[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125874-en":3,"doc-seo-125874-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125874,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Affective State Detection using fNIRs and Machine Learning","Affective states strongly influence mental and physical health, and reliable detection supports mental health monitoring, adaptive entertainment selection, and dynamic workload management. The work reviews relevant approaches that use physiological signals, compares sensors and data-collection methods, and motivates functional near-infrared spectroscopy (fNIRs). An experiment with nine subjects elicits meditation, amusement, and cognitive load and evaluates machine-learning classification using leave-one-out cross validation. Reported accuracies include 83.04% (individual), 84.39% (group), and 60.57% (subject-independent).","Affective State Detection using fNIRs and Machine  \nLearning  \nRitam Ghosh  \nDept. of Electrical Engineering  \nVanderbilt University  \nNashville, USA  \nritam.ghosh@Vanderbilt.Edu  \nAbstract — Affective states regulate our day to day to function and has a tremendous effect on mental and physical health. Detection of affective states is of utmost importance for mental health monitoring, smart entertainment selection and dynamic workload management. In this paper, we discussed relevant literature on affective state detection using physiology data, the benefits and limitations of different sensors and methods used for collecting physiology data, and our rationale for selecting functional near-infrared spectroscopy. We present the design of an experiment involving nine subjects to evoke the affective states of meditation, amusement and cognitive load and the results of the attempt to classify using machine learning. A mean accuracy of 83.04% was achieved in three class classification with an individual model; 84.39% accuracy was achieved for a group model and 60.57% accuracy was achieved for subject independent model using leave one out cross validation. It was found that prediction accuracy for cognitive load was higher (evoked using a pen and paper task) than the other two classes (evoked using computer bases tasks). To verify that this discrepancy was not due to motor skills involved in the pen and paper task, a second experiment was conducted using four participants and the results of that experiment has also been presented in the paper.  \nKeywords—affective states, fNIRs, brain imaging, BCI  \nI. INTRODUCTION  \nAffective states regulate our daily function and has a tremendous effect on our mental and physical health. A lot of research has been undertaken recently on detection and classification of affective states using automated techniques. Knowledge of affective states can be used in various applications like judging the mental health of a subject and evaluating the effectiveness of any therapy or intervention, particularly for people in the autism spectrum, who do not exhibit similar facial expressions as neurotypical individuals [1,2] . Affective states like amusement or sorrow can provide insights into an individual’s mental state. Quantitative measurement of stress or cognitive overload are used for various applications like adaptive difficulty control of games or other physical activities [3,4], dynamic workload management to ensure optimum productivity and preventing overburdening of employees in the workplace [5,6] . An accurate and automated real time measurement of affective states can be used as feedback to a controller to control the intensity of the administered stimulus.  \nVarious methods are used to measure affective states like measuring the concentration of various hormones in the bloodstream, judging affective state from facial expressions, body movements and gestures etc. While affective state measurement from bloodwork is very accurate, it is intrusive, requires clinical practitioners and is not real-time. A trained human observer can provide real time affective state assessment based on facial expressions and body movements but that requires specialized human labor. Also, they are susceptible to human bias, will be inconsistent in between  \nobservers and is not scalable. Computer vision techniques have been used to automate the process of detecting affective states from facial expressions [7] but those techniques can suffer from bias in the training dataset due to the fact that individual subjects have different expressions corresponding to different affective states and they might choose to deliberately mask their expressions if they do not want to reveal their emotions.  \nOn the other hand, physiology depends on the autonomic nervous system which controls the involuntary functions of the body and cannot be voluntarily controlled or suppressed. Also, physiology is largely consistent among individuals and ","cbCaiiNinzZr8dKt","https://ap.wps.com/l/cbCaiiNinzZr8dKt","pdf",456249,6,1,7,"English","en",105,"# Introduction\n## Motivation and applications\n## Measuring affective states\n# Related Work\n## Common physiological sensors\n## fNIRs-related datasets and benchmarks","[{\"question\":\"Why is affective state detection important in this study?\",\"answer\":\"It enables mental health monitoring, smarter entertainment selection, and dynamic workload management by providing automated, real-time feedback about an individual’s affective condition.\"},{\"question\":\"What physiological modality does the paper focus on and why?\",\"answer\":\"The study selects functional near-infrared spectroscopy because it provides a non-invasive physiological signal suited for classifying affective states.\"},{\"question\":\"How were affective states and model performance evaluated?\",\"answer\":\"Nine subjects were used to evoke meditation, amusement, and cognitive load, and classification was performed with machine learning using leave-one-out cross validation, producing accuracies of 83.04% (individual), 84.39% (group), and 60.57% (subject independent).\"}]","Affective State Detection using fNIRs and Machine Learning | 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is affective state detection important in this study?","Question",{"text":77,"@type":78},"It enables mental health monitoring, smarter entertainment selection, and dynamic workload management by providing automated, real-time feedback about an individual’s affective condition.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What physiological modality does the paper focus on and why?",{"text":82,"@type":78},"The study selects functional near-infrared spectroscopy because it provides a non-invasive physiological signal suited for classifying affective states.",{"name":84,"@type":75,"acceptedAnswer":85},"How were affective states and model performance evaluated?",{"text":86,"@type":78},"Nine subjects were used to evoke meditation, amusement, and cognitive load, and classification was performed with machine learning using leave-one-out cross validation, producing accuracies of 83.04% (individual), 84.39% (group), and 60.57% (subject 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