Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 7.519
Filtrar
2.
Artículo en Inglés | MEDLINE | ID: mdl-33669708

RESUMEN

Given the COVID-19 pandemic crisis that has deeply affected the health and well-being of people worldwide, the main objective of this paper was to explore the existing relationship between health, welfare, and population aging until the pandemic burst, on the basis of two distinctive groups of European Union (EU) countries, namely, the old and the new member states. The methodological endeavor was based on two advanced econometric techniques, namely, structural equation modelling and network analysis through Gaussian graphical models, applied for each group of EU countries, analyzed during the period of 1995-2017. The main results revealed significant differentiation among the new and old EU countries as follows: public health support was found to have a positive impact on healthy aging and well-being of older people, on other social determinants, and on people's perceived good and very good health; overall, significant influences were revealed in terms of the aging dimensions. The main implications of our findings relate to other researchers as a baseline comparison with the existing situation before the COVID-19 pandemic outbreak, but also to policymakers that have to rethink the public health allocations, both in old and new EU member states, in order to endorse the aging credentials, underpinning a successful and healthy integration of the elderly within all life dimensions.


Asunto(s)
Envejecimiento Saludable , Salud Pública , Anciano , Anciano de 80 o más Años , Europa (Continente) , Unión Europea , Humanos , Análisis de Clases Latentes , Distribución Normal , Determinantes Sociales de la Salud
3.
Biomed Eng Online ; 20(1): 29, 2021 Mar 25.
Artículo en Inglés | MEDLINE | ID: mdl-33766044

RESUMEN

BACKGROUND: As an object's electrical passive property, the electrical conductivity is proportional to the mobility and concentration of charged carriers that reflect the brain micro-structures. The measured multi-b diffusion-weighted imaging (Mb-DWI) data by controlling the degree of applied diffusion weights can quantify the apparent mobility of water molecules within biological tissues. Without any external electrical stimulation, magnetic resonance electrical properties tomography (MREPT) techniques have successfully recovered the conductivity distribution at a Larmor-frequency. METHODS: This work provides a non-invasive method to decompose the high-frequency conductivity into the extracellular medium conductivity based on a two-compartment model using Mb-DWI. To separate the intra- and extracellular micro-structures from the recovered high-frequency conductivity, we include higher b-values DWI and apply the random decision forests to stably determine the micro-structural diffusion parameters. RESULTS: To demonstrate the proposed method, we conducted phantom and human experiments by comparing the results of reconstructed conductivity of extracellular medium and the conductivity in the intra-neurite and intra-cell body. The phantom and human experiments verify that the proposed method can recover the extracellular electrical properties from the high-frequency conductivity using a routine protocol sequence of MRI scan. CONCLUSION: We have proposed a method to decompose the electrical properties in the extracellular, intra-neurite, and soma compartments from the high-frequency conductivity map, reconstructed by solving the electro-magnetic equation with measured B1 phase signals.


Asunto(s)
Imagen de Difusión por Resonancia Magnética , Conductividad Eléctrica , Procesamiento de Imagen Asistido por Computador/métodos , Tomografía , Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Mapeo Encefálico , Líquido Cefalorraquídeo , Impedancia Eléctrica , Humanos , Distribución Normal , Fantasmas de Imagen , Reproducibilidad de los Resultados
4.
Sci Rep ; 11(1): 4943, 2021 03 02.
Artículo en Inglés | MEDLINE | ID: mdl-33654146

RESUMEN

The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.


Asunto(s)
/diagnóstico , Saliva/química , Espectrometría Raman/métodos , Anciano , Anciano de 80 o más Años , Anticuerpos Antivirales/análisis , Comorbilidad , Biología Computacional , Aprendizaje Profundo , Femenino , Humanos , Masculino , Persona de Mediana Edad , Distribución Normal , Reproducibilidad de los Resultados , Sensibilidad y Especificidad
5.
Sensors (Basel) ; 21(4)2021 Feb 11.
Artículo en Inglés | MEDLINE | ID: mdl-33670096

RESUMEN

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.


