Pietro Arico'
Pietro Aricò is an Associate Professor in Biomedical Engineering at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome. He received his Master’s degree in Biomedical Engineering from Sapienza University (2010) and his Ph.D. in Bioengineering from the University of Bologna (2014). His research focuses on brain–computer interfaces (BCI), with a particular emphasis on passive BCIs, which exploit biosignal processing (EEG, ECG, GSR) to assess cognitive and emotional states in real-world applications.
Recognized internationally for his contributions to neuroscience and machine learning applied to BCIs, Professor Aricò has coordinated and participated in numerous national and European research projects (Horizon 2020, FP7, Italian Ministries). He has authored more than 120 peer-reviewed journal articles, holds a patent, and has presented his work at major international conferences (link to publications).
In 2024, he was included in the “Top 2% Scientists” ranking compiled by Stanford University and Elsevier, which identifies the world’s most influential researchers based on standardized bibliometric indicators.
He serves as Associate Editor for leading international journals such as IEEE Transactions on Biomedical Engineering (TBME) and as reviewer and guest editor for several others, including Frontiers in Neuroinformatics and the Journal of Neural Engineering. He also lectures in the Master’s programs in Biomedical Engineering and Medicine & Surgery HT at Sapienza University.
Beyond academia, he is Chief Technology Officer (CTO) and Project Manager at BrainSigns, where he oversees the translation of neurotechnology 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 field of applied neuroengineering.
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" |