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Capotorto R, Ronca V, Sciaraffa N, Borghini G, Di Flumeri G, Mezzadri L, Vozzi A, Giorgi A, Germano D, Babiloni F, Aricò P. Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment. Front Neuroinform 2024; 18:1409322. [PMID: 39376698 PMCID: PMC11457730 DOI: 10.3389/fninf.2024.1409322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 08/23/2024] [Indexed: 10/09/2024] Open
Abstract
Introduction In operational environments, human interaction and cooperation between individuals are critical to efficiency and safety. These states are influenced by individuals' cognitive and emotional states. Human factor research aims to objectively quantify these states to prevent human error and maintain constant performances, particularly in high-risk settings such as aviation, where human error and performance account for a significant portion of accidents. Methods Thus, this study aimed to evaluate and validate two novel methods for assessing the degree of cooperation among professional pilots engaged in real-flight simulation tasks. In addition, the study aimed to assess the ability of the proposed metrics to differentiate between the expertise levels of operating crews based on their levels of cooperation. Eight crews were involved in the experiments, consisting of four crews of Unexperienced pilots and four crews of Experienced pilots. An expert trainer, simulating air traffic management communication on one side and acting as a subject matter expert on the other, provided external evaluations of the pilots' mental states during the simulation. The two novel approaches introduced in this study were formulated based on circular correlation and mutual information techniques. Results and discussion The findings demonstrated the possibility of quantifying cooperation levels among pilots during realistic flight simulations. In addition, cooperation time is found to be significantly higher (p < 0.05) among Experienced pilots compared to Unexperienced ones. Furthermore, these preliminary results exhibited significant correlations (p < 0.05) with subjective and behavioral measures collected every 30 s during the task, confirming their reliability.
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Affiliation(s)
- Rossella Capotorto
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, Rome, Italy
| | - Vincenzo Ronca
- BrainSigns srl, Rome, Italy
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, University of Rome “Sapienza”, Rome, Italy
| | | | - Gianluca Borghini
- BrainSigns srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Gianluca Di Flumeri
- BrainSigns srl, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | | | | | | | - Daniele Germano
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, University of Rome “Sapienza”, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Fabio Babiloni
- BrainSigns srl, Rome, Italy
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
- Department of Computer Science, Hangzhou Dianzi University, Hangzhou, China
| | - Pietro Aricò
- BrainSigns srl, Rome, Italy
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, University of Rome “Sapienza”, Rome, Italy
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Demirezen G, Taşkaya Temizel T, Brouwer AM. Reproducible machine learning research in mental workload classification using EEG. FRONTIERS IN NEUROERGONOMICS 2024; 5:1346794. [PMID: 38660590 PMCID: PMC11039816 DOI: 10.3389/fnrgo.2024.1346794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/22/2024] [Indexed: 04/26/2024]
Abstract
This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.
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Affiliation(s)
- Güliz Demirezen
- Department of Information Systems, Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Tuğba Taşkaya Temizel
- Department of Data Informatics, Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Anne-Marie Brouwer
- Human Performance, Netherlands Organisation for Applied Scientific Research (TNO), Soesterberg, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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3
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Clemente L, La Rocca M, Paparella G, Delussi M, Tancredi G, Ricci K, Procida G, Introna A, Brunetti A, Taurisano P, Bevilacqua V, de Tommaso M. Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2329. [PMID: 38610540 PMCID: PMC11014209 DOI: 10.3390/s24072329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/03/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.
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Affiliation(s)
- Livio Clemente
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Marianna La Rocca
- Interateneo Department of Fisica ‘M. Merlin’, University of Bari, 70125 Bari, Italy;
- Laboratory of Neuroimaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Giulia Paparella
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Marianna Delussi
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Giusy Tancredi
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Katia Ricci
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Giuseppe Procida
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Alessandro Introna
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Antonio Brunetti
- Electrical and Information Engineering Department, Polytechnic of Bari, 70125 Bari, Italy; (A.B.); (V.B.)
| | - Paolo Taurisano
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Vitoantonio Bevilacqua
- Electrical and Information Engineering Department, Polytechnic of Bari, 70125 Bari, Italy; (A.B.); (V.B.)
| | - Marina de Tommaso
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
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Degirmenci M, Yuce YK, Perc M, Isler Y. EEG-based finger movement classification with intrinsic time-scale decomposition. Front Hum Neurosci 2024; 18:1362135. [PMID: 38505099 PMCID: PMC10948500 DOI: 10.3389/fnhum.2024.1362135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/15/2024] [Indexed: 03/21/2024] Open
Abstract
Introduction Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals. Methods In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not. Results As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems. Discussion When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).
