Search Results for author: Quanying Liu

Found 22 papers, 4 papers with code

Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data

no code implementations19 Jul 2023 Peizhen Yang, Xinke Shen, Zongsheng Li, Zixiang Luo, Kexin Lou, Quanying Liu

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.

EEG Electroencephalogram (EEG)

Mapping the whole-brain effective connectome with excitatory-inhibitory causal relationship

no code implementations31 Dec 2022 Zixiang Luo, Zhichao Liang, Chenyu Xu, Changsong Zhou, Quanying Liu

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

no code implementations26 Dec 2022 Mowen Yin, Weikai Huang, Zhichao Liang, Quanying Liu, Xiaoying Tang

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

no code implementations10 Dec 2022 Wenwei Luo, Wanguang Yin, Quanying Liu, Youzhi Qu

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.

EEG Electroencephalogram (EEG)

Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain

1 code implementation10 Nov 2022 Mo Wang, Kexin Lou, Zeming Liu, Pengfei Wei, Quanying Liu

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.

Evolutionary Algorithms

Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network

no code implementations8 Oct 2022 Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo Wang, Quanying Liu

The results show that STpGCN significantly improves brain decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions.

Brain Decoding

Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding

no code implementations9 Aug 2022 Wanguang Yin, Zhichao Liang, JianGuo Zhang, Quanying Liu

To this end, we propose a new method to solve the partial least square regression, named PLSR via optimization on bi-Grassmann manifold (PLSRbiGr).

EEG Eeg Decoding +1

Immunofluorescence Capillary Imaging Segmentation: Cases Study

1 code implementation14 Jul 2022 Runpeng Hou, Ziyuan Ye, Chengyu Yang, Linhao Fu, Chao Liu, Quanying Liu

Our work offers a benchmark dataset for training deep learning models for capillary image segmentation and provides a potential tool for future capillary research.

Benchmarking Image Segmentation +2

Transfer learning to decode brain states reflecting the relationship between cognitive tasks

no code implementations7 Jun 2022 Youzhi Qu, Xinyao Jian, Wenxin Che, Penghui Du, Kai Fu, Quanying Liu

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.

Transfer Learning

Embedding Decomposition for Artifacts Removal in EEG Signals

1 code implementation2 Dec 2021 Junjie Yu, Chenyi Li, Kexin Lou, Chen Wei, Quanying Liu

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.

Denoising EEG +1

Deep Auto-encoder with Neural Response

no code implementations30 Nov 2021 Xuming Ran, Jie Zhang, Ziyuan Ye, Haiyan Wu, Qi Xu, Huihui Zhou, Quanying Liu

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.

Image Reconstruction

Kuramoto model based analysis reveals oxytocin effects on brain network dynamics

no code implementations18 May 2021 Shuhan Zheng, Zhichao Liang, Youzhi Qu, Qingyuan Wu, Haiyan Wu, Quanying Liu

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

no code implementations26 Mar 2021 Zhichao Liang, Zixiang Luo, Keyin Liu, Jingwei Qiu, Quanying Liu

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.

EEG Electroencephalogram (EEG) +1

Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review

no code implementations5 Feb 2021 Jie Yuan, Xuming Ran, Keyin Liu, Chen Yao, Yi Yao, Haiyan Wu, Quanying Liu

Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.

BIG-bench Machine Learning EEG +2

Riemannian Manifold Optimization for Discriminant Subspace Learning

no code implementations20 Jan 2021 Wanguang Yin, Zhengming Ma, Quanying Liu

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.

General Classification Image Classification +1

HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction

no code implementations18 Jan 2021 Wanguang Yin, Youzhi Qu, Zhengming Ma, Quanying Liu

However, most of tensor decomposition methods are the linear feature extraction techniques, which are unable to reveal the nonlinear structure within high-dimensional data.

Clustering Dimensionality Reduction +4

Bigeminal Priors Variational auto-encoder

no code implementations5 Oct 2020 Xuming Ran, Mingkun Xu, Qi Xu, Huihui Zhou, Quanying Liu

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

2 code implementations24 Sep 2020 Haoming Zhang, Mingqi Zhao, Chen Wei, Dante Mantini, Zherui Li, Quanying Liu

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.

Denoising EEG +1

Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation

no code implementations16 Jul 2020 Xuming Ran, Mingkun Xu, Lingrui Mei, Qi Xu, Quanying Liu

To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs.

Anomaly Detection

Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning

no code implementations13 Jun 2019 Quanying Liu, Haiyan Wu, Anqi Liu

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.

Decision Making reinforcement-learning +1

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