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Found 18 papers, 1 papers with code
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model
Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention.
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification
Second, a supervised part learns a classifier based on the labeled training samples using the latent features acquired in the unsupervised part.
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition
Although deep learning-based algorithms have demonstrated excellent performance in automated emotion recognition via electroencephalogram (EEG) signals, variations across brain signal patterns of individuals can diminish the model's effectiveness when applied across different subjects.
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition
Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli.
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging
Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods.
Combining physics-based modeling and deep learning for ultrasound elastography
Ultrasound elasticity images which enable the visualization of quantitative maps of tissue stiffness can be reconstructed by solving an inverse problem.
Regularization by Adversarial Learning for Ultrasound Elasticity Imaging
In contrast, standard data-driven methods count solely on supervised learning on the training data pairs leading to massive network parameters for unnecessary physical model relearning which might not be consistent with the governing physical models of the imaging system.
MR elasticity reconstruction using statistical physical modeling and explicit data-driven denoising regularizer
Elasticity image, visualizing the quantitative map of tissue stiffness, can be reconstructed by solving an inverse problem.
Finite Element Reconstruction Of Stiffness Images In MR Elastography Using Statistical Physical Forward Modeling And Proximal Optimization Methods
Quantitative characterization of tissue properties, known as elasticity imaging, can be cast as solving an ill-posed inverse problem.
Ultrasound Elasticity Imaging Using Physics-based Models And Learning-based Plug-And-Play Priors
Existing physical model-based imaging methods for ultrasound elasticity reconstruction utilize fixed variational regularizers that may not be appropriate for the application of interest or may not capture complex spatial prior information about the underlying tissues.
Online Graph Learning under Smoothness Priors
The growing success of graph signal processing (GSP) approaches relies heavily on prior identification of a graph over which network data admit certain regularity.
Online Discriminative Graph Learning from Multi-Class Smooth Signals
Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks.
Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task
Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks.
Combining nonparametric spatial context priors with nonparametric shape priors for dendritic spine segmentation in 2-photon microscopy images
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that shape and data terms can then work together to produce better segmentations.
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors
In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation.
Bayesian Nonparametric Models for Synchronous Brain-Computer Interfaces
In this work, we propose a Bayesian nonparametric model for brain signal classification that does not require "a priori" selection of the number of hidden states and the number of Gaussian mixtures of a HMM.
Dendritic Spine Shape Analysis: A Clustering Perspective
We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem.
Disjunctive Normal Level Set: An Efficient Parametric Implicit Method
Level set methods are widely used for image segmentation because of their capability to handle topological changes.
