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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 4875

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Prof. Dr. Ting Li

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Guest Editor

1. Biomedical Engineering (BME) Institute, Chinese Academy of Medical Sciences and Peking Union Medical College, Baidi Road, Tianjin 300192, China
2. Electronics Science Technology College, University of Electronic Science and Technology of China, Chengdu 610051, China
Interests: optoelectronic sensors and sensing system; medical optoelectronics; device and instrumentation; optical imaging
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Special Issue Information

Dear Colleagues,

EEG and fNIRS-based sensors have been widely used in human–computer interfaces, medical diagnosis, and treatment in recent decades. They provide a non-invasive method to interact with the human brain and other tissues. This Special Issue, therefore, aims to compile original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of EEG and fNIRS-based sensors and systems.

We are pleased to invite you to submit papers to this Special Issue. Topics include, but are not limited to, the following:

Novel electrodes or fNIRS probes;
Novel signal processing methods with EEG and fNIRS-based Sensors;
Hemodynamic monitoring and interpretation;
Multimode signal recording/processing;
Time-domain near-infrared spectroscopy (TD-NIRS);
Frequency-domain near-infrared spectroscopy (FD-NIRS);
EEG and/or fNIRS-based sensors in the brain–computer interface;
EEG and/or fNIRS-based sensors in theranostics;
Reliability analysis and design;
Algorithm to identify EEG and/or fNIRS data.

Prof. Dr. Ting Li
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.

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

electroencephalogram
near-infrared spectroscopy
brain–computer interface
multimode sensing
sensor

Published Papers (4 papers)

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Research

11 pages, 2434 KiB

Open AccessArticle

Detection and Evaluation for High-Quality Cardiopulmonary Resuscitation Based on a Three-Dimensional Motion Capture System: A Feasibility Study

by Xingyi Tang, Yan Wang, Haoming Ma, Aoqi Wang, You Zhou, Sijia Li, Runyuan Pei, Hongzhen Cui, Yunfeng Peng and Meihua Piao

Sensors 2024, 24(7), 2154; https://doi.org/10.3390/s24072154 - 27 Mar 2024

Viewed by 528

Abstract

High-quality cardiopulmonary resuscitation (CPR) and training are important for successful revival during out-of-hospital cardiac arrest (OHCA). However, existing training faces challenges in quantifying each aspect. This study aimed to explore the possibility of using a three-dimensional motion capture system to accurately and effectively assess CPR operations, particularly about the non-quantified arm postures, and analyze the relationship among them to guide students to improve their performance. We used a motion capture system (Mars series, Nokov, China) to collect compression data about five cycles, recording dynamic data of each marker point in three-dimensional space following time and calculating depth and arm angles. Most unstably deviated to some extent from the standard, especially for the untrained students. Five data sets for each parameter per individual all revealed statistically significant differences (p < 0.05). The correlation between Angle 1′ and Angle 2′ for trained (r_s = 0.203, p < 0.05) and untrained students (r_s = −0.581, p < 0.01) showed a difference. Their performance still needed improvement. When conducting assessments, we should focus on not only the overall performance but also each compression. This study provides a new perspective for quantifying compression parameters, and future efforts should continue to incorporate new parameters and analyze the relationship among them. Full article

(This article belongs to the Special Issue EEG and fNIRS-Based Sensors)

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12 pages, 3331 KiB

Open AccessArticle

A Novel Approach to Evaluating Crosstalk for Near-Infrared Spectrometers

by Zemeng Chen, Xinliang Cao, Xianglin Li, Boan Pan, Pengbo Wang and Ting Li

Sensors 2024, 24(3), 990; https://doi.org/10.3390/s24030990 - 03 Feb 2024

Viewed by 553

Abstract

Multi-channel and multi-parameter near-infrared spectroscopy (NIRS) has gradually become a new research direction and hot spot due to its ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks for the evaluation of the reliability, sensitivity, stability and response consistency of the NIRS instruments. In this study, a set of test methods (a human blood model test, ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analyzing crosstalk and verifying the reliability of NIRS was conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency of the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for NIRS instrument reliability testing. Full article

(This article belongs to the Special Issue EEG and fNIRS-Based Sensors)

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16 pages, 4242 KiB

Open AccessArticle

Automatic Seizure Detection Based on Stockwell Transform and Transformer

by Xiangwen Zhong, Guoyang Liu, Xingchen Dong, Chuanyu Li, Haotian Li, Haozhou Cui and Weidong Zhou

Sensors 2024, 24(1), 77; https://doi.org/10.3390/s24010077 - 22 Dec 2023

Cited by 1 | Viewed by 931

Abstract

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications. Full article

(This article belongs to the Special Issue EEG and fNIRS-Based Sensors)

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18 pages, 4041 KiB

Open AccessArticle

Respiratory Rate Extraction from Neonatal Near-Infrared Spectroscopy Signals

by Naser Hakimi, Mohammad Shahbakhti, Jörn M. Horschig, Thomas Alderliesten, Frank Van Bel, Willy N. J. M. Colier and Jeroen Dudink

Sensors 2023, 23(9), 4487; https://doi.org/10.3390/s23094487 - 05 May 2023

Viewed by 1964

Abstract

Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain ‘noise’ from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS and respiratory rate (RR) using a single sensor would facilitate a perfectly time-synced assessment of (cerebral) physiology. Our aim was to extract respiratory rate from cerebral NIRS intensity signals in neonates admitted to a neonatal intensive care unit (NICU). Methods: A novel algorithm, NRR (NIRS RR), is developed for extracting RR from NIRS signals recorded from critically ill neonates. In total, 19 measurements were recorded from ten neonates admitted to the NICU with a gestational age and birth weight of 38 ± 5 weeks and 3092 ± 990 g, respectively. We synchronously recorded NIRS and reference RR signals sampled at 100 Hz and 0.5 Hz, respectively. The performance of the NRR algorithm is assessed in terms of the agreement and linear correlation between the reference and extracted RRs, and it is compared statistically with that of two existing methods. Results: The NRR algorithm showed a mean error of 1.1 breaths per minute (BPM), a root mean square error of 3.8 BPM, and Bland–Altman limits of agreement of 6.7 BPM averaged over all measurements. In addition, a linear correlation of 84.5% (p < 0.01) was achieved between the reference and extracted RRs. The statistical analyses confirmed the significant (p < 0.05) outperformance of the NRR algorithm with respect to the existing methods. Conclusions: We showed the possibility of extracting RR from neonatal NIRS in an intensive care environment, which showed high correspondence with the reference RR recorded. Adding the NRR algorithm to a NIRS system provides the opportunity to record synchronously different physiological sources of information about cerebral perfusion and respiration by a single monitoring system. This allows for a concurrent integrated analysis of the impact of breathing (including apnea) on cerebral hemodynamics. Full article

(This article belongs to the Special Issue EEG and fNIRS-Based Sensors)

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