Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Signal Processing Using Non-Invasive Physiological Sensors 2022
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Signal Processing Using Non-Invasive Physiological Sensors 2022

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

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 3909

Special Issue Editors

Dr. Imran Khan Niazi

E-Mail Website
Guest Editor
Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
Interests: neurorehabilitation; biomedical signal processing; machine learning; applied artificial intelligence; EEG; EMG
Special Issues, Collections and Topics in MDPI journals
Dr. Noman Naseer

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, Air University, Islamabad 44000, Pakistan
Interests: neurorobotics; brain–computer interface; machine learning; artificial intelligence; feature extraction; classification; fNIRS; EMG; EEG
Special Issues, Collections and Topics in MDPI journals
Dr. Hendrik Santosa

E-Mail Website
Guest Editor
Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
Interests: functional near-infrared spectroscopy (fNIRS) data processing; statistical analysis for fNIRS signal; multi-modal neuroimaging; brain-computer interface (BCI) applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to cordially invite you to participate in a Special Issue on “Signal Processing Using Non-Invasive Physiological Sensors”. This Special Issue shall concentrate on two aspects:

  1. Non-invasive biomedical sensors for the monitoring of physiological parameters from the human body. Today, a critical factor in providing a cost-effective healthcare system is improving the quality of life and mobility of patients, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home, or integrated into wearable devices for long-term data collection;
  2. Another factor which plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this Special Issue, we aim to attract researchers who are interested in the application of signal processing methods to different biomedical signals such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encourage new signal processing methods or the use of existing signal processing methods for novel applications in the domain of physiological signals to help healthcare providers to make better decisions.

Original papers that describe new research on any of the themes mentioned above are welcomed. We look forward to your participation in this Special Issue.

Dr. Imran Khan Niazi
Dr. Noman Naseer
Dr. Hendrik Santosa
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.

Keywords

  • biosensing
  • infrared detectors
  • portable technology
  • cutaneous temperature
  • EEG
  • EMG
  • ECG
  • fNIRS
  • applied signal processing
  • machine learning
  • artificial intelligence
  • real-world application
  • clinical environment
  • statistical analysis
  • multimodal neuroimaging
  • artifact detection and removal
  • noise removal
  • independent component analysis
  • signal filtering techniques

Published Papers (2 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

17 pages, 1735 KiB  
Article
Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills
by Mateo Tobón-Henao, Andrés Álvarez-Meza and Germán Castellanos-Domínguez
Sensors 2022, 22(15), 5771; https://doi.org/10.3390/s22155771 - 2 Aug 2022
Cited by 4 | Viewed by 1452
Abstract
The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data [...] Read more.
The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data resolution, and the inter- and intra-subject variability hamper the extraction of discriminant features. Indeed, subjects with poor motor skills have difficulties in practicing MI tasks against low SNR scenarios. Here, we propose a subject-dependent preprocessing approach that includes the well-known Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts based on the MI performance. In addition, power- and phase-based functional connectivity measures are studied to extract relevant and interpretable patterns and identify subjects of inefficency. As a result, our proposal, Subject-dependent Artifact Removal (SD-AR), improves the MI classification performance in subjects with poor motor skills. Consequently, electrooculography and volume-conduction EEG artifacts are mitigated within a functional connectivity feature-extraction strategy, which favors the classification performance of a straightforward linear classifier. Full article
(This article belongs to the Special Issue Signal Processing Using Non-Invasive Physiological Sensors 2022)
►▼ Show Figures

Figure 1

17 pages, 2223 KiB  
Article
Improved Cognitive Vigilance Assessment after Artifact Reduction with Wavelet Independent Component Analysis
by Nadia Abu Farha, Fares Al-Shargie, Usman Tariq and Hasan Al-Nashash
Sensors 2022, 22(8), 3051; https://doi.org/10.3390/s22083051 - 15 Apr 2022
Cited by 5 | Viewed by 1838
Abstract
Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can [...] Read more.
Vigilance level assessment is of prime importance to avoid life-threatening human error. Critical working environments such as air traffic control, driving, or military surveillance require the operator to be alert the whole time. The electroencephalogram (EEG) is a very common modality that can be used in assessing vigilance. Unfortunately, EEG signals are prone to artifacts due to eye movement, muscle contraction, and electrical noise. Mitigating these artifacts is important for an accurate vigilance level assessment. Independent Component Analysis (ICA) is an effective method and has been extensively used in the suppression of EEG artifacts. However, in vigilance assessment applications, it was found to suffer from leakage of the cerebral activity into artifacts. In this work, we show that the wavelet ICA (wICA) method provides an alternative for artifact reduction, leading to improved vigilance level assessment results. We conducted an experiment in nine human subjects to induce two vigilance states, alert and vigilance decrement, while performing a Stroop Color–Word Test for approximately 45 min. We then compared the performance of the ICA and wICA preprocessing methods using five classifiers. Our classification results showed that in terms of features extraction, the wICA method outperformed the existing ICA method. In the delta, theta, and alpha bands, we obtained a mean classification accuracy of 84.66% using the ICA method, whereas the mean accuracy using the wICA methodwas 96.9%. However, no significant improvement was observed in the beta band. In addition, we compared the topographical map to show the changes in power spectral density across the brain regions for the two vigilance states. The proposed method showed that the frontal and central regions were most sensitive to vigilance decrement. However, in this application, the proposed wICA shows a marginal improvement compared to the Fast-ICA. Full article
(This article belongs to the Special Issue Signal Processing Using Non-Invasive Physiological Sensors 2022)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop