BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.2// METHOD:PUBLISH X-WR-CALNAME;VALUE=TEXT:Eventi DIAG BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:STANDARD DTSTART:20241027T030000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET END:STANDARD BEGIN:DAYLIGHT DTSTART:20250330T020000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:calendar.28666.field_data.0@www.diag.uniroma1.it DTSTAMP:20260404T130129Z CREATED:20250302T025334Z DESCRIPTION:In ottemperanza ai requisiti previsti dalla procedura valutativ a ai fini della chiamata a Professore di II Fascia ai sensi dell’art. 24\, comma 5 L. 240/2010 per il Settore Concorsuale 09/IBIO-01 (ex 09/G2) – Se ttore Scientifico Disciplinare IBIO-01/A (ex ING-INF/06) presso il Diparti mento di Ingegneria informatica\, automatica e gestionale Antonio Ruberti\ , il giornomercoledì 5 marzo 2025 alle ore 12:00Pietro Aricò\, a seguito d ell'esito positivo ottenuto nella procedura\, terrà presso questo dipartim ento un seminario sulle attività di ricerca svolte e in corso di svolgimen to\, in modalità mista:presso l'Aula Magna del DIAGin collegamento Zoom al link https://uniroma1.zoom.us/j/84822596267 Titolo:Passive Brain-Computer Interfaces: From Research to Real-World ApplicationsAbstract:In recent ye ars\, electroencephalography (EEG)-based passive brain-computer interfaces (pBCIs) have gained increasing attention due to their potential to enhanc e human performance\, safety\, and well-being across various real-world sc enarios. These systems enable the non-intrusive\, real-time monitoring of users' mental and emotional states\, facilitating improved human-machine i nteraction (HMI)\, adaptive automation\, and cognitive workload management . By leveraging neurophysiological signals such as EEG\, electrodermal act ivity (EDA)\, and photoplethysmography (PPG)\, pBCIs provide objective ins ights into workload\, attention\, fatigue\, and stress\, making them valua ble tools in critical environments such as aviation\, healthcare\, manufac turing\, and autonomous systems.Recent technological advancements have sig nificantly contributed to the transition of pBCIs from laboratory research to real-world deployment. Improvements in wearable EEG sensors\, signal p rocessing algorithms\, and artificial intelligence-driven classification t echniques have enhanced the reliability\, usability\, and accessibility of these systems. Additionally\, the development of dry electrodes\, cloud-b ased data processing\, and advanced front-end interfaces has facilitated t heir integration into consumer-grade applications\, broadening their reach beyond specialized research settings.Despite these advancements\, several challenges remain\, including inter-individual variability in neural sign als\, robustness to environmental noise\, and user adaptation over time. A ddressing these issues is crucial for ensuring the widespread adoption and effectiveness of pBCI technologies.This talk will provide a comprehensive overview of the methodological steps involved in designing effective pass ive BCI systems\, highlighting common pitfalls\, best practices\, and emer ging trends. Furthermore\, real-world case studies will be presented to il lustrate successful applications and ongoing developments in the field. Th e session will also discuss future directions for scaling pBCI technology\ , exploring its potential role in shaping the next generation of human-cen tered computing and neuroadaptive technologies.Bio sketch:Pietro Aricò is a Tenure Track Assistant Professor (RTD-B) in Biomedical Engineering at Sa pienza University of Rome. He holds a degree in Biomedical Engineering fro m the same institution and a Ph.D. in Bioengineering from the University A lma Mater Studiorum of Bologna. His research focuses on the evaluation of mental and cognitive states\, particularly in passive Brain-Computer Inter faces (pBCIs)\, leveraging neurophysiological signals such as EEG\, EDA\, and PPG to enhance human-machine interaction (HMI) in real-world applicati ons.During his Ph.D. in Bioengineering\, which began in 2010\, he started working on the concept of pBCIs\, focusing on decoding mental and emotiona l states for adaptive systems in out-of-the-lab contexts. Over the years\, he has developed strong collaborations with academic and industrial partn ers\, working on bridging the gap between experimental research and real-w orld applications.Dr. Aricò has been involved in numerous national and int ernational research projects\, securing funding for 17 projects\, includin g 11 as Principal Investigator (PI).He has authored over 120 articles in p eer-reviewed journals and contributions to international conferences\, wit h an h-index of 34 and more than 3\,300 citations (Scopus\, 2025). His con tributions also include a patent\, high-impact publications\, and multiple research awards.Dr. Aricò currently serves as an Associate Editor for IEE E Transactions on Biomedical Engineering\, Frontiers in Computational Neur oscience\, Frontiers in Human Neuroscience\, and Brain Organoid and System Neuroscience. Additionally\, he is a Guest Editor for various internation al journals in applied neuroscience and brain-computer interfaces. He also serves as a reviewer for high-impact scientific journals and has been inv ited as a keynote speaker and lecturer at numerous international conferenc es and scientific schools. He has supervised several Ph.D. students and re search fellows\, contributing to the training of young researchers in biom edical engineering. DTSTART;TZID=Europe/Paris:20250305T120000 DTEND;TZID=Europe/Paris:20250305T120000 LAST-MODIFIED:20250302T085950Z LOCATION:Aula Magna SUMMARY:Seminario pubblico di Pietro Aricò - \n\n\n \n \n\n \n\n\nPiet ro\n\n\nArico' \n\n \n\n \n\n\n\n\n\nProfessore Associato\n\nstanza:  \n\nA206\n\nMember of: \n\n \n\n \n\n \n\nBiografia: \n\n\n\nPietro A ricò is an Associate Professor in Biomedical Engineering at the Department of Computer\, Control and Management Engineering (DIAG) of Sapienza Unive rsity of Rome. He received his Master’s degree in Biomedical Engineering f rom Sapienza University (2010) and his Ph.D. in Bioengineering from the Un iversity of Bologna (2014). His research focuses on brain–computer interfa ces (BCI)\, with a particular emphasis on passive BCIs\, which exploit bio signal processing (EEG\, ECG\, GSR) to assess cognitive and emotional stat es in real-world applications.