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Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering: Extended Papers from the 2022 IEEE MetroXRAINE Conference

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 7039

Special Issue Editors


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Guest Editor
Department of Engineering for Innovation, University of Salento, Lecce, Italy
Interests: brain-computer interfaces; assessment of cognitive and emotional processes; bioimpedance spectroscopy; machine learning; applied metrology

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Guest Editor

Special Issue Information

Dear Colleagues,

The 2022 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) will be held from 26 to 28 October 2022 in Rome, Italy. The conference website can be found at https://metroxraine.org/

IEEE MetroXRAINE 2022 will be an international event, mainly aiming to create synergy between experts in the fields of augmented, virtual and mixed reality, brain–computer interfaces, and advanced processing techniques such as machine learning and deep learning, with particular attention being paid to measurements and other metrological aspects.

The conference will be a unique opportunity for discussion among scientists, technologists, and companies in very specific sectors in order to increase the visibility and the scientific impact for the participants. The organizing formula will be original, owing to the emphasis on the interaction between the participants to exchange ideas and material useful for their research activities.

MetroXRAINE will be configured as a synergistic collection of sessions organized by the individual members of the Scientific Committee.

This Special Issue of Sensors (IF: 3.576, ISSN 1424-8220) will collect a number of outstanding papers presented at the WCE. Conference participants are invited to submit extended versions of their conference papers to this Special Issue.

Potential topics include, but are not limited to, the following:

  • Instrumental solutions and measurement principles for enhancing the accuracy and robustness of XR-BCI systems;
  • Display technologies and human vision;
  • Wearable sensors in extended reality and neuroimaging;
  • User experience, perception, and interactions in XR and BCI;
  • Multisensory experiences and improved immersion;
  • Psychophysical condition monitoring;
  • Advanced machine learning techniques for XR-BCI;
  • Deep-learning-based classification;
  • VR-supported mindfulness based on EEG signals;
  • Immersive user experience with XR-BCI;
  • Human-in-the-loop AI;
  • Bioengineering and rehabilitation;
  • Biosignal processing;
  • Nondestructive testing (NDT) methods, technologies, and systems;
  • Advanced and novel sensor types and actuator types;
  • Sensor data fusion;
  • Intelligent sensors and sensor networks;
  • Digital twins;
  • Industry 4.0;
  • Measurement methods, technologies, and systems.

Dr. Nicola Moccaldi
Dr. Antonio Esposito
Prof. Dr. Egidio De Benedetto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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17 pages, 835 KiB  
Article
Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System
by Pasquale Arpaia, Damien Coyle, Antonio Esposito, Angela Natalizio, Marco Parvis, Marisa Pesola and Ersilia Vallefuoco
Sensors 2023, 23(13), 5836; https://doi.org/10.3390/s23135836 - 23 Jun 2023
Viewed by 1249
Abstract
The present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the [...] Read more.
The present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a “neurofeedback” group, which performed motor imagery while receiving feedback, and a “control” group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual’s ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain–computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation. Full article
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19 pages, 7975 KiB  
Article
Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors
by Kathiravan Thangavel, Dario Spiller, Roberto Sabatini, Stefania Amici, Nicolas Longepe, Pablo Servidia, Pier Marzocca, Haytham Fayek and Luigi Ansalone
Sensors 2023, 23(6), 3344; https://doi.org/10.3390/s23063344 - 22 Mar 2023
Cited by 10 | Viewed by 2167
Abstract
Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission [...] Read more.
Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations. Full article
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Review

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37 pages, 1708 KiB  
Review
A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder
by Pasquale Arpaia, Attilio Covino, Loredana Cristaldi, Mirco Frosolone, Ludovica Gargiulo, Francesca Mancino, Federico Mantile and Nicola Moccaldi
Sensors 2022, 22(13), 4934; https://doi.org/10.3390/s22134934 - 29 Jun 2022
Cited by 6 | Viewed by 2708
Abstract
A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as [...] Read more.
A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as ADHD. The Quality Assessment Tool for Quantitative Studies (QATQS) and field-weighted citation impact metric (Scopus) were used to assess the methodological rigor of the studies and their impact on the scientific community, respectively. One hundred and one articles, concerning the diagnostics and therapy of ADHD children aged from 8 to 14, were collected. Event-related potential components were mainly exploited for executive functions related to the cluster inhibition, whereas band power spectral density is the most considered EEG feature for executive functions related to the cluster working memory. This review identifies the most used (also by rigorous and relevant articles) EEG signal processing strategies for executive function assessment in ADHD. Full article
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