Asunto(s)
Acústica , Algoritmos , Inteligencia Artificial , Aprendizaje Automático , Humanos , Redes Neurales de la Computación , Distribución Normal , Reproducibilidad de los Resultados
6.
J Vis Exp ; (168)2021 02 08.
Artículo en Inglés | MEDLINE | ID: mdl-33616097

RESUMEN

Measurements of the specificity and affinity of antigen-antibody interactions are critically important for medical and research applications. In this protocol, we describe the implementation of a new single-molecule technique, mass photometry (MP), for this purpose. MP is a label- and immobilization-free technique that detects and quantifies molecular masses and populations of antibodies and antigen-antibody complexes on a single-molecule level. MP analyzes the antigen-antibody sample within minutes, allowing for the precise determination of the binding affinity and simultaneously providing information on the stoichiometry and the oligomeric state of the proteins. This is a simple and straightforward technique that requires only picomole quantities of protein and no expensive consumables. The same procedure can be used to study protein-protein binding for proteins with a molecular mass larger than 50 kDa. For multivalent protein interactions, the affinities of multiple binding sites can be obtained in a single measurement. However, the single-molecule mode of measurement and the lack of labeling imposes some experimental limitations. This method gives the best results when applied to measurements of sub-micromolar interaction affinities, antigens with a molecular mass of 20 kDa or larger, and relatively pure protein samples. We also describe the procedure for performing the required fitting and calculation steps using basic data analysis software.


Asunto(s)
Afinidad de Anticuerpos , Complejo Antígeno-Anticuerpo/inmunología , Fotometría/métodos , Anticuerpos/inmunología , Humanos , Imagenología Tridimensional , Peso Molecular , Distribución Normal , Unión Proteica , Programas Informáticos , Trombina/inmunología
7.
Artículo en Inglés | MEDLINE | ID: mdl-33466917

RESUMEN

The high blue proportion of phosphor-conversion white-light emitting diodes (pc-LEDs), especially of those with higher correlated color temperatures (CCT), raises concern about photochemically induced retinal damages. Although almost all general lighting service LEDs are safe, other applications exist, like spotlights for theatres or at construction sites, that can pose a severe blue-light hazard (BLH) risk, and their photobiological safety must be assessed. Because of required but challenging radiance measurements, a calculative approach can be supportive for risk assessment. It is the aim of this work to exploit Gaussian functions to study LED parameter variations affecting BLH exposure. Gaussian curve approximations for color LEDs, the BLH action spectrum, and the spectral luminous efficiency for photopic vision enabled analytically solving the BLH efficiency, ηB, and the BLH efficacy of luminous radiation, KB,v. It was found that sigmoidal functions describe the CCT dependence of ηB and KB,v for different color LEDs with equal spectral bandwidth. Regarding pc-LEDs, variations of peak wavelengths, intensities, and bandwidths led to linear or parabolic shaped chromaticity coordinate correlations. ηB and KB,v showed pronounced CCT dependent extrema that might be exploited to reduce BLH. Finally, an experimental test of the presented Gaussian approach yielded its successful applicability for color and pc-LEDs but a minor accuracy for blue and green LEDs.


Asunto(s)
Color , Distribución Normal
8.
Atten Percept Psychophys ; 83(3): 956-969, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33392976

RESUMEN

Recent research has established that humans can extract the average perceptual feature over briefly presented arrays of visual elements or the average of a rapid temporal sequence of numbers. Here we compared the extraction of the average over briefly presented arrays, for a perceptual feature (orientations) and for numerical values (1-9 digits), using an identical experimental design for the two tasks. We hypothesized that the averaging of numbers, more than of orientations, would be constrained by capacity limitations. Arrays of Gabor elements or digits were simultaneously presented for 300 ms and observers were required to estimate the average on a continuous response scale. In each trial the elements were sampled from normal distributions (of various means) and we varied the set size (4-12). We found that while for orientation the averaging precision remained constant with set size, for numbers it decreased with set size. Using computational modeling we also extracted capacity parameters (the number of elements that are pooled in the average extraction). Despite marked heterogeneity between observers, the capacity for orientations (around eight items) was much larger than for numbers (around four items). The orientation task also had a larger fraction of participants relying on distributed attention to all elements. Our study thus supports the idea that numbers more than perceptual features are subject to capacity or attentional limitations when observers need to evaluate the average over an ensemble of stimuli.


