Search Results for author: Quanying Liu

Found 11 papers, 1 papers with code

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.

Deep Koopman-operator based model predictive control for closed-loop electrical neurostimulation in epilepsy

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

Our framework combines a deep Koopman-operator based model for seizure prediction in an approximated finite dimensional linear dynamics and the model predictive control (MPC) for designing optimal seizure suppression strategies.

Seizure prediction

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.

EEG Feature Engineering

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.

Classification General Classification +2

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 Denoising

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

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