[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119502-en":3,"doc-seo-119502-105":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},119502,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A Hybrid Machine Learning Approach Utilizing PCA and ICA Features for Stress Classification","Electroencephalographic (EEG) signal-based personal identification systems depend strongly on EEG stability, and stress—an emotion that disrupts everyday performance—becomes a key factor to address. The research classifies stress levels into low (2) and high (1) by extracting features using Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Decision Tree, k-NN, Naive Bayes, SVM, and Ensemble methods are evaluated on 40 EEG recordings from the Stroop color-word test with an 80/20 train-test split. PCA delivers the highest average accuracy (0.718), while Ensemble achieves the best results at 0.770 with PCA features and 0.745 with ICA features, emphasizing the importance of feature selection and model choice for EEG-based stress detection.","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage : www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nA Hybrid Machine Learning Approach Utilizing PCA and ICA  \nFeatures for Stress Classification  \nSetyorini a,b, Ilham Ari Elbaith Zaeni a,*, Hakkun Elmunsyah a  \na Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Malang, Indonesia b Faculty of Technology and Design, Institut Asia, Malang, Indonesia  \nCorresponding author:*[setyorini.2205349@students.um.ac.id](setyorini.2205349@students.um.ac.id)  \nAbstract—Electroencephalographic (EEG) signal-based personal identification systems have notable advantages and disadvantages. These systems heavily rely on the stability of EEG signals, which several factors. One of the primary factors affecting the stability of EEG signals is an individual's emotional state. Among emotional states, stress significantly impairs people's ability to perform daily tasks. This research aims to identify stress levels, classified as low (2) and high (1), using features from Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Machine learning methods, including Decision Tree, k-Nearest Neighbors (k-NN), Naive Bayes, Support Vector Machine (SVM), and Ensemble techniques, are employed to classify the stress levels. The dataset comprises 40 EEG recordings from the Stroop color-word test, and the data is split using a random holdout function with a ratio of 80% for training and 20% for testing. This study examines the most effective features for identifying stress levels and compares the performance of various machine learning models. The experimental results demonstrate that PCA is the most effective feature extraction method, achieving an average accuracy of 0.718 in stress level classification. Among the machine learning models tested, the Ensemble method performs the best, achieving an accuracy of 0.770 when using PCA features and 0.745 with ICA features. This study highlights the importance of selecting optimal features and machine learning techniques for improving stress detection in EEG-based systems. Further improvements in classification accuracy may be achieved by incorporating additional physiological signals or refining feature extraction techniques.  \nKeywords—Stress classification; EEG; hybrid machine learning.  \nManuscript received 20 Sep. 2024; revised 13 Nov. 2024; accepted 19 Feb. 2025. Date of publication 31 May 2025.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nSince stress has both positive and negative impacts when managed effectively, and its effects can spiral out of control, research on stress has been conducted for a considerable amount of time. This is demonstrated by the fact that positive stress, or eustress, is known to provide goals and incentives to overcome obstacles and make individuals more resilient to similar problems in the future. However, the issue arises when concern and negative stress take hold, blocking the will to improve or overcome the obstacles in the way, thereby reducing the possibilities of what could have been accomplished. Electroencephalography (EEG) has been utilized in several studies to detect mental stress, particularly in conjunction with computer algorithms such as artificial intelligence and machine learning [1] .  \nThe action potential produced by neurons firing as a result of a chemical reaction in the brain is measured scientifically  \nby an electroencephalogram. These action potentials will result in a potential difference, which is then calculated on a human scalp using electrodes. The use of brain waves as a biometric modality has garnered increasing attention in recent years. Brain waves are tough to fake and offer a great degree of uniqueness, permanence, and universality.  \nCurrent EEG-based personal identification systems ","cbCaishcznDvWbC4","https://ap.wps.com/l/cbCaishcznDvWbC4","pdf",3661578,1,9,"English","en",105,"# Introduction\n## Background and motivation\n## EEG and stress detection approaches","[{\"question\":\"What problem does the study address in EEG-based systems?\",\"answer\":\"It addresses the impact of emotional state—especially stress—on the stability and reliability of EEG signals used for personal identification and classification tasks.\"},{\"question\":\"How are stress levels defined and classified in this research?\",\"answer\":\"Stress levels are classified into two categories: low (2) and high (1), using machine learning models fed with ICA and PCA feature representations.\"},{\"question\":\"Which feature extraction method and model perform best?\",\"answer\":\"PCA is the most effective feature extraction method (average accuracy 0.718). Among models, the Ensemble method performs best, reaching 0.770 accuracy with PCA features and 0.745 with ICA features.\"}]","A Hybrid Machine Learning Approach Utilizing PCA and ICA Features for Stress Classification | PDF",1785724681,23,{"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},"a-hybrid-machine-learning-approach-utilizing-pca-and-ica-features-for-stress-classification","",{"@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/a-hybrid-machine-learning-approach-utilizing-pca-and-ica-features-for-stress-classification/119502/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in EEG-based systems?","Question",{"text":75,"@type":76},"It addresses the impact of emotional state—especially stress—on the stability and reliability of EEG signals used for personal identification and classification tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are stress levels defined and classified in this research?",{"text":80,"@type":76},"Stress levels are classified into two categories: low (2) and high (1), using machine learning models fed with ICA and PCA feature representations.",{"name":82,"@type":73,"acceptedAnswer":83},"Which feature extraction method and model perform best?",{"text":84,"@type":76},"PCA is the most effective feature extraction method (average accuracy 0.718). Among models, the Ensemble method performs best, reaching 0.770 accuracy with PCA features and 0.745 with ICA features.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]