Asunto(s)
Orientación Espacial , Orientación , Humanos , Distribución Normal , Percepción
9.
Nat Commun ; 12(1): 392, 2021 01 15.
Artículo en Inglés | MEDLINE | ID: mdl-33452238

RESUMEN

Living and non-living active matter consumes energy at the microscopic scale to drive emergent, macroscopic behavior including traveling waves and coherent oscillations. Recent work has characterized non-equilibrium systems by their total energy dissipation, but little has been said about how dissipation manifests in distinct spatiotemporal patterns. We introduce a measure of irreversibility we term the entropy production factor to quantify how time reversal symmetry is broken in field theories across scales. We use this scalar, dimensionless function to characterize a dynamical phase transition in simulations of the Brusselator, a prototypical biochemically motivated non-linear oscillator. We measure the total energetic cost of establishing synchronized biochemical oscillations while simultaneously quantifying the distribution of irreversibility across spatiotemporal frequencies.


Asunto(s)
Entropía , Modelos Teóricos , Simulación por Computador , Distribución Normal
10.
Nat Commun ; 12(1): 509, 2021 01 21.
Artículo en Inglés | MEDLINE | ID: mdl-33479220

RESUMEN

Motile plant structures such as Mimosa pudica leaves, Impatiens glandulifera seedpods, and Dionaea muscipula leaves exhibit fast nastic movements in a few seconds or less. This motion is stimuli-independent mechanical movement following theorema egregium rules. Artificial analogs of tropistic motion in plants are exemplified by shape-morphing systems, which are characterized by high functional robustness and resilience for creating 3D structures. However, all shape-morphing systems developed so far rely exclusively on continuous external stimuli and result in slow response. Here, we report a Gaussian-preserved shape-morphing system to realize ultrafast shape morphing and non-volatile reconfiguration. Relying on the Gaussian-preserved rules, the transformation can be triggered by mechanical or thermal stimuli within a microsecond. Moreover, as localized energy minima are encountered during shape morphing, non-volatile configuration is preserved by geometrically enhanced rigidity. Using this system, we demonstrate a suite of electronic devices that are reconfigurable, and therefore, expand functional diversification.


Asunto(s)
Algoritmos , Electrónica/métodos , Modelos Biológicos , Hojas de la Planta/fisiología , Fenómenos Biomecánicos , Droseraceae/fisiología , Electrónica/instrumentación , Impatiens/fisiología , Mimosa/fisiología , Movimiento (Física) , Distribución Normal
11.
J Environ Manage ; 280: 111748, 2021 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-33309395

RESUMEN

Sustainable development is being reconsidered as a process with unknown endpoint. Outputs of sustainable urban water systems defined as 'policies, projects, laws, technologies, and consumption and reuse amounts associated with urban water sustainability goals' are therefore being viewed as inadequate monitoring instruments. I propose a new methodology for sustainability monitoring whereby normality of a system is diagnosed through normality of its supporting inputs in lieu of normality of its complex outputs. Supporting inputs are 'intents and behaviors that support system goals'. Supporting inputs follow a principle of self-organization to remain in the norm and behavior zone commonly associated with system goals. This implies that normality of supporting inputs can be inferred from their longitudinally normal or Gaussian distribution that can be explored by significance tests; in particular, the Shapiro-Wilk test which is most powerful for n < 50. We identify fourteen supporting inputs of sustainable urban water systems - such as internet searches, community campaigns, staff training, agent-principal reporting and legislation propositions about water sustainability - and define quantitative indicators for them. The Shapiro-Wilk test and Kolmogorov-Smirnov test (K-S) of these indicators and a subsequent Boxplot outlying examination of non-normal indicators are undertaken in Yazd - a desert city in central Iran with a historic record in water conservation - in the light of its complex wastewater speculation. Qualitative examination of non-normal supporting inputs confirms the ability of our statistical methodology to detect problems in the system.


