[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123927-en":3,"doc-seo-123927-105":30,"detail-sidebar-cat-0-en-105":94},{"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},123927,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Monitoring Human Attention with a Portable EEG Sensor and Supervised Machine Learning - Paper","Portable and wearable sensors make continuous health monitoring feasible, yet mental states remain less explored than physical conditions. This paper presents an attention-level estimation approach using a cheap, unobtrusive EEG sensor paired with supervised machine learning. EEG signals are preprocessed and features are extracted with sliding windows to classify attentive versus distracted states. Two datasets from a portable headset and a more advanced device are compared to assess performance and generalization across subjects.","Monitoring Human Attention with a Portable EEG Sensor and Supervised Machine Learning  \nSilvia M. Massa 1 , Giovanni Usai1 and Daniele Riboni1  \n1Dept. of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy  \nAbstract  \nFor several healthcare applications, it is important to monitor the attention level of people, especially in the fields of rehabilitation and psychology. The recent availability of cheap and portable EEG readers has enabled continuous and unobtrusive acquisition ofEEG signals. Those signals may be preprocessed and analysed with machine learning algorithms to estimate the attention level of people without interfering with their current activities. In this paper, we report our experience with attention level estimation using two kinds of devices: an off-the-shelf portable EEG headset, and a more sophisticated EEG device.  \nKeywords  \nPervasive healthcare, human attention monitoring, EEG sensor data, supervised machine learning  \n1. Introduction  \nThe increasing availability of portable and wearable sensors, more and more integrated in everyday objects, is paving the way to a new generation of applications to support personal health and well-being. Consequently, impressive research efforts have been devoted to devise effective techniques for recognizing human activities and complex behaviors based on those sensor data [1, 2, 3] .  \nInterestingly, while a vast amount of healthcare applications use sensor-based artificial intelligence for addressing the physical dimension of health, the mental dimension is less investigated [4, 5] . However, a substantial portion of the world’s population deals with mental disability. Many people with mental illnesses do not have equal access to healthcare, education, and employment opportunities, do not receive specific disability-related services, and experience exclusion from everyday life activities. Unfortunately, there is a large amount of diverse mental disabilities, which require ad-hoc and personalized solutions. Moreover, the design and implementation of effective and efficient technologies is a complex and expensive process involving challenging issues, including usability and acceptability.  \nIn this paper, we evaluate the use of a cheap and unobtrusive portable electroencephalography (EEG) sensor for monitoring the human attention level. Indeed, the ability to monitor human attention is fundamental for treating several conditions, including the diagnosis and  \nPublished in the Workshop Proceedings ofthe EDBT/ICDT 2023 Joint Conference (March 28-March 31, 2023), Ioannina, Greece [Envelope-Open](Envelope-Open silviam.massa@unica.it)[ silviam.massa@unica.it](Envelope-Open silviam.massa@unica.it) (S. M. Massa);  \n[giovanniusai1@hotmail.com](giovanniusai1@hotmail.com) (G. Usai); [riboni@unica.it](riboni@unica.it) (D. Riboni) Orcid 0000-0002-3285-8971 (S. M. Massa); 0000-0002-0695-2040 (D. Riboni)  \n© 2023 Copyright for this paper by its authors. Use permitted under Creative  \n\n|  | CEUR Workshop Proceedings |\n| --- | --- |\n\nCommons License Attribution 4 .0 International (CC BY 4 .0) .  \nCEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org))  \n[http://ceur-ws.org](http://ceur-ws.org)  \n[ISSN 1613-0073](ISSN 1613-0073)  \nrehabilitation of children with attention-deficit/hyperactivity disorder [6] . We propose a feature extraction technique based on sliding windows, and supervised machine learning to distinguish between attentive and distracted states. We experimentally compared the performance of the portable EEG sensor with a more powerful EEG device using real-world datasets. Results indicate that the accuracy achieved by the simpler EEG sensor is close to the one achieved by the more sophisticated device. Moreover, the technique provides reliable results when the machine learning algorithm is trained on the specific subject, while accuracy significantly drops when the algorithm is trained on other subjects. This study provides us","cbCaijXLAyDdstUy","https://ap.wps.com/l/cbCaijXLAyDdstUy","pdf",324207,1,4,"English","en",105,"# Introduction\n# Material and methods\n## Image-labeling dataset","[{\"question\":\"Why is monitoring human attention important in healthcare?\",\"answer\":\"Attention level monitoring supports treating conditions in rehabilitation and psychology, including scenarios such as ADHD-related rehabilitation for children.\"},{\"question\":\"How do the authors estimate attention level from EEG data?\",\"answer\":\"They preprocess EEG signals, extract features using sliding windows, and apply supervised machine learning to distinguish attentive from distracted states.\"},{\"question\":\"What datasets and devices are used for the experiments?\",\"answer\":\"Experiments use two datasets: one collected with a portable 4-channel headset (Image-labeling dataset) and one collected with a more sophisticated 7-channel EEG device (Epoc).\"},{\"question\":\"Do results transfer well to other subjects?\",\"answer\":\"Accuracy remains reliable when the learning model is trained on the specific subject, but performance significantly drops when trained on different subjects.\"}]","Monitoring Human Attention with a Portable EEG Sensor and Supervised Machine Learning - 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