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Monitoring and Sensing in Neuroscience

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

Deadline for manuscript submissions: closed (29 June 2023) | Viewed by 20117

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Dr. Mario Ortiz García

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Brain-Machine Interface Systems Lab, Institute of Research on Engineering of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
Interests: mathematical transforms applied to electrical signal processing; neural network applications for signal classification; brain–machine interfaces and neuro-robotics
Special Issues, Collections and Topics in MDPI journals

Dr. José M. Azorín

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Guest Editor

Brain-Machine Interface Systems Lab, Institute of Research on Engineering of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
Interests: brain–machine interfaces; neuro-robotics; rehabilitation robotics
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Dr. Eduardo Iáñez Martínez

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Guest Editor

Brain-Machine Interface Systems Lab, Institute of Research on Engineering of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
Interests: brain–computer interfaces (BCIs) (non-invasive brain interfaces); multimodal human–robot interfaces (integrating brain, ocular and haptic information)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain processes are still one of the greatest mysteries of the human body. The monitoring of brain signals can be used for medical diagnosis or, thanks to brain–computer interfaces, interpreted by a computer for the development of new applications in neuroscience. This Special Issue will focus on the different technologies used for monitoring and sensing in neuroscience.

The scope of the Special Issue will cover not only new developments, but also derived applications of the recording and decoding of different brain signals. The objective is to collect highly innovative research contributions that present new developments in the sensing of brain signals, the mitigation of artifacts, the testing of different applications for brain–computer/machine interfaces (BCIs/BMIs) and new decoding algorithms.

Original papers describing completed and unpublished works that are not currently under review by any other journal, magazine or conference, are solicited.

Dr. Mario Ortiz García
Dr. José M. Azorín
Dr. Eduardo Iáñez Martínez
Guest Editors

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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.

Keywords

neuroscience
brain–computer interfaces (BCIs)
brain–machine interfaces (BMIs)
electroencephalography (EEG)
functional near-infrared spectroscopy (fNIRS)
neurorobotics
neuro engineering
neuroinformatics
applications and case studies

Published Papers (6 papers)

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Research

13 pages, 837 KiB

Open AccessArticle

Perceptual Integration Compensates for Attention Deficit in Elderly during Repetitive Auditory-Based Sensorimotor Task

by Nikita Frolov, Elena Pitsik, Vadim Grubov, Artem Badarin, Vladimir Maksimenko, Alexander Zakharov, Semen Kurkin and Alexander Hramov

Sensors 2023, 23(14), 6420; https://doi.org/10.3390/s23146420 - 14 Jul 2023

Cited by 6 | Viewed by 810

Abstract

Sensorimotor integration (SI) brain functions that are vital for everyday life tend to decline in advanced age. At the same time, elderly people preserve a moderate level of neuroplasticity, which allows the brain’s functionality to be maintained and slows down the process of neuronal degradation. Hence, it is important to understand which aspects of SI are modifiable in healthy old age. The current study focuses on an auditory-based SI task and explores: (i) if the repetition of such a task can modify neural activity associated with SI, and (ii) if this effect is different in young and healthy old age. A group of healthy older subjects and young controls underwent an assessment of the whole-brain electroencephalography (EEG) while repetitively executing a motor task cued by the auditory signal. Using EEG spectral power and functional connectivity analyses, we observed a differential age-related modulation of theta activity throughout the repetition of the SI task. Growth of the anterior stimulus-related theta oscillations accompanied by enhanced right-lateralized frontotemporal phase-locking was found in elderly adults. Their young counterparts demonstrated a progressive increase in prestimulus occipital theta power. Our results suggest that the short-term repetition of the auditory-based SI task modulates sensory processing in the elderly. Older participants most likely progressively improve perceptual integration rather than attention-driven processing compared to their younger counterparts. Full article

(This article belongs to the Special Issue Monitoring and Sensing in Neuroscience)

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26 pages, 10825 KiB

Open AccessArticle

Design and Validation of a Low-Cost Mobile EEG-Based Brain–Computer Interface

by Alexander Craik, Juan José González-España, Ayman Alamir, David Edquilang, Sarah Wong, Lianne Sánchez Rodríguez, Jeff Feng, Gerard E. Francisco and Jose L. Contreras-Vidal

