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Zhuravlev M, Novikov M, Parsamyan R, Selskii A, Runnova A. The Objective Assessment of Event-Related Potentials: An Influence of Chronic Pain on ERP Parameters. Neurosci Bull 2023; 39:1105-1116. [PMID: 36813952 PMCID: PMC10313590 DOI: 10.1007/s12264-023-01035-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 10/07/2022] [Indexed: 02/24/2023] Open
Abstract
The article presents an original method for the automatic assessment of the quality of event-related potentials (ERPs), based on the calculation of the coefficient ε, which describes the compliance of recorded ERPs with some statistically significant parameters. This method was used to analyze the neuropsychological EEG monitoring of patients suffering from migraines. The frequency of migraine attacks was correlated with the spatial distribution of the coefficients ε, calculated for EEG channels. More than 15 migraine attacks per month was accompanied by an increase in calculated values in the occipital region. Patients with infrequent migraines exhibited maximum quality in the frontal areas. The automatic analysis of spatial maps of the coefficient ε demonstrated a statistically significant difference between the two analyzed groups with different means of migraine attack numbers per month.
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Affiliation(s)
- Maksim Zhuravlev
- Coordinating Center for Fundamental Research, National Medical Research Center for Therapy and Preventive Medicine, Moscow, 101000, Russia.
- Institute of Physics, Saratov State University, Saratov, 410012, Russia.
| | - Mikhail Novikov
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
| | - Ruzanna Parsamyan
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
| | - Anton Selskii
- Institute of Physics, Saratov State University, Saratov, 410012, Russia
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
| | - Anastasiya Runnova
- Coordinating Center for Fundamental Research, National Medical Research Center for Therapy and Preventive Medicine, Moscow, 101000, Russia
- Department of Fundamental Research in Neurocardiology, Institute of Cardiological Research, Saratov State Medical University, Saratov, 410012, Russia
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2
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Xue H, Wang D, Jin M, Gao H, Wang X, Xia L, Li D, Sun K, Wang H, Dong X, Zhang C, Cong F, Lin J. Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition. MICROSYSTEMS & NANOENGINEERING 2023; 9:79. [PMID: 37313471 PMCID: PMC10258200 DOI: 10.1038/s41378-023-00524-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 02/28/2023] [Accepted: 03/15/2023] [Indexed: 06/15/2023]
Abstract
Noninvasive brain-computer interfaces (BCIs) show great potential in applications including sleep monitoring, fatigue alerts, neurofeedback training, etc. While noninvasive BCIs do not impose any procedural risk to users (as opposed to invasive BCIs), the acquisition of high-quality electroencephalograms (EEGs) in the long term has been challenging due to the limitations of current electrodes. Herein, we developed a semidry double-layer hydrogel electrode that not only records EEG signals at a resolution comparable to that of wet electrodes but is also able to withstand up to 12 h of continuous EEG acquisition. The electrode comprises dual hydrogel layers: a conductive layer that features high conductivity, low skin-contact impedance, and high robustness; and an adhesive layer that can bond to glass or plastic substrates to reduce motion artifacts in wearing conditions. Water retention in the hydrogel is stable, and the measured skin-contact impedance of the hydrogel electrode is comparable to that of wet electrodes (conductive paste) and drastically lower than that of dry electrodes (metal pin). Cytotoxicity and skin irritation tests show that the hydrogel electrode has excellent biocompatibility. Finally, the developed hydrogel electrode was evaluated in both N170 and P300 event-related potential (ERP) tests on human volunteers. The hydrogel electrode captured the expected ERP waveforms in both the N170 and P300 tests, showing similarities in the waveforms generated by wet electrodes. In contrast, dry electrodes fail to detect the triggered potential due to low signal quality. In addition, our hydrogel electrode can acquire EEG for up to 12 h and is ready for recycled use (7-day tests). Altogether, the results suggest that our semidry double-layer hydrogel electrodes are able to detect ERPs in the long term in an easy-to-use fashion, potentially opening up numerous applications in real-life scenarios for noninvasive BCI.
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Affiliation(s)
- Hailing Xue
- Key State Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 116024 Dalian, China
| | - Dongyang Wang
- Key State Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 116024 Dalian, China
| | - Mingyan Jin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Hanbing Gao
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Xuhui Wang
- Key Laboratory of Energy Materials and School of Materials Science and Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Long Xia
- Key State Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 116024 Dalian, China
| | - Dong’ang Li
- Key State Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 116024 Dalian, China
| | - Kai Sun
- Key State Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 116024 Dalian, China
| | - Huanan Wang
- Key State Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 116024 Dalian, China
| | - Xufeng Dong
- Key Laboratory of Energy Materials and School of Materials Science and Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Chi Zhang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Jiaqi Lin
- Key State Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 116024 Dalian, China
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Saibene A, Caglioni M, Corchs S, Gasparini F. EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:2798. [PMID: 36905004 PMCID: PMC10007053 DOI: 10.3390/s23052798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.
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Affiliation(s)
- Aurora Saibene
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
| | - Mirko Caglioni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Silvia Corchs
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
- Department of Theoretical and Applied Sciences, University of Insubria, Via J. H. Dunant 3, 21100 Varese, Italy
| | - Francesca Gasparini
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
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Perez-Ortiz CX, Gordillo JL, Mendoza-Montoya O, Antelis JM, Caraza R, Martinez HR. Functional Connectivity and Frequency Power Alterations during P300 Task as a Result of Amyotrophic Lateral Sclerosis. SENSORS (BASEL, SWITZERLAND) 2021; 21:6801. [PMID: 34696014 PMCID: PMC8541445 DOI: 10.3390/s21206801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/22/2021] [Accepted: 09/26/2021] [Indexed: 12/01/2022]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p < 0.05) were: an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The evolution observed over months of an advanced-state patient backs up these findings. These results were used to compute connectivity- and power-based features to discriminate between ALS and Control groups using Support Vector Machine (SVM). Cross-validation achieved a 100% in specificity and 75% in sensitivity, with an overall 89% success.
