Levin Kuhlmann

According to our database1, Levin Kuhlmann authored at least 27 papers between 2002 and 2020.

Collaborative distances:



In proceedings 
PhD thesis 



On csauthors.net:


Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review.
CoRR, 2020

Computationally Efficient Epileptic Seizure Prediction based on Extremely Randomised Trees.
Proceedings of the Australasian Computer Science Week, 2020

Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study.
Proceedings of the Australasian Computer Science Week, 2020

Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction.
CoRR, 2019

Epileptic Seizure Forecasting With Generative Adversarial Networks.
IEEE Access, 2019

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.
Neural Networks, 2018

Integer Convolutional Neural Network for Seizure Detection.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2018

Semi-supervised Seizure Prediction with Generative Adversarial Networks.
CoRR, 2018

Convolutional Neural Networks for Epileptic Seizure Prediction.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018

Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based Depth of Anesthesia Monitoring.
IEEE Trans. Biomed. Engineering, 2017

Probing to Observe Neural Dynamics Investigated with Networked Kuramoto Oscillators.
Int. J. Neural Syst., 2017

Int. J. Neural Syst., 2017

Statistical Performance Analysis of Data-Driven Neural Models.
Int. J. Neural Syst., 2017

Supervised learning in automatic channel selection for epileptic seizure detection.
Expert Syst. Appl., 2017

A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis.
CoRR, 2017

Neural mass model-based tracking of anesthetic brain states.
NeuroImage, 2016

Parameter and State Estimation of Nonlinear Systems Using a Multi-Observer Under the Supervisory Framework.
IEEE Trans. Automat. Contr., 2015

Approximate, Computationally Efficient Online Learning in Bayesian Spiking Neurons.
Neural Computation, 2014

Observability limits for networked oscillators.
Automatica, 2014

State and parameter estimation of nonlinear systems: A multi-observer approach.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

A robust circle criterion observer with application to neural mass models.
Automatica, 2012

Online learning in Bayesian Spiking Neurons.
Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

Parameter and state estimation for a class of neural mass models.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Probing for cortical excitability.
Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

A circle criterion observer for estimating the unmeasured membrane potential of neuronal populations.
Proceedings of the 2011 Australian Control Conference, 2011

Observability issues in networked clocks with applications to epilepsy.
Proceedings of the 48th IEEE Conference on Decision and Control, 2009

Summation of Spatiotemporal Input Patterns in Leaky Integrate-and-Fire Neurons: Application to Neurons in the Cochlear Nucleus Receiving Converging Auditory Nerve Fiber Input.
Journal of Computational Neuroscience, 2002