Asunto(s)
Conservación de los Recursos Hídricos , Agua , Ciudades , Conservación de los Recursos Naturales , Humanos , Irán , Distribución Normal
12.
Neural Netw ; 133: 148-156, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-33217683

RESUMEN

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that adversarial training on fake samples is implemented in vanilla GANs, but adversarial training on real samples has long been overlooked. Consequently, the discriminator is extremely vulnerable to adversarial perturbation and the gradient given by the discriminator contains non-informative adversarial noises, which hinders the generator from catching the pattern of real samples. Here, we develop adversarial symmetric GANs (AS-GANs) that incorporate adversarial training of the discriminator on real samples into vanilla GANs, making adversarial training symmetrical. The discriminator is therefore more robust and provides more informative gradient with less adversarial noise, thereby stabilizing training and accelerating convergence. The effectiveness of the AS-GANs is verified on image generation on CIFAR-10, CIFAR-100, CelebA, and LSUN with varied network architectures. Not only the training is more stabilized, but the FID scores of generated samples are consistently improved by a large margin compared to the baseline. Theoretical analysis is also conducted to explain why AS-GAN can improve training. The bridging of adversarial samples and adversarial networks provides a new approach to further develop adversarial networks.


Asunto(s)
Procesamiento de Imagen Asistido por Computador/métodos , Redes Neurales de la Computación , Reconocimiento de Normas Patrones Automatizadas/métodos , Aprendizaje Automático no Supervisado , Humanos , Distribución Normal
13.
PLoS One ; 15(12): e0243229, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33362207

RESUMEN

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012-2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses.


Asunto(s)
Macrodatos , Informática Médica , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Algoritmos , Femenino , Humanos , Japón/epidemiología , Masculino , Síndrome Metabólico/sangre , Síndrome Metabólico/diagnóstico , Síndrome Metabólico/epidemiología , Persona de Mediana Edad , Distribución Normal , Factores Sexuales
14.
Sensors (Basel) ; 21(1)2020 Dec 28.
Artículo en Inglés | MEDLINE | ID: mdl-33379213

RESUMEN

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.


Asunto(s)
Algoritmos , Pruebas Hematológicas , Procesamiento de Imagen Asistido por Computador , Entropía , Distribución Normal , Venas/diagnóstico por imagen
15.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 37(6): 1080-1088, 2020 Dec 25.
Artículo en Chino | MEDLINE | ID: mdl-33369348

RESUMEN

In clinic, intima and media thickness are the main indicators for evaluating the development of atherosclerosis. At present, these indicators are measured by professional doctors manually marking the boundaries of the inner and media on B-mode images, which is complicated, time-consuming and affected by many artificial factors. A grayscale threshold method based on Gaussian Mixture Model (GMM) clustering is therefore proposed to detect the intima and media thickness in carotid arteries from B-mode images in this paper. Firstly, the B-mode images are clustered based on the GMM, and the boundary between the intima and media of the vessel wall is then detected by the gray threshold method, and finally the thickness of the two is measured. Compared with the measurement technique using the gray threshold method directly, the clustering of B-mode images of carotid artery solves the problem of gray boundary blurring of inner and middle membrane, thereby improving the stability and detection accuracy of the gray threshold method. In the clinical trials of 120 healthy carotid arteries, means of 4 manual measurements obtained by two experts are used as reference values. Experimental results show that the normalized root mean square errors (NRMSEs) of the estimated intima and media thickness after GMM clustering were 0.104 7 ± 0.076 2 and 0.097 4 ± 0.068 3, respectively. Compared with the results of the direct gray threshold estimation, means of NRMSEs are reduced by 19.6% and 22.4%, respectively, which indicates that the proposed method has higher measurement accuracy. The standard deviations are reduced by 17.0% and 21.7%, respectively, which indicates that the proposed method has better stability. In summary, this method is helpful for early diagnosis and monitoring of vascular diseases, such as atherosclerosis.


