[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123011-en":3,"doc-seo-123011-105":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":20,"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},123011,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques","Electroencephalogram (EEG) captures brain activity as electrical currents, enabling emotion inference from neural signals. Growing interest in emotion-aware human-computer connections increases the need for reliable, deployable emotion recognition methods. This study classifies EEG waves into neutral, negative, and positive emotions using machine learning and deep learning. A Muse four-channel EEG headband records the dataset Feeling Emotions EEG, and models are evaluated with accuracy, precision, recall, and F1-Score. Results show strong performance, especially from SVM and CNN.","Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques  \nGst. Ayu Vida Mastrika Giri*1, Made Leo Radhitya2  \n1Program Studi Informatika, Fakultas MIPA, Universitas Udayana, Bali, Indonesia  \n2Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia e-mail: *[1](1vida@unud.ac.id)[vida@unud.ac.id](1vida@unud.ac.id), [2](2 leo.radhitya@instiki.ac.id)[ ](2 leo.radhitya@instiki.ac.id)[leo.radhitya@instiki.ac.id](2 leo.radhitya@instiki.ac.id)  \nAbstrak  \nElectroencephalogram (EEG) merekam aktivitas otak sebagai arus listrik untuk melihatemosi. Seiring dengan meningkatnya minat terhadap hubungan emosional antara manusia dankomputer, algoritma pengenalan emosi yang dapat diandalkan menjadi sangat penting. Penelitian ini mengklasifikasikan gelombang EEG menggunakan machine learning dan deep learning. Muse EEG headband dengan empatsaluran merekam emosi netral, negatif, dan positif pada dataset Feeling Emotions EEG yang tersedia untuk umum. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) digunakan untuk deep learning dalam penelitian ini, sementara SVM, K-NN, dan MLP digunakan untuk pembelajaran mesin. Model-model tersebut dievaluasi berdasarkan akurasi, presisi, recall, dan F1-Score. SVM, K-NN, dan MLP memiliki nilai akurasi sebesar 0.98, 0.95, dan 0.97. Metode deep learning CNN, LSTM, dan GRU memiliki akurasi 0,98, 0,82, dan 0,97. SVM dan CNN unggul dalam hal akurasi, presisi, recall, dan F1-Score. Penelitian ini menunjukkan bahwa machine learning dan deep learning dapat mengklasifikasikan sinyalEEG untuk mengidentifikasi emosi. Hasil akurasi yang tinggi, terutama dari SVM dan CNN, menunjukkan bahwa modelmodel ini dapat digunakan dalam sistem interaksi manusia-komputer yang sadar akan emosi. Penelitian ini menambah penelitian klasifikasi emosi berbasis EEG dengan mengungkapkan pemilihan model dan strategi penyesuaian parameter untuk klasifikasi yang lebih baik.  \nKata kunci—deep learning, electroencephalogram, emosi, klasifikasi, machine learning  \nAbstract  \nElectroencephalogram (EEG) records brain activity as electrical currents to discern emotions. As interest in human-computer emotional connections rises, reliable and implementable emotion recognition algorithms are essential. This study classifies EEG waves using machine and deep learning. A four-channel Muse EEG headband recorded neutral, negative, and positive emotions for the publicly available Feeling Emotions EEG dataset. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized for deep learning, while SVM, K-NN, and MLP were used for machine learning. The models were assessed for accuracy, precision, recall, and F1-Score. SVM, K-NN, and MLP have accuracy scores of 0.98, 0.95, and 0.97. Deep learning methods CNN, LSTM, and GRU had 0.98, 0.82, and 0.97 accuracy. SVMand CNN surpassed other approaches in accuracy, precision, recall, and F1-Score. The research shows that machine learning and deep learning can classify EEG signals to identify emotions. High accuracy results, especially from SVM and CNN, suggest these models could be used in emotion-aware human-computer interaction systems. This study adds to EEG-based emotion classification research by revealing model selection and parameter tweaking strategies for better categorization.  \nKeywords—classification, deep learning, electroencephalogram, emotion, machine learning  \n1. INTRODUCTION  \nNeuroscience and computer science studies have intertwined. In recent years, neural networks and deep learning in neuroscience have garnered interest for their ability to analyze complicated neurological data using artificial intelligence methods like machine learning and deep learning. Today, commercial headsets can collect EEG signals from our brains to provide neurological data. EEG measures brain activity through electric currents. Electrodes on the s","cbCaisID2la4Eih6","https://ap.wps.com/l/cbCaisID2la4Eih6","pdf",762051,1,12,"English","en",105,"# Introduction\n## EEG signals and emotion recognition\n## Machine learning and deep learning approaches\n## Study motivation and research basis","[{\"question\":\"Which EEG-based emotions are classified in this study?\",\"answer\":\"The study classifies EEG signals into neutral, negative, and positive emotions based on the Feeling Emotions EEG dataset.\"},{\"question\":\"What machine learning models are used?\",\"answer\":\"SVM, K-NN, and MLP are used for machine learning-based emotion classification.\"},{\"question\":\"What deep learning models are compared, and which performs best?\",\"answer\":\"The deep learning models include CNN, LSTM, and GRU. The results indicate that SVM and CNN achieve the strongest overall performance across accuracy, precision, recall, and F1-Score.\"}]","Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques | PDF",1785814163,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"electroencephalogram-based-emotion-classification-using-machine-learning-and-deep-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/electroencephalogram-based-emotion-classification-using-machine-learning-and-deep-learning-techniques/123011/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which EEG-based emotions are classified in this study?","Question",{"text":75,"@type":76},"The study classifies EEG signals into neutral, negative, and positive emotions based on the Feeling Emotions EEG dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning models are used?",{"text":80,"@type":76},"SVM, K-NN, and MLP are used for machine learning-based emotion classification.",{"name":82,"@type":73,"acceptedAnswer":83},"What deep learning models are compared, and which performs best?",{"text":84,"@type":76},"The deep learning models include CNN, LSTM, and GRU. 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