\n\n\nRecognized internationally for his con tributions to neuroscience and machine learning applied to BCIs\, Professo r Aricò has coordinated and participated in numerous national and European research projects (Horizon 2020\, FP7\, Italian Ministries). He has autho red more than 120 peer-reviewed journal articles\, holds a patent\, and ha s presented his work at major international conferences (link to publicati ons).\n\n\nIn 2024\, he was included in the “Top 2% Scientists” ranking co mpiled by Stanford University and Elsevier\, which identifies the world’s most influential researchers based on standardized bibliometric indicators .\n\n\nHe serves as Associate Editor for leading international journals su ch as IEEE Transactions on Biomedical Engineering (TBME) and as reviewer a nd guest editor for several others\, including Frontiers in Neuroinformati cs and the Journal of Neural Engineering. He also lectures in the Master’s programs in Biomedical Engineering and Medicine & Surgery HT at Sapienza University.\n\n\nBeyond academia\, he is Chief Technology Officer (CTO) an d Project Manager at BrainSigns\, where he oversees the translation of neu rotechnology applications to the market. His contributions to passive BCI research encompass assistive technologies\, cognitive workload monitoring\ , and human–machine interaction\, positioning him as a key figure in the f ield of applied neuroengineering.\n\n\nInteressi di ricerca: \n\n\n\n\n\nM y research activity has always been focused on one of the most innovative and fascinating areas of bioengineering applied to neuroscience\, the brai n-computer interface (BCI)\, defined as “a system that measures Central ne rvous System (CNS) activity and converts it into artificial output that re places\, restores\, enhances\, supplements\, or improves natural CNS outpu t and thereby changes the ongoing interactions between the CNS and its ext ernal or internal environment\, Wolpaw et al.\, 2012”. In this regard\, I had the possibility to work with different types of BCI systems\, by the i nvolvement in many national and international projects (see sections VIII and XI)\, in particular (i) as assistive technology (i.e. communication an d control)\, (ii) for rehabilitation purposes (i.e. motor imagery) and (ii i) for “passive” monitoring of internal states of the user (i.e. workload\ , attention\, stress\, etc) while dealing with a task (i.e. driving a car or piloting an aircraft). My specific background as bioengineer\, is focus ed on the (i) processing and features extraction of different kind of bios ignals (i.e. electroencephalography-EEG\, electrocardiography-ECG\, photop lethysmography-PPG\, Electro Dermal Activity-EDA\, Electromyography-EMG\, Electrooculography-EOG)\, and (ii) machine learning techniques able to emp loy such mentioned features to maximize BCI performances.\n\n\nBCI for com munication & control: At the beginning of my activity I worked with BCI sy stems for communication and control\, for locked-in patients. In particula r\, it can be possible to decode some specific features extracted from the EEG signal of the subjects\, and employ them as a communication and/or co ntrol channel. In this regard\, I got great knowledge in processing EEG si gnals in time domain\, extract and analyse Event Related Potentials (i.e. ERPs\, P300 and N200 potentials). In this regard\, I have developed an alg orithm able to maximize the signal to noise ratio for an improved extracti on of ERPs from the background EEG noise. At the same time\, I had the pos sibility to deal with machine learning techniques (both linear and non lin ear) applied to such mentioned features\, to be used to enhance BCI perfor mances.\n\n\nBCI for rehabilitation: The principle at the basis of this ki nd of BCI is that the system can be used to “reinforce” specific brain pat terns of post-stroke patients\, while performing simple tasks (e.g. graspi ng an object)\, and so fasten the rehabilitation phase. I have generated i n this regard a hybrid BCI system that employ at the same time EEG and EMG signals\, to maximize the reinforcement of physiological brain patterns\, inhibiting the activation of pathological patterns. During this activity I got expertise in analysing frequency domain features of EEG signals.\n\n \n\nPassive BCI: This kind of BCI is used to “passively” monitor mental an d emotional states of the user\, while dealing with specific operational t asks (e.g. driving a car or piloting an airplane). In particular\, my acti vity was focused on the extraction and classification (by using machine le arning techniques) of specific features\, responsive to variations of actu al mental states of the user. In this regard\, I had the possibility to de al with the processing of different kind of biosignals\, i.e. EEG\, ECG\, PPG\, EDA\, EOG\, and to face with all the constraints of the 'real-settin g' that are often not taken into account by most of the work carried out i n literature (laboratory-setting). In this framework I had patented an alg orithm able to generate in real-time a measure of the mental workload expe rienced by the user\, by using his/her EEG signals.\n\n\nkeywords: \n\nAlg orithmic Data AnalysisAlgorithmsData Acquisition and Sensor NetworksData S ciencePattern RecognitionProcessing and analysis of bioelectrical signals \n\nqualifica_rr: \n\nAssociate professors URL;TYPE=URI:https://www.diag.uniroma1.it/node/28666 END:VEVENT END:VCALENDAR