Sensors 2023, 23(13), 5930; https://doi.org/10.3390/s23135930 - 26 Jun 2023

Cited by 4 | Viewed by 9721

Abstract

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user’s hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal–to–noise ratio (SNR) and common–mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device’s use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications. Full article

(This article belongs to the Special Issue Monitoring and Sensing in Neuroscience)

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19 pages, 9351 KiB

Open AccessArticle

Design and Evaluation of a Potential Non-Invasive Neurostimulation Strategy for Treating Persistent Anosmia in Post-COVID-19 Patients

by Desirée I. Gracia, Mario Ortiz, Tatiana Candela, Eduardo Iáñez, Rosa M. Sánchez, Carmina Díaz and José M. Azorín

Sensors 2023, 23(13), 5880; https://doi.org/10.3390/s23135880 - 25 Jun 2023

Cited by 1 | Viewed by 1694

Abstract

A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world’s population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection. Full article

(This article belongs to the Special Issue Monitoring and Sensing in Neuroscience)

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21 pages, 8696 KiB

Open AccessArticle

Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery

by Semen Kurkin, Susanna Gordleeva, Andrey Savosenkov, Nikita Grigorev, Nikita Smirnov, Vadim V. Grubov, Anna Udoratina, Vladimir Maksimenko, Victor Kazantsev and Alexander E. Hramov

Sensors 2023, 23(10), 4661; https://doi.org/10.3390/s23104661 - 11 May 2023

Cited by 7 | Viewed by 3201

Abstract

Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study. Participants were randomly assigned to receive sham (15 subjects) or real high-frequency rTMS (15 subjects). We performed EEG sensor-level, source-level, and connectivity analyses to evaluate the rTMS effects. We revealed that excitatory stimulation of the left DLPFC increases theta-band power in the right precuneus (PrecuneusR) via the functional connectivity between them. The precuneus theta-band power negatively correlates with the latency of the MI response, so the rTMS speeds up the responses in 50% of participants. We suppose that posterior theta-band power reflects attention modulation of sensory processing; therefore, high power may indicate attentive processing and cause faster responses. Full article

(This article belongs to the Special Issue Monitoring and Sensing in Neuroscience)

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22 pages, 1754 KiB

Open AccessArticle

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

by Amad Zafar, Shaik Javeed Hussain, Muhammad Umair Ali and Seung Won Lee

Sensors 2023, 23(7), 3714; https://doi.org/10.3390/s23073714 - 3 Apr 2023

Cited by 9 | Viewed by 2260

Abstract

In recent decades, the brain–computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset’s dimensionality, increase the computing effectiveness, and enhance the BCI’s performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications. Full article

(This article belongs to the Special Issue Monitoring and Sensing in Neuroscience)

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15 pages, 437 KiB

Open AccessArticle

Preliminary Evidence of EEG Connectivity Changes during Self-Objectification of Workers

by Irma N. Angulo-Sherman, Annel Saavedra-Hernández, Natalia E. Urbina-Arias, Zahamara Hernández-Granados and Mario Sainz

Sensors 2022, 22(20), 7906; https://doi.org/10.3390/s22207906 - 17 Oct 2022

Viewed by 1564

Abstract

Economic objectification is a form of dehumanization in which workers are treated as tools for enhancing productivity. It can lead to self-objectification in the workplace, which is when people perceive themselves as instruments for work. This can cause burnout, emotional drain, and a modification of self-perception that involves a loss of human attributes such as emotions and reasoning while focusing on others’ perspectives for evaluating the self. Research on workers self-objectification has mainly analyzed the consequences of this process without exploring the brain activity that underlies the individual’s experiences of self-objectification. Thus, this project explores the electroencephalographic (EEG) changes that occur in participants during an economic objectifying task that resembled a job in an online store. After the task, a self-objectification questionnaire was applied and its resulting index was used to label the participants as self-objectified or non-self-objectified. The changes over time in EEG event-related synchronization (ERS) and partial directed coherence (PDC) were calculated and compared between the self-objectification groups. The results show that the main differences between the groups in ERS and PDC occurred in the beta and gamma frequencies, but only the PDC results correlated with the self-objectification group. These results provide information for further understanding workers’ self-objectification. These EEG changes could indicate that economic self-objectification is associated with changes in vigilance, boredom, and mind-wandering. Full article

(This article belongs to the Special Issue Monitoring and Sensing in Neuroscience)

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