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Affiliation(s)
- Murside Degirmenci
- Department of Biomedical Technologies, Izmir Katip Celebi University, Izmir, Türkiye
| | - Yilmaz Kemal Yuce
- Department of Computer Engineering, Alanya Alaaddin Keykubat University, Alanya, Antalya, Türkiye
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Complexity Science Hub Vienna, Vienna, Austria
- Department of Physics, Kyung Hee University, Seoul, Republic of Korea
| | - Yalcin Isler
- Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir, Türkiye
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Sun Y, Kargarandehkordi A, Slade C, Jaiswal A, Busch G, Guerrero A, Phillips KT, Washington P. Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study. JMIR Res Protoc 2024; 13:e46493. [PMID: 38324375 PMCID: PMC10882478 DOI: 10.2196/46493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-powered digital therapies that detect methamphetamine cravings via consumer devices have the potential to reduce health care disparities by providing remote and accessible care solutions to communities with limited care solutions, such as Native Hawaiian, Filipino, and Pacific Islander communities. However, Native Hawaiian, Filipino, and Pacific Islander communities are understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other racial groups. OBJECTIVE In this study, we aimed to understand the feasibility of continuous remote digital monitoring and ecological momentary assessments in Native Hawaiian, Filipino, and Pacific Islander communities in Hawaii by curating a novel data set of longitudinal Fitbit (Fitbit Inc) biosignals with the corresponding craving and substance use labels. We also aimed to develop personalized AI models that predict methamphetamine craving events in real time using wearable sensor data. METHODS We will develop personalized AI and machine learning models for methamphetamine use and craving prediction in 40 individuals from Native Hawaiian, Filipino, and Pacific Islander communities by curating a novel data set of real-time Fitbit biosensor readings and the corresponding participant annotations (ie, raw self-reported substance use data) of their methamphetamine use and cravings. In the process of collecting this data set, we will gain insights into cultural and other human factors that can challenge the proper acquisition of precise annotations. With the resulting data set, we will use self-supervised learning AI approaches, which are a new family of machine learning methods that allows a neural network to be trained without labels by being optimized to make predictions about the data. The inputs to the proposed AI models are Fitbit biosensor readings, and the outputs are predictions of methamphetamine use or craving. This paradigm is gaining increased attention in AI for health care. RESULTS To date, more than 40 individuals have expressed interest in participating in the study, and we have successfully recruited our first 5 participants with minimal logistical challenges and proper compliance. Several logistical challenges that the research team has encountered so far and the related implications are discussed. CONCLUSIONS We expect to develop models that significantly outperform traditional supervised methods by finetuning according to the data of a participant. Such methods will enable AI solutions that work with the limited data available from Native Hawaiian, Filipino, and Pacific Islander populations and that are inherently unbiased owing to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46493.
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Affiliation(s)
- Yinan Sun
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Ali Kargarandehkordi
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Christopher Slade
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Aditi Jaiswal
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Gerald Busch
- Department of Psychiatry, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Anthony Guerrero
- Department of Psychiatry, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Kristina T Phillips
- Center for Integrated Health Care Research, Kaiser Permanente Hawaii, Honolulu, HI, United States
| | - Peter Washington
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
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Giorgi A, Ronca V, Vozzi A, Aricò P, Borghini G, Capotorto R, Tamborra L, Simonetti I, Sportiello S, Petrelli M, Polidori C, Varga R, van Gasteren M, Barua A, Ahmed MU, Babiloni F, Di Flumeri G. Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving. Front Neurorobot 2023; 17:1240933. [PMID: 38107403 PMCID: PMC10721973 DOI: 10.3389/fnbot.2023.1240933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/18/2023] [Indexed: 12/19/2023] Open
Abstract
The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their "surroundings." However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its "surroundings" but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.
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Affiliation(s)
- Andrea Giorgi
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy
- BrainSigns SRL, Rome, Italy
| | - Vincenzo Ronca
- BrainSigns SRL, Rome, Italy
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Alessia Vozzi
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy
- BrainSigns SRL, Rome, Italy
| | - Pietro Aricò
- BrainSigns SRL, Rome, Italy
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Gianluca Borghini
- BrainSigns SRL, Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Rossella Capotorto
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Luca Tamborra
- BrainSigns SRL, Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Ilaria Simonetti
- BrainSigns SRL, Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Simone Sportiello
- Department of Civil Engineering, Computer Science and Aeronautical Technologies, Roma Tre University, Rome, Italy
- Department of Enterprise Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Marco Petrelli
- Department of Civil Engineering, Computer Science and Aeronautical Technologies, Roma Tre University, Rome, Italy
| | - Carlo Polidori
- Italian Association of Road Safety Professionals (AIPSS), Rome, Italy
| | - Rodrigo Varga
- Instituto Tecnologico de Castilla y Leon, Burgos, Spain
| | | | - Arnab Barua
- Academy for Innovation, Design and Technology, Mälardalens University, Västerås, Sweden
| | - Mobyen Uddin Ahmed
- Academy for Innovation, Design and Technology, Mälardalens University, Västerås, Sweden
| | - Fabio Babiloni
- BrainSigns SRL, Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Gianluca Di Flumeri
- BrainSigns SRL, Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
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Di Flumeri G, Giorgi A, Germano D, Ronca V, Vozzi A, Borghini G, Tamborra L, Simonetti I, Capotorto R, Ferrara S, Sciaraffa N, Babiloni F, Aricò P. A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees. SENSORS (BASEL, SWITZERLAND) 2023; 23:8389. [PMID: 37896483 PMCID: PMC10610858 DOI: 10.3390/s23208389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
When assessing trainees' progresses during a driving training program, instructors can only rely on the evaluation of a trainee's explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one's mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver's subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events.
