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Stochastic maximum likelihood mean and cross-spectrum structure modelling in neuro-magnetic source estimation
2005
Digital signal processing (Print)
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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
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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)
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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
2013
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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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]
2020
bioRxiv
pre-print
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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
2019
IEEE Access
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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
2016
Journal of Applied Statistics
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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
2020
PLoS Computational Biology
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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
2013
Sensors
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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]
2018
arXiv
pre-print
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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
2014
PLoS Computational Biology
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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]
2018
bioRxiv
pre-print
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, 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
2017
Frontiers in Integrative Neuroscience
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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
2020
IEEE Communications Surveys and Tutorials
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2020 pre-print arXiv:2005.00058v1 fatcat:mxsuueh3vjeizmmd75n5z7byku
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
2019
NeuroImage
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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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