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361 results on '"Liu, Keyin"'

14. Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy

Author

Liang, Zhichao, Luo, Zixiang, Liu, Keyin, Qiu, Jingwei, and Liu, Quanying

Subjects
Electrical Engineering and Systems Science - Systems and Control, Quantitative Biology - Neurons and Cognition
Abstract

Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. 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. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.

Published
2021

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

Author

Yuan, Jie, Ran, Xuming, Liu, Keyin, Yao, Chen, Yao, Yi, Wu, Haiyan, and Liu, Quanying

Subjects
Computer Science - Machine Learning
Abstract

Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: i) the conventional machine learning approach combining manual feature engineering and classifiers, ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.

Published
2021

29. Harmonizing Thickness and Permeability in Bone Tissue Engineering: A Novel Silk Fibroin Membrane Inspired by Spider Silk Dynamics (Adv. Mater. 13/2024)

Author

Chen, Wenze, primary, Liu, Keyin, additional, Liao, Xiaoyu, additional, Wu, Jing, additional, Chen, Lu, additional, Yang, Zihan, additional, Wang, Xiping, additional, Liao, Yinxiu, additional, Fu, Guiqiang, additional, Yang, Xiaonian, additional, Wang, Zishuo, additional, Qu, Guanlin, additional, Wang, Li, additional, Zhou, Yuqiong, additional, Zhang, ZhiYuan, additional, Yang, Chi, additional, Ni, Siyuan, additional, Zheng, Jisi, additional, Tao, Tiger H., additional, and Zou, Duohong, additional

Published
2024
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38. Advanced Nerve Regeneration Enabled by Neural Conformal Electronic Stimulators Enhancing Mitochondrial Transport

Author

Bai, Hao, primary, Liu, Keyin, additional, Zhang, Siqi, additional, Yang, Huiran, additional, Wang, Jing, additional, Chen, Hongli, additional, Li, Jia, additional, Yang, Qian, additional, Peng, Bo, additional, Zhu, Ziyi, additional, Ni, Siyuan, additional, Lei, Wei, additional, Tao, Tiger. H., additional, and Feng, Yafei, additional

Published
2024
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41. Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy.

Author

Qin, Nan, Zhang, Shaoqing, Jiang, Jianjuan, Corder, Stephanie Gilbert, Qian, Zhigang, Zhou, Zhitao, Lee, Woonsoo, Liu, Keyin, Wang, Xiaohan, Li, Xinxin, Shi, Zhifeng, Mao, Ying, Bechtel, Hans A, Martin, Michael C, Xia, Xiaoxia, Marelli, Benedetto, Kaplan, David L, Omenetto, Fiorenzo G, Liu, Mengkun, and Tao, Tiger H

Subjects
Animals, Spiders, Bombyx, Silk, Fibroins, Imaging, Three-Dimensional, Spectroscopy, Fourier Transform Infrared, Molecular Conformation, Imaging, Three-Dimensional, Spectroscopy, Fourier Transform Infrared
Abstract

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.

Published
2016

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