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Affiliation(s)
- Gianluca Di Flumeri
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy; (D.G.); (G.B.); (R.C.); (F.B.)
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
| | - Andrea Giorgi
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Daniele Germano
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy; (D.G.); (G.B.); (R.C.); (F.B.)
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
| | - Vincenzo Ronca
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
| | - Alessia Vozzi
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Gianluca Borghini
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy; (D.G.); (G.B.); (R.C.); (F.B.)
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
| | - Luca Tamborra
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Ilaria Simonetti
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Rossella Capotorto
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy; (D.G.); (G.B.); (R.C.); (F.B.)
| | - Silvia Ferrara
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
| | - Nicolina Sciaraffa
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
| | - Fabio Babiloni
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy; (D.G.); (G.B.); (R.C.); (F.B.)
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Pietro Aricò
- BrainSigns srl, 00198 Rome, Italy; (A.G.); (V.R.); (A.V.); (L.T.); (I.S.); (S.F.); (N.S.); (P.A.)
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
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Washington P. Personalized Machine Learning using Passive Sensing and Ecological Momentary Assessments for Meth Users in Hawaii: A Research Protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.24.23294587. [PMID: 37662253 PMCID: PMC10473804 DOI: 10.1101/2023.08.24.23294587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Artificial intelligence (AI)-powered digital therapies which detect meth cravings delivered on consumer devices have the potential to reduce these disparities by providing remote and accessible care solutions to Native Hawaiians, Filipinos, and Pacific Islanders (NHFPI) communities with limited care solutions. However, NHFPI are fully understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other races. Objective We seek to fulfill two research aims: (1) Understand the feasibility of continuous remote digital monitoring and ecological momentary assessments (EMAs) in NHFPI in Hawaii by curating a novel dataset of longitudinal FitBit biosignals with corresponding craving and substance use labels. (2) Develop personalized AI models which predict meth craving events in real time using wearable sensor data. Methods We will develop personalized AI/ML (artificial intelligence/machine learning) models for meth use and craving prediction in 40 NHFPI individuals by curating a novel dataset of real-time FitBit biosensor readings and corresponding participant annotations (i.e., raw self-reported substance use data) of their meth use and cravings. In the process of collecting this dataset, we will glean insights about cultural and other human factors which can challenge the proper acquisition of precise annotations. With the resulting dataset, we will employ self-supervised learning (SSL) AI approaches, which are a new family of ML methods that allow a neural network to be trained without labels by being optimized to make predictions about the data itself. The inputs to the proposed AI models are FitBit biosensor readings and the outputs are predictions of meth use or craving. This paradigm is gaining increased attention in AI for healthcare. Conclusions We expect to develop models which significantly outperform traditional supervised methods by fine-tuning to an individual subject's data. Such methods will enable AI solutions which work with the limited data available from NHFPI populations and which are inherently unbiased due to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse.
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Vozzi A, Martinez Levy A, Ronca V, Giorgi A, Ferrara S, Mancini M, Capotorto R, Cherubino P, Trettel A, Babiloni F, Di Flumeri G. Time-Dependent Analysis of Human Neurophysiological Activities during an Ecological Olfactory Experience. Brain Sci 2023; 13:1242. [PMID: 37759843 PMCID: PMC10526851 DOI: 10.3390/brainsci13091242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
It has been demonstrated that odors could affect humans at the psychophysiological level. Significant research has been done on odor perception and physiological mechanisms; however, this research was mainly performed in highly controlled conditions in order to highlight the perceptive phenomena and the correlated physiological responses in the time frame of milliseconds. The present study explored how human physiological activity evolves in response to different odor conditions during an ecological olfactory experience on a broader time scale (from 1 to 90 s). Two odors, vanilla and menthol, together with a control condition (blank) were employed as stimuli. Electroencephalographic (EEG) activity in four frequency bands of interest, theta, alpha, low beta, and high beta, and the electrodermal activity (EDA) of the skin conductance level and response (SCL and SCR) were investigated at five time points taken during: (i) the first ten seconds of exposure (short-term analysis) and (ii) throughout the entire exposure to each odor (90 s, long-term analysis). The results revealed significant interactions between the odor conditions and the time periods in the short-term analysis for the overall frontal activity in the theta (p = 0.03), alpha (p = 0.005), and low beta (p = 0.0067) bands, the frontal midline activity in the alpha (p = 0.015) and low beta (p = 0.02) bands, and the SCR component (p = 0.024). For the long-term effects, instead, only one EEG parameter, frontal alpha asymmetry, was significantly sensitive to the considered dimensions (p = 0.037). In conclusion, the present research determined the physiological response to different odor conditions, also demonstrating the sensitivity of the employed parameters in characterizing the dynamic of such response during the time. As an exploratory study, this work points out the relevance of considering the effects of continuous exposure instead of short stimulation when evaluating the human olfactory experience, providing insights for future studies in the field.
