Pietro Arico'
Pietro Aricò received his M.Sc. Degree in Biomedical Engineering from Sapienza University of Rome in 2010 and a PhD in Bioengineering from University of Bologna Alma Mater Studiorum in 2014. In 2016 he joined the Department of Molecular Medicine of the Sapienza University and the spin-off company BrainSigns, in which he is Chief Technology Officer and Project Manager. He is now Assistant Professor (RTD-B) at the Department of Computer, Control and Management Engineering, Sapienza University of Rome.
His research activity has always been focused on one of the most innovative and fascinating areas of bioengineering applied to neuroscience, the brain-computer interface (BCI), by focusing in particular to the development of advanced methodologies for the processing and classification of biosignals (i.e. Electroencephalography, Electrocardiogram, Galvanic Skin Response), with the purpose to assess mental and emotional states (e.g. workload, attention, stress) of the user in out-of-the-labs applications (i.e. passive-BCI).
In particular, Pietro Aricò is internationally recognized for his research activities in the area of passive BCI.
He participated in several national and international research projects, also as Principal Investigator, funded by the Italian Ministry of Education, Italian Ministry of Health, 7th Framework Program and Horizon 2020 of the European Commission, and he is a reviewer of research proposals for several international programs.
Dr. Aricò is co-author of more than 50 scientific articles in peer-reviewed international journals, 1 patent and more than 50 scientific contributions in peer-reviewed national and international conferences. He serves as reviewer for several peer-review journals, he is the Associate Editor for the International Journal of Bioelectromagnetism journal, and Guest Editor within many special issues on international journals, such as Frontiers in Human Neuroscience and Brain Sciences.
He teaches in the MSc programs in Biomedical Engineering and Medicine and Surgery HT (courses: Biosignal Processing - 6 CFU; Biosignal Processing and electromagnetic fields - 3 CFU).
He is member of the board of the PhD programme in Control engineering, Bioengineering and Operations research at Sapienza University of Rome.
My research activity has always been focused on one of the most innovative and fascinating areas of bioengineering applied to neuroscience, the brain-computer interface (BCI), defined as “a system that measures Central nervous System (CNS) activity and converts it into artificial output that replaces, restores, enhances, supplements, or improves natural CNS output and thereby changes the ongoing interactions between the CNS and its external or internal environment, Wolpaw et al., 2012”. In this regard, I had the possibility to work with different types of BCI systems, by the involvement in many national and international projects (see sections VIII and XI), in particular (i) as assistive technology (i.e. communication and control), (ii) for rehabilitation purposes (i.e. motor imagery) and (iii) 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 focused on the (i) processing and features extraction of different kind of biosignals (i.e. electroencephalography-EEG, electrocardiography-ECG, photoplethysmography-PPG, Electro Dermal Activity-EDA, Electromyography-EMG, Electrooculography-EOG), and (ii) machine learning techniques able to employ such mentioned features to maximize BCI performances.
BCI for communication & control: At the beginning of my activity I worked with BCI systems for communication and control, for locked-in patients. In particular, 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 control channel. In this regard, I got great knowledge in processing EEG signals in time domain, extract and analyse Event Related Potentials (i.e. ERPs, P300 and N200 potentials). In this regard, I have developed an algorithm able to maximize the signal to noise ratio for an improved extraction of ERPs from the background EEG noise. At the same time, I had the possibility to deal with machine learning techniques (both linear and non linear) applied to such mentioned features, to be used to enhance BCI performances.
BCI for rehabilitation: The principle at the basis of this kind of BCI is that the system can be used to “reinforce” specific brain patterns of post-stroke patients, while performing simple tasks (e.g. grasping an object), and so fasten the rehabilitation phase. I have generated in 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.
Passive BCI: This kind of BCI is used to “passively” monitor mental and emotional states of the user, while dealing with specific operational tasks (e.g. driving a car or piloting an airplane). In particular, my activity was focused on the extraction and classification (by using machine learning techniques) of specific features, responsive to variations of actual mental states of the user. In this regard, I had the possibility to deal 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-setting" that are often not taken into account by most of the work carried out in literature (laboratory-setting). In this framework I had patented an algorithm able to generate in real-time a measure of the mental workload experienced by the user, by using his/her EEG signals.
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2023 - present |
Scientific Board Member of the Phd in automatic control, bioengineering and operations research, Sapienza University of Rome. |
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2021 - present |
Marie Curie Alumni Association |
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2014-2015 |
- IEEE Membership: from 01/03/2014 to 31/12/2015 |
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2014 - present |
National Group of Bioengineering (GNB) |
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2012 - present |
Italian Association of Clinical Engineering (AIIC) |
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2023 |
AIIC Awards: Selection by the scientific committee to present the scientific work “MINDTOOTH: EEG wearable system for real-time assessment of a patient's mental and emotional states in clinical settings” to the National Conference organized by the Italian Association of Clinical Engineering (AIIC), Florence, Italy. |
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2013 |
Finalist - Scientific Award "I Guidoniani" - Air Traffic Control Section for young researchers (medicine and science section, 2013), awarded by the Italian Association of Aeronautical and Space Medicine (AIMAS), for the work entitled "Brain-computer interface for online estimation of pilots' mental load". |
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2014 |
Award “Massimo Grattarola" (twelfth edition), Assigned by GNB. For originality and scientific value for the PhD thesis entitled: "Mental states passive monitoring through Brain-Computer Interface systems" |
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2014 |
Scientific Prize "I Guidoniani" - Section Air Traffic Control for young researchers (section Medicine and Science, 2014), awarded by the Association of Italian Aeronautical and Space Medicine (AIMAS), for the work entitled "Study of the mental load of the Comptroller Air Traffic during training missions phases to the simulator" |