction methods are compared?\",\"answer\":\"The study compares wavelet transform features with nonlinear dynamics features, including approximate entropy and sample entropy.\"},{\"question\":\"Which classifiers and feature-reduction comparisons are evaluated?\",\"answer\":\"Four classifiers are compared (KNN, Naive Bayes, SVM, Random Forest) and five feature-reduction algorithms are tested based on classification accuracy.\"}]","EEG-based Affect Classification with Machine Learning Algorithms | 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