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Neuromonitoring, Neuromodulation and Medical Informatics
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Neuromonitoring, Neuromodulation and Medical Informatics

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 23909

Special Issue Editors

Dr. Omid Kavehei

E-Mail Website
Guest Editor
School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia
Interests: neuromonitoring; multimodal neurosensing; closed-loop neuromodulation; responsive neuromodulation; critical care; biomedical Informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alistair McEwan

E-Mail Website
Co-Guest Editor
School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia
Interests: electrical properties of biological tissue; development of new devices to improve diagnosis and treatment of health problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Adaptive closed-loop neuromodulation and symptom advisory systems require reliable, real-time, and sometimes long-term and multimodal sensory systems. Access to such neuromonitoring technology could improve the outcome of therapeutic neurostimulation in several disorders, including complex neurological diseases. This Special Issue targets challenges in reliability, real-time signal processing, artifact masking, biomarker detection, identification and/or prediction in a range of technologies from implantable micro/nanodevices, circuits, and systems to wearable technologies and biomedical signal processing and control, with a focus on neuromonitoring.

Dr. Omid Kavehei
Guest Editor

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.

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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.

Keywords

  • neuromonitoring
  • multimodal neurosensing
  • closed-loop neuromodulation
  • responsive neuromodulation
  • critical care
  • biomedical informatics

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Published Papers (4 papers)

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Research

12 pages, 832 KiB  
Article
Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
by Alexander Miller, Jacqueline Huvanandana, Peter Jones, Heather Jeffery, Angela Carberry, Christine Slater and Alistair McEwan
Sensors 2021, 21(6), 2028; https://doi.org/10.3390/s21062028 - 12 Mar 2021
Viewed by 1863
Abstract
Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat [...] Read more.
Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3–24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3–24 months (all subjects), 0–6 months, and 7–24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings. Full article
(This article belongs to the Special Issue Neuromonitoring, Neuromodulation and Medical Informatics)
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20 pages, 2326 KiB  
Article
Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals
by Raquib-ul Alam, Haifeng Zhao, Andrew Goodwin, Omid Kavehei and Alistair McEwan
Sensors 2020, 20(21), 6285; https://doi.org/10.3390/s20216285 - 4 Nov 2020
Cited by 17 | Viewed by 5601
Abstract
There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed [...] Read more.
There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman–Harris window), EEG time window choices (−750 ms to 0 ms and −250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch’s method, Fast Fourier Transform, and Burg’s method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared. Full article
(This article belongs to the Special Issue Neuromonitoring, Neuromodulation and Medical Informatics)
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18 pages, 2086 KiB  
Article
Embodiment Is Related to Better Performance on a Brain–Computer Interface in Immersive Virtual Reality: A Pilot Study
by Julia M. Juliano, Ryan P. Spicer, Athanasios Vourvopoulos, Stephanie Lefebvre, Kay Jann, Tyler Ard, Emiliano Santarnecchi, David M. Krum and Sook-Lei Liew
Sensors 2020, 20(4), 1204; https://doi.org/10.3390/s20041204 - 22 Feb 2020
Cited by 49 | Viewed by 7102
Abstract
Electroencephalography (EEG)-based brain–computer interfaces (BCIs) for motor rehabilitation aim to “close the loop” between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays [...] Read more.
Electroencephalography (EEG)-based brain–computer interfaces (BCIs) for motor rehabilitation aim to “close the loop” between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants’ performance on the BCI was similar between display conditions, the participants’ reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens. Full article
(This article belongs to the Special Issue Neuromonitoring, Neuromodulation and Medical Informatics)
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22 pages, 3166 KiB  
Article
AttentivU: An EEG-Based Closed-Loop Biofeedback System for Real-Time Monitoring and Improvement of Engagement for Personalized Learning
by Nataliya Kosmyna and Pattie Maes
Sensors 2019, 19(23), 5200; https://doi.org/10.3390/s19235200 - 27 Nov 2019
Cited by 40 | Viewed by 8594
Abstract
Information about a person’s engagement and attention might be a valuable asset in many settings including work situations, driving, and learning environments. To this end, we propose the first prototype of a device called AttentivU—a system that uses a wearable system which consists [...] Read more.
Information about a person’s engagement and attention might be a valuable asset in many settings including work situations, driving, and learning environments. To this end, we propose the first prototype of a device called AttentivU—a system that uses a wearable system which consists of two main components. Component 1 is represented by an EEG headband used to measure the engagement of a person in real-time. Component 2 is a scarf, which provides subtle, haptic feedback (vibrations) in real-time when the drop in engagement is detected. We tested AttentivU in two separate studies with 48 adults. The participants were engaged in a learning scenario of either watching three video lectures on different subjects or participating in a set of three face-to-face lectures with a professor. There were three conditions administrated during both studies: (1) biofeedback, meaning the scarf (component 2 of the system) was vibrating each time the EEG headband detected a drop in engagement; (2) random feedback, where the vibrations did not correlate or depend on the engagement level detected by the system, and (3) no feedback, when no vibrations were administered. The results show that the biofeedback condition redirected the engagement of the participants to the task at hand and improved their performance on comprehension tests. Full article
(This article belongs to the Special Issue Neuromonitoring, Neuromodulation and Medical Informatics)
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