Asunto(s)
Arterias Carótidas , Grosor Intima-Media Carotídeo , Arterias Carótidas/diagnóstico por imagen , Distribución Normal , Ultrasonografía
16.
BMC Bioinformatics ; 21(1): 524, 2020 Nov 13.
Artículo en Inglés | MEDLINE | ID: mdl-33187469

RESUMEN

BACKGROUND: Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Current methods for the estimation of reference limits focus either on modelling the age-dependent dynamics of different analytes directly in a prospective setting or the extraction of independent distributions from contaminated data sources, e.g. data with latent heterogeneity due to unlabeled pathologic cases. In this article, we propose a new method to estimate indirect reference limits with non-linear dependencies on covariates from contaminated datasets by combining the framework of mixture models and distributional regression. RESULTS: Simulation results based on mixtures of Gaussian and gamma distributions suggest accurate approximation of the true quantiles that improves with increasing sample size and decreasing overlap between the mixture components. Due to the high flexibility of the framework, initialization of the algorithm requires careful considerations regarding appropriate starting weights. Estimated quantiles from the extracted distribution of healthy hemoglobin concentration in boys and girls provide clinically useful pediatric reference limits similar to solutions obtained using different approaches which require more samples and are computationally more expensive. CONCLUSIONS: Latent class distributional regression models represent the first method to estimate indirect non-linear reference limits from a single model fit, but the general scope of applications can be extended to other scenarios with latent heterogeneity.


Asunto(s)
Algoritmos , Hemoglobinas/normas , Niño , Femenino , Hemoglobinas/análisis , Humanos , Almacenamiento y Recuperación de la Información , Masculino , Modelos Estadísticos , Distribución Normal , Valores de Referencia
18.
PLoS One ; 15(10): e0238835, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33052923

RESUMEN

One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to non-experts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.


Asunto(s)
Visualización de Datos , Algoritmos , Interpretación Estadística de Datos , Minería de Datos , Humanos , Método de Montecarlo , Distribución Normal , Probabilidad , Programas Informáticos , Procesos Estocásticos
19.
Artículo en Inglés | MEDLINE | ID: mdl-33017919

RESUMEN

The objective of quantitative ultrasound (QUS) is to characterize tissue microstructure by parametrizing backscattered radiofrequency (RF) signals from clinical ultrasound scanners. Herein, we develop a novel technique based on dynamic programming (DP) to simultaneously estimate the acoustic attenuation, the effective scatterer size (ESS), and the acoustic concentration (AC) from ultrasound backscattered power spectra. This is achieved through two different approaches: (1) using a Gaussian form factor (GFF) and (2) using a general form factor (gFF) that is more flexible than the Gaussian form factor but involves estimating more parameters. Both DP methods are compared to an adaptation of a previously proposed least-squares (LSQ) method. Simulation results show that in the GFF approach, the variance of DP is on average 88%, 75% and 32% lower than that of LSQ for the three estimated QUS parameters. The gFF approach also yields similar improvements.


Asunto(s)
Acústica , Análisis de los Mínimos Cuadrados , Distribución Normal , Ultrasonografía
20.
Artículo en Inglés | MEDLINE | ID: mdl-33017922

RESUMEN

The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification approach for the VAR and SS models, based on Least Absolute Shrinkage and Selection Operator (LASSO), that has the advantages of maintaining good accuracy even when few data samples are available and yielding as output a sparse matrix of estimated information transfer. The performances of LASSO identification were first tested and compared to those of OLS by a simulation study and then validated on real electroencephalographic (EEG) signals recorded during a motor imagery task. Both studies indicated that LASSO, under conditions of data paucity, provides better performances in terms of network structure. Given the general nature of the model, this work opens the way to the use of LASSO regression for the computation of several measures of information dynamics currently in use in computational neuroscience.


Asunto(s)
Electroencefalografía , Entropía , Análisis de los Mínimos Cuadrados , Modelos Lineales , Distribución Normal
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA
...