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Stochastic maximum likelihood mean and cross-spectrum structure modelling in neuro-magnetic source estimation

Raoul P.P.P. Grasman, Hilde M. Huizenga, Lourens J. Waldorp, Peter C.M. Molenaar, Koen B.E. Böcker
2005 Digital signal processing (Print)  
To obtain estimates we propose a stochastic maximum likelihood (SML) method, and obtain the concentrated likelihood that includes the trial means.  2004 Elsevier Inc. All rights reserved.  ...  Eng. 51 (1) (2004) 45-55] we proposed to analyze cross-spectrum matrices obtained from electro-or magnetoencephalographic (EEG/MEG) signals, to obtain estimates of the EEG/MEG sources and their coherence  ...  In Section 3 the mean and cross-spectrum model is given, and a framework for modelling source amplitude coherence is presented.  ... 
doi:10.1016/j.dsp.2004.09.003 fatcat:7mrydao43be6hhep57h5yuwbka

Front Matter [chapter]

2014 Identification of Physical Systems  
Likelihood Estimation 139 3.5.1 Formulation of Maximum Likelihood Estimation 139 3.5.2 Illustrative Examples: Maximum Likelihood Estimation of Mean or Median 141 3.5.3 Illustrative Examples:  ...  Maximum Likelihood Estimation of Mean and Variance 148 3.5.4 Properties of Maximum Likelihood Estimator 154 3.6 Summary 154 3.7 Appendix: Cauchy-Schwarz Inequality 157 3.8 Appendix: Cramér-Rao  ...  (⋅) ℤ(⋅) z-transform of (⋅) z z -transform variable ℤ −1 (⋅) the inverse z-transform of (⋅) frequency in radians per second  ... 
doi:10.1002/9781118536483.fmatter fatcat:5uxb6gtr3zdlpoj2ugu77qcaai

Table of Content

2020 2020 28th Iranian Conference on Electrical Engineering (ICEE)  
Full Duplex ... 1640 Maximum Likelihood Timing Recovery for Digitally Modulated Burst Signals with Sampling Frequency and Delay Offset .................................................................  ...  Magnetic Resonance Images ............................. 1620 A Novel PWM Digital Pixel Sensor with In-pixel Memory Structure ................................................... 1625 BIMODAL ANFISGC FOR  ... 
doi:10.1109/icee50131.2020.9260902 fatcat:7gs43h5sqraabcu35jsrax4cqu

Acoustic signal based traffic density state estimation using adaptive Neuro-Fuzzy classifier

Prashant Borkar, L. G. Malik
2013 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
Adaptive Neuro-Fuzzy classifier is used to model the traffic density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h).  ...  Adaptive Neuro-Fuzzy classifier is used to classify the acoustic signal segments spanning duration of 20-40 s, which results in a classification accuracy of 93.33% for 13-D MFCC coefficients and around  ...  likelihood estimation, maximum posterior probability estimation, Gaussian mixture models, hidden Markov models or k-nearest neighbor method. • Syntactic or structural classifiers based on linear or nonlinear  ... 
doi:10.1109/fuzz-ieee.2013.6622444 dblp:conf/fuzzIEEE/BorkarM13 fatcat:ztv4yhop5bd2vpqgwgvfryeaxy

Frequency-specific meso-scale structure of spontaneous oscillatory activity in the human brain [article]

Riccardo Iandolo, Marianna Semprini, Dante Mantini, Stefano Buccelli, Diego Sona, Laura Avanzino, Michela Chiappalone
2020 bioRxiv   pre-print
Then, we inferred the community structure using weighted stochastic block-modelling to capture the landscape of meso-scale structures across the frequency domain.  ...  Despite meso-scale modalities were mixed over the entire spectrum, we found a selective increase of disassortativity in the delta/theta bands, and of core-peripheriness in the low/high gamma bands.  ...  Additionally, it is 514 important to note that stochastic block-modelling has the unmet advantage of being a generative model, as 515 it tries to estimate the process underlying the observed network topology  ... 
doi:10.1101/2020.05.26.114488 fatcat:4xcpwb2sd5axffaca3fi3odpqi

Adaptive Independent Subspace Analysis (AISA) of Brain Magnetic Resonance Imaging (MRI) Data

Qiao Ke, Jiangshe Zhang, Wei Wei, Robertas Damasevicius, Marcin Wozniak
2019 IEEE Access  
Methods for image registration, segmentation, and visualization of magnetic resonance imaging (MRI) data are used widely to help medical doctors in supporting diagnostics.  ...  INDEX TERMS Adaptive independent subspace analysis (AISA), magnetic resonance imaging (MRI), image processing, autism spectrum disorder.  ...  The learning rule is defined by estimating multi-parameter that maximize the likelihood of z j based on the general super-gaussian PDF.  ... 
doi:10.1109/access.2019.2893496 fatcat:fkmqbtlwabe7vejklh4le5x4lm

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

Cristina Mollica, Lea Petrella
2016 Journal of Applied Statistics  
methods for Predictive and Exploratory Path modeling  ...  Specialized teams Currently the ERCIM WG has over 1150 members and the following specialized teams BM: Bayesian Methodology CODA: Complex data structures and Object Data Analysis CPEP: Component-based  ...  likelihood (ML) estimators and restricted maximum likelihood (REML) estimators.  ... 
doi:10.1080/02664763.2016.1263835 fatcat:l5eyielgxrct7hq5ljqeej5ccy

