Chen Liu
Orcid: 0000-0003-1635-3479Affiliations:
- Tianjin University, School of Electrical Engineering and Automation, China (PhD 2016)
- Case Western Reserve University, Department of Electrical Engineering and Computer Science, Cleveland, OH, USA (2015)
According to our database1,
Chen Liu
authored at least 43 papers
between 2013 and 2024.
Collaborative distances:
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Bibliography
2024
IEEE Trans. Biomed. Circuits Syst., February, 2024
Multi-origins of pathological theta oscillation from neuron to network inferred by a combined data and model study with cubature Kalman filter.
Commun. Nonlinear Sci. Numer. Simul., 2024
Kinematic-driven human-robot interaction system with deep learning for flexible acupuncture needling manipulations.
Biomed. Signal Process. Control., 2024
Biomed. Signal Process. Control., 2024
Model-Based Closed-Loop Seizure Suppression: Algorithm Development and Hardware Implementation.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2024
Deep Brain Stimulation Reshapes the Brain's Internal State Observed by Microstate-Improved Brain Network Analysis.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2024
2023
Probing the flexible internal state transition and low-dimensional manifold dynamics of human brain with acupuncture.
Biomed. Signal Process. Control., April, 2023
An Enhanced EEG Microstate Recognition Framework Based on Deep Neural Networks: An Application to Parkinson's Disease.
IEEE J. Biomed. Health Informatics, March, 2023
Multi-compartment structure optimizes the deep learning network from the biophysical perspective.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2023
A Closed-Loop Electrophysiological Hardware Prototype to Estimate and Control Neuronal States.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2023
2022
Intensity-Varied Closed-Loop Noise Stimulation for Oscillation Suppression in the Parkinsonian State.
IEEE Trans. Cybern., 2022
Subthalamic and pallidal stimulation in Parkinson's disease induce distinct brain topological reconstruction.
NeuroImage, 2022
Decoding Digital Visual Stimulation From Neural Manifold With Fuzzy Leaning on Cortical Oscillatory Dynamics.
Frontiers Comput. Neurosci., 2022
Modulation of cortical oscillations by periodic electrical stimulation is frequency-dependent.
Commun. Nonlinear Sci. Numer. Simul., 2022
Adaptive closed-loop control strategy inhibiting pathological basal ganglia oscillations.
Biomed. Signal Process. Control., 2022
Oscillation suppression effects of intermittent noisy deep brain stimulation induced by coordinated reset pattern based on a computational model.
Biomed. Signal Process. Control., 2022
Analysis of Brain Functional Network Based on EEG Signals for Early-Stage Parkinson's Disease Detection.
IEEE Access, 2022
An Accelerometer-based Wearable Multi-node Motion Detection System of Freezing of Gait in Parkinson's Disease.
Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2022
2021
Delayed Feedback-Based Suppression of Pathological Oscillations in a Neural Mass Model.
IEEE Trans. Cybern., 2021
2020
Supervised Network-Based Fuzzy Learning of EEG Signals for Alzheimer's Disease Identification.
IEEE Trans. Fuzzy Syst., 2020
Firing Rate Oscillation and Stochastic Resonance in Cortical Networks With Electrical-Chemical Synapses and Time Delay.
IEEE Trans. Fuzzy Syst., 2020
Multiple Stochastic Resonances and Oscillation Transitions in Cortical Networks With Time Delay.
IEEE Trans. Fuzzy Syst., 2020
The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations.
Neural Networks, 2020
Neural Network-Based Closed-Loop Deep Brain Stimulation for Modulation of Pathological Oscillation in Parkinson's Disease.
IEEE Access, 2020
2019
Design of Hidden-Property-Based Variable Universe Fuzzy Control for Movement Disorders and Its Efficient Reconfigurable Implementation.
IEEE Trans. Fuzzy Syst., 2019
Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks.
IEEE Trans. Cybern., 2019
IEEE Trans. Cybern., 2019
2018
IEEE Trans. Neural Networks Learn. Syst., 2018
Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia-thalamic network.
Neural Networks, 2018
Cost-efficient FPGA implementation of a biologically plausible dopamine neural network and its application.
Neurocomputing, 2018
FPGA implementation of hippocampal spiking network and its real-time simulation on dynamical neuromodulation of oscillations.
Neurocomputing, 2018
2017
IEEE Trans. Neural Networks Learn. Syst., 2017
Efficient hardware implementation of the subthalamic nucleus-external globus pallidus oscillation system and its dynamics investigation.
Neural Networks, 2017
Neural mass models describing possible origin of the excessive beta oscillations correlated with Parkinsonian state.
Neural Networks, 2017
2016
Neurocomputing, 2016
2015
Variable universe fuzzy closed-loop control of tremor predominant Parkinsonian state based on parameter estimation.
Neurocomputing, 2015
Adaptive Control of Parkinson's State Based on a Nonlinear Computational Model with Unknown Parameters.
Int. J. Neural Syst., 2015
Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models.
Commun. Nonlinear Sci. Numer. Simul., 2015
Adaptive stochastic resonance in self-organized small-world neuronal networks with time delay.
Commun. Nonlinear Sci. Numer. Simul., 2015
2014
Model-based iterative learning control of Parkinsonian state in thalamic relay neuron.
Commun. Nonlinear Sci. Numer. Simul., 2014
The effects of time delay on the stochastic resonance in feed-forward-loop neuronal network motifs.
Commun. Nonlinear Sci. Numer. Simul., 2014
Proceedings of the 7th International Conference on Biomedical Engineering and Informatics, 2014
2013
Closed-Loop Control of the thalamocortical Relay Neuron's Parkinsonian State Based on Slow Variable.
Int. J. Neural Syst., 2013