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Affiliation(s)
- Claudia X. Perez-Ortiz
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Monterrey 64849, Mexico; (C.X.P.-O.); (J.L.G.); (J.M.A.)
| | - Jose L. Gordillo
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Monterrey 64849, Mexico; (C.X.P.-O.); (J.L.G.); (J.M.A.)
| | - Omar Mendoza-Montoya
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Monterrey 64849, Mexico; (C.X.P.-O.); (J.L.G.); (J.M.A.)
| | - Javier M. Antelis
- Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Monterrey 64849, Mexico; (C.X.P.-O.); (J.L.G.); (J.M.A.)
| | - Ricardo Caraza
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey 64849, Mexico; (R.C.); (H.R.M.)
| | - Hector R. Martinez
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey 64849, Mexico; (R.C.); (H.R.M.)
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Li S, Jin J, Daly I, Wang X, Lam HK, Cichocki A. Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method. J Neurosci Methods 2021; 362:109300. [PMID: 34343575 DOI: 10.1016/j.jneumeth.2021.109300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/14/2021] [Accepted: 07/29/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability. NEW METHODS In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention. RESULTS The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance. COMPARISON WITH EXISTING METHODS The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms. CONCLUSIONS The proposed MSFF method is able to improve the performance of P300-based BCIs.
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Affiliation(s)
- Shurui Li
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Hak-Keung Lam
- Department of Engineering, King's College London, London WC2R 2LS, UK
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia; Systems Research Institute PAS, Warsaw, Poland; Nicolaus Copernicus University (UMK), Torun, Poland
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Wang Z, Zhao X, Zhang M, Hu H. A Maximum Likelihood Perspective of Spatial Filter Design in SSVEP-Based BCIs. IEEE Trans Biomed Eng 2021; 68:2706-2717. [PMID: 33417535 DOI: 10.1109/tbme.2021.3049853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In steady-state visual-evoked potential (SSVEP) based brain-computer interfaces (BCIs), existing detection algorithms utilizing spatial filters like task-related component analysis (TRCA) derive the spatial filters mainly through maximizing the inter-trial similarity between the combined signals over the training set. Although they achieve by far the best classification performance in SSVEP-based BCIs, some important problems are still unresolved. Especially, the mechanism of how spatial filters cancel the background noise in brain signals and optimize the signal-to-noise ratio (SNR) of SSVEPs is still not figured out. Therefore, to solve these problems, in this paper a new perspective of spatial filter design is proposed. Specifically, a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors. In the same time, the relation between maximum likelihood estimation and signal-to-noise ratio (SNR) maximization is discussed. Through a step-by-step formulation, this paper provides a theoretical justification for those conventional algorithms utilizing spatial filters. As for the classification performance, the proposed scheme is tested on a benchmark dataset of 35 subjects. Experiment results show that the classification performance of the proposed scheme is competitive against three benchmark algorithms, which include TRCA. Especially, the proposed scheme achieves a fair performance improvement over the benchmark methods in the cases where a shorter time window, or a larger number of electrodes, or a smaller number of training blocks are adopted.
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Jeong JH, Yu BW, Lee DH, Lee SW. Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals. Brain Sci 2019; 9:E348. [PMID: 31795445 PMCID: PMC6956039 DOI: 10.3390/brainsci9120348] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/22/2019] [Accepted: 11/26/2019] [Indexed: 11/16/2022] Open
Abstract
Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot's mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.
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Affiliation(s)
- Ji-Hoon Jeong
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul 02841, Korea; (J.-H.J.); (B.-W.Y.); (D.-H.L.)
| | - Baek-Woon Yu
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul 02841, Korea; (J.-H.J.); (B.-W.Y.); (D.-H.L.)
| | - Dae-Hyeok Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul 02841, Korea; (J.-H.J.); (B.-W.Y.); (D.-H.L.)
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul 02841, Korea; (J.-H.J.); (B.-W.Y.); (D.-H.L.)
- Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-ku, Seoul 02841, Korea
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Kundu S, Ari S. P300 based character recognition using sparse autoencoder with ensemble of SVMs. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Türk Ö, Özerdem MS. Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals. Brain Sci 2019; 9:brainsci9050115. [PMID: 31109020 PMCID: PMC6562774 DOI: 10.3390/brainsci9050115] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/15/2019] [Accepted: 05/17/2019] [Indexed: 11/16/2022] Open
Abstract
The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.
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Affiliation(s)
- Ömer Türk
- Department of Computer programming, Mardin Artuklu University, Mardin 47500, Turkey.
| | - Mehmet Siraç Özerdem
- Department of Electronics Engineering Dicle University, Diyarbakır 21100, Turkey.
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Brain⁻Computer Interfaces for Human Augmentation. Brain Sci 2019; 9:brainsci9020022. [PMID: 30682766 PMCID: PMC6406539 DOI: 10.3390/brainsci9020022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 01/22/2019] [Indexed: 01/06/2023] Open
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