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Affiliation(s)
- Alessia Vozzi
- Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
| | - Ana Martinez Levy
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Vincenzo Ronca
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy
| | - Andrea Giorgi
- Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
| | - Silvia Ferrara
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
| | - Marco Mancini
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
| | - Rossella Capotorto
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy
| | - Patrizia Cherubino
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Arianna Trettel
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
| | - Fabio Babiloni
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- Department of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Gianluca Di Flumeri
- BrainSigns Srl, Via Tirso, 14, 00198 Rome, Italy (F.B.); (G.D.F.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
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Simonetti I, Tamborra L, Giorgi A, Ronca V, Vozzi A, Aricò P, Borghini G, Sciaraffa N, Trettel A, Babiloni F, Picardi M, Di Flumeri G. Neurophysiological Evaluation of Students' Experience during Remote and Face-to-Face Lessons: A Case Study at Driving School. Brain Sci 2023; 13:95. [PMID: 36672076 PMCID: PMC9856302 DOI: 10.3390/brainsci13010095] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Nowadays, fostered by technological progress and contextual circumstances such as the economic crisis and pandemic restrictions, remote education is experiencing growing deployment. However, this growth has generated widespread doubts about the actual effectiveness of remote/online learning compared to face-to-face education. The present study was aimed at comparing face-to-face and remote education through a multimodal neurophysiological approach. It involved forty students at a driving school, in a real classroom, experiencing both modalities. Wearable devices to measure brain, ocular, heart and sweating activities were employed in order to analyse the students' neurophysiological signals to obtain insights into the cognitive dimension. In particular, four parameters were considered: the Eye Blink Rate, the Heart Rate and its Variability and the Skin Conductance Level. In addition, the students filled out a questionnaire at the end to obtain an explicit measure of their learning performance. Data analysis showed higher cognitive activity, in terms of attention and mental engagement, in the in-presence setting compared to the remote modality. On the other hand, students in the remote class felt more stressed, particularly during the first part of the lesson. The analysis of questionnaires demonstrated worse performance for the remote group, thus suggesting a common "disengaging" behaviour when attending remote courses, thus undermining their effectiveness. In conclusion, neuroscientific tools could help to obtain insights into mental concerns, often "blind", such as decreasing attention and increasing stress, as well as their dynamics during the lesson itself, thus allowing the definition of proper countermeasures to emerging issues when introducing new practices into daily life.
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Affiliation(s)
- Ilaria Simonetti
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Luca Tamborra
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Andrea Giorgi
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Vincenzo Ronca
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Alessia Vozzi
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, 00161 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | | | - Gianluca Borghini
- BrainSigns srl, 00198 Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy
| | | | | | - Fabio Babiloni
- BrainSigns srl, 00198 Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | | | - Gianluca Di Flumeri
- BrainSigns srl, 00198 Rome, Italy
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy
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Galiotta V, Quattrociocchi I, D'Ippolito M, Schettini F, Aricò P, Sdoia S, Formisano R, Cincotti F, Mattia D, Riccio A. EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review. Front Hum Neurosci 2022; 16:1040816. [PMID: 36545350 PMCID: PMC9760911 DOI: 10.3389/fnhum.2022.1040816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 11/17/2022] [Indexed: 12/11/2022] Open
Abstract
Background Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs). Objectives The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI. Methods The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient. Results Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients. Conclusion Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.
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Affiliation(s)
- Valentina Galiotta
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Ilaria Quattrociocchi
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy
| | - Mariagrazia D'Ippolito
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,*Correspondence: Mariagrazia D'Ippolito
| | - Francesca Schettini
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Pietro Aricò
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy,Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy,BrainSigns srl, Rome, Italy
| | - Stefano Sdoia
- Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Rita Formisano
- Neurorehabilitation 2 and Post-Coma Unit, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Febo Cincotti
- Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Angela Riccio
- Neuroelectric Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia (IRCCS), Rome, Italy,Servizio di Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
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