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Found 22 papers, 4 papers with code
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Specifically, we trained neural networks (i. e., CNN, vanilla RNN, GRU, LSTM, and Transformer) to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity (EC) between the perturbed EEG channel and the rest of the channels.
Mapping the whole-brain effective connectome with excitatory-inhibitory causal relationship
Understanding the large-scale causal relationship among brain regions is crucial for elucidating the information flow that the brain integrates external stimuli and generates behaviors.
Network analysis on cortical morphometry in first-episode schizophrenia
Our work supports that cortical morphological connectivity, which is constructed based on correlations across subjects' cortical thickness, may serve as a tool to study topological abnormalities in neurological disorders.
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms.
Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain
In this paper, we propose a general framework called multi-objective optimization via evolutionary algorithms (MOVEA) to address the non-convex optimization problem in designing TES strategies without predefined direction.
Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network
The results show that STpGCN significantly improves brain decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions.
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
To this end, we propose a new method to solve the partial least square regression, named PLSR via optimization on bi-Grassmann manifold (PLSRbiGr).
Immunofluorescence Capillary Imaging Segmentation: Cases Study
Our work offers a benchmark dataset for training deep learning models for capillary image segmentation and provides a potential tool for future capillary research.
Transfer learning to decode brain states reflecting the relationship between cognitive tasks
Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning.
Embedding Decomposition for Artifacts Removal in EEG Signals
DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal.
Deep Auto-encoder with Neural Response
In this study, we propose an integrated framework called Deep Autoencoder with Neural Response (DAE-NR), which incorporates information from ANN and the visual cortex to achieve better image reconstruction performance and higher neural representation similarity between biological and artificial neurons.
Kuramoto model based analysis reveals oxytocin effects on brain network dynamics
Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN.
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies.
Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization
EEG source localization is an important technical issue in EEG analysis.
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.
Riemannian Manifold Optimization for Discriminant Subspace Learning
Linear discriminant analysis (LDA) is a widely used algorithm in machine learning to extract a low-dimensional representation of high-dimensional data, it features to find the orthogonal discriminant projection subspace by using the Fisher discriminant criterion.
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction
However, most of tensor decomposition methods are the linear feature extraction techniques, which are unable to reveal the nonlinear structure within high-dimensional data.
Bigeminal Priors Variational auto-encoder
The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset.
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models.
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs.
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.