The Affective Ising Model: A computational account of human affect dynamics

Tim Loossens, Merijn Mestdagh, Egon Dejonckheere, Peter Kuppens, Francis Tuerlinckx, Stijn Verdonck, Jacopo Grilli
2020 PLoS Computational Biology  
The predictive performance of the models is also compared by means of leave-one-out cross-validation.  ...  In this paper, a nonlinear stochastic model for the dynamics of positive and negative affect is proposed called the Affective Ising Model (AIM).  ...  Second, the model was fitted to each data set and using the maximum likelihood estimates, 1,000 new (replicated) data sets were generated.  ... 
doi:10.1371/journal.pcbi.1007860 pmid:32413047 fatcat:7ufhpokfhnexbbajbil35fx77i

Surface Electromyography Signal Processing and Classification Techniques

Rubana Chowdhury, Mamun Reaz, Mohd Ali, Ashrif Bakar, Kalaivani Chellappan, Tae Chang
2013 Sensors  
Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and  ...  Abstract: Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more  ...  Acknowledgments The author would like to thank and acknowledge the medical services of Teknologi Kasihatan dan Perubatan Research Group.  ... 
doi:10.3390/s130912431 pmid:24048337 pmcid:PMC3821366 fatcat:dpmex65sbfgsljq5edqn3qzmki

End to End Brain Fiber Orientation Estimation using Deep Learning [article]

Nandakishore Puttashamachar, Ulas Bagci
2018 arXiv   pre-print
We introduce an end to end Deep Learning framework which can accurately estimate the most probable likelihood orientation at each voxel along a neuronal pathway.  ...  We use Probabilistic Tractography as our baseline model to obtain the training data and which also serve as a Tractography Gold Standard for our evaluations.  ...  Cross Entropy Loss Given a discrete variable x and two distributions,p(x) an estimate of the of the actual distribution p(x) the cross entropy between the two distributions is given by the equation H(p  ... 
arXiv:1806.03969v1 fatcat:mqytem4vx5erbfwi4a2syed4b4

VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

Jean Daunizeau, Vincent Adam, Lionel Rigoux, Andreas Prlic
2014 PLoS Computational Biology  
estimation/model selection, and (iii) experimental design optimization.  ...  This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes.  ...  One can see that both the estimated group mean and the Bayesian model comparison are coherent, in terms of inferring whether there is a non-zero group-mean (second level effect).  ... 
doi:10.1371/journal.pcbi.1003441 pmid:24465198 pmcid:PMC3900378 fatcat:bdtqpwkjmjbkzopt2tc6jw2lsu

Causal inference in the multisensory brain [article]

Yinan Cao, Christopher Summerfield, Hame Park, Bruno L. Giordano, Christoph Kayser
2018 bioRxiv   pre-print
, continuing with sensory fusion in parietal-temporal regions and culminating as causal inference in the frontal lobe.  ...  The brain could solve this challenge using a hierarchical principle, by deriving rapidly a fused sensory estimate for computational expediency and, later and if required, filtering out irrelevant signals  ...  Table 1 : 1 Selective encoding of candidate model estimates in source-localised MEG activity.  ... 
doi:10.1101/500413 fatcat:qpsiyomeezfk5ft5ja3zhmsrp4

Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders

Elizabeth B. Torres, Sejal Mistry, Carla Caballero, Caroline P. Whyatt
2017 Frontiers in Integrative Neuroscience  
autistic spectrum.  ...  The approximate 5:1 male to female ratio in clinical detection of Autism Spectrum Disorder (ASD) prevents research from characterizing the female phenotype.  ...  ACKNOWLEDGMENTS We thank the participants in these studies and the researchers who contributed the data in ABIDE.  ... 
doi:10.3389/fnint.2017.00010 pmid:28638324 pmcid:PMC5461345 fatcat:h3qj35lnnrcgnenbsjuitupbau

A Journey from Improper Gaussian Signaling to Asymmetric Signaling

Sidrah Javed, Osama Amin, Basem Shihada, Mohamed-Slim Alouini
2020 IEEE Communications Surveys and Tutorials  
contributions in this enormous journey.  ...  As such, the theory of impropriety has vast applications in medicine, geology, acoustics, optics, image and pattern recognition, computer vision, and other numerous research fields with our main focus  ...  However, many applications require a stochastic modeling of the underlying phenomena such as electromagnetic waves carrying random codes, polarized magnetic disturbances, and noise in image processing  ... 
doi:10.1109/comst.2020.2989626 fatcat:zyno7ku6n5eqnp6rrcopczb4qu

Benchmarking functional connectome-based predictive models for resting-state fMRI

Kamalaker Dadi, Mehdi Rahim, Alexandre Abraham, Darya Chyzhyk, Michael Milham, Bertrand Thirion, Gaël Varoquaux
2019 NeuroImage  
We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric  ...  Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations in the populations and sites.  ...  With regards to covariance estimation, we also investigate the empirical covariance (maximum likelihood estimator) and sparse inverse covariance (Appendix H).  ... 
doi:10.1016/j.neuroimage.2019.02.062 pmid:30836146 fatcat:gyc6jxopp5gihn3alwgrf7zcge
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