Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
3.8
Journals
Bioengineering
Special Issues
Neuroimaging Techniques for Wearable Devices in Bioengineering
share announcement

Neuroimaging Techniques for Wearable Devices in Bioengineering

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 8294

Special Issue Editors

Dr. Luis Coelho

E-Mail Website
Guest Editor
ISEP—School of Engineering, Polytechnic of Porto, Porto, Portugal
Interests: physiological signal processing; image processing; machine learning; neurodegenerative diseases; virtual reality
Special Issues, Collections and Topics in MDPI journals
Dr. Dorota Kamińska

E-Mail
Co-Guest Editor
Institute of Mechatronics and Information Systems, Lodz University of Technology, 90-924 Lodz, Poland
Interests: human behavior analysis; affective computing; universal design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a Special Issue dedicated to a rapidly evolving field of bioengineering related to neuroimaging techniques for wearable devices. The integration of neuroimaging with wearable technology has the potential to revolutionize brain monitoring, cognitive assessment, and real-world applications. This Special Issue aims to bring together researchers, engineers, and practitioners in bioengineering to share their latest findings, methodologies, and technologies in the exciting domains of neuroimaging for wearable devices.

We invite authors to submit their original research, review articles, and methodological papers related to, but not limited to, the following topics:

  • Wearable EEG Devices:
    1. Advances in wearable EEG technology and their applications;
    2. Real-time brain monitoring in daily life;
    3. Brain–computer interfaces (BCIs) and neurofeedback using wearable EEG;
    4. Clinical and neurorehabilitation applications of wearable EEG devices.
  • Wearable fNIRS Systems:
    1. Developments in portable fNIRS technology for wearable applications;
    2. Cognitive and emotional monitoring in real-world settings;
    3. Clinical use of wearable fNIRS in neurology, psychiatry, and neonatal care;
    4. Wearable fNIRS for enhancing human-computer interaction and neuroergonomics.
  • Wearable Multimodal Neuroimaging:
    1. Integration of EEG, fNIRS, or other neuroimaging modalities into wearable devices;
    2. Applications in neurofeedback, augmented reality, and cognitive load assessment;
    3. Innovations in wearable multimodal neuroimaging for a wide range of scenarios.
  • Signal Processing and Data Analysis for Wearable Neuroimaging:
    1. Methods for artifact correction and data analysis in wearable neuroimaging;
    2. Machine learning and data analytics for extracting meaningful insights from wearable neuroimaging data;
    3. Real-time and cloud-based data processing for continuous monitoring.

Dr. Luis Coelho
Dr. Dorota Kamińska
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • neuroimaging
  • wearable devices
  • brain monitoring
  • portable neuroimaging
  • EEG (electroencephalography)
  • fNIRS (functional near-infrared spectroscopy)
  • brain–computer interfaces (BCIs)
  • cognitive assessment
  • real-time neuroimaging
  • mobile brain monitoring
  • clinical applications
  • neurofeedback
  • multimodal neuroimaging
  • cognitive load assessment
  • signal processing
  • data analysis
  • wearable EEG
  • wearable fNIRS
  • continuous monitoring
  • human–computer interaction
  • neuroergonomics
  • artifact correction
  • machine learning
  • cloud-based data processing
  • brain health monitoring
  • digital health
  • mobile neuroscience
  • brain research technology
  • personalized medicine
  • neuromodulation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

8 pages, 4150 KiB  
Article
Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain
by Osamu Hiwaki
Bioengineering 2024, 11(9), 917; https://doi.org/10.3390/bioengineering11090917 - 13 Sep 2024
Viewed by 1642
Abstract
Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain–machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive [...] Read more.
Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain–machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain. Full article
(This article belongs to the Special Issue Neuroimaging Techniques for Wearable Devices in Bioengineering)
►▼ Show Figures

Graphical abstract

20 pages, 1977 KiB  
Article
Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm
by Tarannum Rahnuma, Sairamya Nanjappan Jothiraj, Vishal Kuvar, Myrthe Faber, Robert T. Knight and Julia W. Y. Kam
Bioengineering 2024, 11(8), 760; https://doi.org/10.3390/bioengineering11080760 - 27 Jul 2024
Cited by 2 | Viewed by 1657
Abstract
One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated [...] Read more.
One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal–external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts. Full article
(This article belongs to the Special Issue Neuroimaging Techniques for Wearable Devices in Bioengineering)
►▼ Show Figures

Figure 1

14 pages, 1848 KiB  
Article
A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG
by Xiaodong Li, Shuoheng Yang, Ningbo Fei, Junlin Wang, Wei Huang and Yong Hu
Bioengineering 2024, 11(6), 613; https://doi.org/10.3390/bioengineering11060613 - 15 Jun 2024
Cited by 1 | Viewed by 2132
Abstract
The application of wearable electroencephalogram (EEG) devices is growing in brain–computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state [...] Read more.
The application of wearable electroencephalogram (EEG) devices is growing in brain–computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum–convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices. Full article
(This article belongs to the Special Issue Neuroimaging Techniques for Wearable Devices in Bioengineering)
►▼ Show Figures

Graphical abstract

11 pages, 1891 KiB  
Article
Application of the Single Source—Detector Separation Algorithm in Wearable Neuroimaging Devices: A Step toward Miniaturized Biosensor for Hypoxia Detection
by Thien Nguyen, Soongho Park, Jinho Park, Asma Sodager, Tony George and Amir Gandjbakhche
Bioengineering 2024, 11(4), 385; https://doi.org/10.3390/bioengineering11040385 - 16 Apr 2024
Cited by 1 | Viewed by 2060
Abstract
Most currently available wearable devices to noninvasively detect hypoxia use the spatially resolved spectroscopy (SRS) method to calculate cerebral tissue oxygen saturation (StO2). This study applies the single source—detector separation (SSDS) algorithm to calculate StO2. Near-infrared spectroscopy (NIRS) data [...] Read more.
Most currently available wearable devices to noninvasively detect hypoxia use the spatially resolved spectroscopy (SRS) method to calculate cerebral tissue oxygen saturation (StO2). This study applies the single source—detector separation (SSDS) algorithm to calculate StO2. Near-infrared spectroscopy (NIRS) data were collected from 26 healthy adult volunteers during a breath-holding task using a wearable NIRS device, which included two source—detector separations (SDSs). These data were used to derive oxyhemoglobin (HbO) change and StO2. In the group analysis, both HbO change and StO2 exhibited significant change during a breath-holding task. Specifically, they initially decreased to minimums at around 10 s and then steadily increased to maximums, which were significantly greater than baseline levels, at 25–30 s (p-HbO < 0.001 and p-StO2 < 0.05). However, at an individual level, the SRS method failed to detect changes in cerebral StO2 in response to a short breath-holding task. Furthermore, the SSDS algorithm is more robust than the SRS method in quantifying change in cerebral StO2 in response to a breath-holding task. In conclusion, these findings have demonstrated the potential use of the SSDS algorithm in developing a miniaturized wearable biosensor to monitor cerebral StO2 and detect cerebral hypoxia. Full article
(This article belongs to the Special Issue Neuroimaging Techniques for Wearable Devices in Bioengineering)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop