scholarly journals Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG

2021 ◽  
Author(s):  
Dung A. Nguyen-Danse ◽  
Shobana Singaravelu ◽  
Léa A. S. Chauvigné ◽  
Anaïs Mottaz ◽  
Leslie Allaman ◽  
...  

Abstract Objectives Functional connectivity (FC) is increasingly used as target for neuromodulation and enhancement of performance. A reliable assessment of FC with electroencephalography (EEG) currently requires a laboratory environment with high-density montages and a long preparation time. This study investigated the feasibility of reconstructing source FC with a low-density EEG montage towards a usage in real life applications. Methods Source FC was reconstructed with inverse solutions and quantified as node degree of absolute imaginary coherence in alpha frequencies. We used simulated coherent point sources as well as two real datasets to investigate the impact of electrode density (19 vs. 128 electrodes) and usage of template vs. individual MRI-based head models on localization accuracy. In addition, we checked whether low-density EEG is able to capture inter-individual variations in coherence strength. Results In numerical simulations as well as real data, a reduction of the number of electrodes led to less reliable reconstructions of coherent sources and of coupling strength. Yet, when comparing different approaches to reconstructing FC from 19 electrodes, source FC obtained with beamformers outperformed sensor FC, FC computed after independent component analysis, and source FC obtained with sLORETA. In particular, only source FC based on beamformers was able to capture neural correlates of motor behavior. Conclusion Reconstructions of FC from low-density EEG is challenging, but may be feasible when using source reconstructions with beamformers.

Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.


2013 ◽  
Vol 14 (1) ◽  
pp. 57-78
Author(s):  
Alexander Grakovski ◽  
Yuri Krasnitski ◽  
Igor Kabashkin ◽  
Victor Truhachov

Abstract Some possibilities of fibre-optic sensors (FOS) application for measuring the weight of moving vehicles realized in weightin- motion (WIM) systems are discussed. As the first, the model of small-buried seismic sensor transient response excited by a car tyre interaction with asphalt-concrete road pavement is proposed. It is supposed that a seismic wave received by the sensor is the vertical component of surface Raleigh wave. The model is based on supposition that a tyre footprint is acceptable to consider as some array of point sources of these waves. The proper algorithms permit to vary different parameters of the array excitation, as to footprint dimensions, load distribution, car velocities and others. The set of Matlab codes is worked out for seismic pulses modelling and processing. The second way considered is to simulate the FOS signal in the basis of differential equations describing a deformable wheel behaviour, or wheel oscillations, in order to identify relations with optoelectronic mechanical parameters. An attempt to find the mass of the vehicle is based on minimizing the discrepancy between the actual FOS signal and the solution of the differential equation. The accuracy of the evaluated weight depends on many external factors, the mathematical modelling of them are expressed in the numerical values of the coefficients and external stimuli. The influence of these factors are analysed and tested by simulations and field experiments. One of ideas in dynamic weighing problem solution should consist in evaluation of position of virtual gravity centre of the vehicle in time. The processing algorithm of the data received from the FOS is proposed based on conception of database retaining in some reference system memory. Certain requirements concerning the elements and blocks of the algorithm are defined as well. The reference system is realized as the digital filter with the finite impulse response. The method to estimate the filter coefficients is worked out. Several experiments with this algorithm have been carried out for the vehicle identification with the reference loads adopted from real data. The different factors have an influence on the measurement accuracy of FOS. The roadbed features, temperature, nonlinearities and delay effects in FOS are among them. The results of laboratory and field measurements with FOS responses to different axle’s loadings are presented. Charging and inertial characteristics of FOS under the impact of various external factors (protective cover, temperature, contact area, and installation mode especially) as well as their approximations are investigated. It is found that the final calibration of the FOS has to be done individually and only after it has been installed in the pavement. Certain methods and algorithms of linearization, as well temperature and dynamic errors compensation of FOS data are discussed.


Motor Control ◽  
2021 ◽  
pp. 1-16
Author(s):  
W. Tolentino-Castro ◽  
L. Mochizuki ◽  
H. Wagner

According to the literature, persons with intellectual disabilities have poor motor control in tasks in which motor anticipation is needed. Our study aimed to assess their motor behavior during interceptive tasks (a tennis ball interception with external-and-oneself throw conditions). A stick-bar was used as a reference or to support cloth to occlude a ball’s trajectory. Catch performance and interceptive behavior were analyzed (26 persons). The results show that high/low values of the initial approaching movement led to successful/successful catches, respectively. Our results are in line with the literature about the impact of poor motor control on performance in those with intellectual disabilities. We suggest that low anticipation may relate to problems in real-life situations.


Author(s):  
Salem Alawbathani ◽  
Mehreen Batool ◽  
Jan Fleckhaus ◽  
Sarkawt Hamad ◽  
Floyd Hassenrück ◽  
...  

AbstractA poor understanding of statistical analysis has been proposed as a key reason for lack of replicability of many studies in experimental biomedicine. While several authors have demonstrated the fickleness of calculated p values based on simulations, we have experienced that such simulations are difficult to understand for many biomedical scientists and often do not lead to a sound understanding of the role of variability between random samples in statistical analysis. Therefore, we as trainees and trainers in a course of statistics for biomedical scientists have used real data from a large published study to develop a tool that allows scientists to directly experience the fickleness of p values. A tool based on a commonly used software package was developed that allows using random samples from real data. The tool is described and together with the underlying database is made available. The tool has been tested successfully in multiple other groups of biomedical scientists. It can also let trainees experience the impact of randomness, sample sizes and choice of specific statistical test on measured p values. We propose that live exercises based on real data will be more impactful in the training of biomedical scientists on statistical concepts.


2010 ◽  
Vol 6 (3) ◽  
pp. 33
Author(s):  
Robert J Petrella ◽  

It is widely recognised that hypertension is a major risk factor for the development of future cardiovascular (CV) events, which in turn are a major cause of morbidity and mortality. Blood pressure (BP) control with antihypertensive drugs has been shown to reduce the risk of CV events. Angiotensin-II receptor blockers (ARBs) are one such class of antihypertensive drugs and randomised controlled trials (RCTs) have shown ARB-based therapies to have effective BP-lowering properties. However, data obtained under these tightly controlled settings do not necessarily reflect actual experience in clinical practice. Real-life databases may offer alternative information that reflects an uncontrolled real-world setting and complements and expands on the findings of clinical trials. Recent analyses of practice-based real-life databases have shown ARB-based therapies to be associated with better persistence and adherence rates and with superior BP control than non-ARB-based therapies. Analyses of real-life databases also suggest that ARB-based therapies may be associated with a lower risk of CV events than other antihypertensive-drug-based therapies.


2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.


This survey of research on psychology in five volumes is a part of a series undertaken by the ICSSR since 1969, which covers various disciplines under social science. Volume One of this survey, Cognitive and Affective Processes, discusses the developments in the study of cognitive and affective processes within the Indian context. It offers an up-to-date assessment of theoretical developments and empirical studies in the rapidly evolving fields of cognitive science, applied cognition, and positive psychology. It also analyses how pedagogy responds to a shift in the practices of knowing and learning. Additionally, drawing upon insights from related fields it proposes epithymetics–desire studies – as an upcoming field of research and the volume investigates the impact of evolving cognitive and affective processes in Indian research and real life contexts. The development of cognitive capability distinguishes human beings from other species and allows creation and use of complex verbal symbols, facilitates imagination and empowers to function at an abstract level. However, much of the vitality characterizing human life is owed to the diverse emotions and desires. This has made the study of cognition and affect as frontier areas of psychology. With this in view, this volume focuses on delineating cognitive scientific contributions, cognition in educational context, context, diverse applications of cognition, psychology of desire, and positive psychology. The five chapters comprising this volume have approached the scholarly developments in the fields of cognition and affect in innovative ways, and have addressed basic as well applied issues.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1207.2-1207
Author(s):  
A. García Fernández ◽  
A. Briones-Figueroa ◽  
L. Calvo Sanz ◽  
Á. Andreu-Suárez ◽  
J. Bachiller-Corral ◽  
...  

Background:Biological therapy (BT) has changed the treatment and perspectives of JIA patients but little is known about when is the best moment to start BT and the impact of this prompt iniciation.Objectives:To analyze the response to BT of Juvenile Idiophatic Arthritis (JIA) patients according to the time when the BT was started.Methods:A retrospective, descriptive study was conducted on JIA patients followed up in a referal hospital that started BT up to 24 months after diagnosis from 2000 to 2018. Disease activity was measured, at 2 years after diagnosis, according to Wallace criteria for remission (absence of: active arthritis, active uveitis, fever, rash or any other manifestation attributable to JIA, normal CRP and ESR, PGA indicating no active disease) for at least 6 months.Results:55 JIA patients that started BT up to 24 months from diagnosis were analyzed. 69,1% were girls with a median age at diagnosis of 8 years old IQR(3-13), median age at the start of BT of 9 years old IQR(3-13). Regarding JIA categories: 25,5% were Oligoarticular Persistent (OligP), 18,2% Systemic JIA (sJIA), 16,4% Entesitis related Arthritis (ERA), 12,7% Psoriatic Arthritis (APso) and Polyarticular RF- (PolyRF-), 5,5% Oligoarticular Extended (OligE) and Polyarticular RF+ (PolyRF+), 3,6% Undifferentiated (Und). 20% of patients had uveitis during followup. Conventional DMARD (cDMARD) was indicated in 83,6% of patients (95,7% Methotrexate) at diagnosis [median 0 months IQR(0-2,3)]. At the end of followup (2 years) only 30,9% of patients continued with cDMARDs. The main causes of discontinuation were: adverse events (46,7%), remission (36,7%). TNF inhibitors were precribed in 81,8% of patients and 18,2% of patients recieved two BT during the first 2 years from diagnosis. 54,5% of BT were indicated during the first 6 months from diagnosis, 27,3% from 7 to 12 months, 12,7% from 13 to 18 months, 5,5% from 19 to 24 months.After 2 years from diagnosis, 78,2% of patients were on remission and 21,8% active. Among patients with active disease: 75% had arthritis, 16,7% had uveitis and 8,3% had both. There were no differences regarding disease activity among patients with uveitis and neither taking cDMARDs. Regarding JIA categories: 66,7% of OligE, 57,1% of PolyRF- and 57,1% of APso patients were active at 2 years from diagnosis when compared to the other categories (p=0.004).Patients on remission at 24 months from diagnosis started sooner the BT than active patients [CI 95% (0,46-8,29) p=0,029]. The time when the BT was started was correlated to the activity at 2 years (K= 0,294 p=0,029). When the BT was prescribed after 7,5months from diagnosis it was correlated, in a COR curve, with a higher probability of active disease at 2 years (S= 0,67 E= 0,63). There was a correlation, among patients on remission at 2 years, between prompt start of BT and less time to reach remission (K= -0,345 p=0,024). Patients with active disease at 2 years, regardless of moment of BT iniciation, required more BT during follow-up (p=0,002).Conclusion:Prompt iniciation of BT was correlated with a better outcome. JIA patients that started BT early after diagnosis had a higher probability of remission after 2 years. Starting BT after 7,5 months was correlated with a higher probability of active disease at 2 years. Active disease at 24 months was correlated with persistent active disease during follow-up.Disclosure of Interests:None declared


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.


2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.


Sign in / Sign up

Export Citation Format

Share Document