A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
Biomedical signals carry signature rhythms of complex physiological processes that control
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …
Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets
In this work, we propose a new method for oversampling the training set of a classifier, in a
scenario of extreme scarcity of training data. It is based on two concepts: Generative …
scenario of extreme scarcity of training data. It is based on two concepts: Generative …
Modeling brain dynamic state changes with adaptive mixture independent component analysis
There is a growing interest in neuroscience in assessing the continuous, endogenous, and
nonstationary dynamics of brain network activity supporting the fluidity of human cognition …
nonstationary dynamics of brain network activity supporting the fluidity of human cognition …
Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG
This study applies adaptive mixture independent component analysis (AMICA) to learn a set
of ICA models, each optimized by fitting a distributional model for each identified component …
of ICA models, each optimized by fitting a distributional model for each identified component …
Fusion of scores in a detection context based on alpha integration
We present a new method for fusing scores corresponding to different detectors (two-
hypotheses case). It is based on alpha integration, which we have adapted to the detection …
hypotheses case). It is based on alpha integration, which we have adapted to the detection …
Multichannel dynamic modeling of non-Gaussian mixtures
This paper presents a novel method that combines coupled hidden Markov models (HMM)
and non-Gaussian mixture models based on independent component analyzer mixture …
and non-Gaussian mixture models based on independent component analyzer mixture …
Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs
Recent works in signal processing on graphs have been driven to estimate the precision
matrix and to use it as the graph Laplacian matrix. The normalized elements of the precision …
matrix and to use it as the graph Laplacian matrix. The normalized elements of the precision …
Deep-learning-based stress-ratio prediction model using virtual reality with electroencephalography data
SY Ji, SY Kang, HJ Jun - Sustainability, 2020 - mdpi.com
The Reich Chancellery, built by Albert Speer, was designed with an overwhelming
ambience to represent the worldview of Hitler. The interior of the Reich Chancellery …
ambience to represent the worldview of Hitler. The interior of the Reich Chancellery …
Computing the partial correlation of ICA models for non-Gaussian graph signal processing
Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian
case, in particular to independent component analysis (ICA) models of the observed …
case, in particular to independent component analysis (ICA) models of the observed …
[图书][B] On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
A Salazar - 2012 - books.google.com
A natural evolution of statistical signal processing, in connection with the progressive
increase in computational power, has been exploiting higher-order information. Thus, high …
increase in computational power, has been exploiting higher-order information. Thus, high …