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Levin Kuhlmann
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2020 – today
- 2024
- [j21]Naima Vahab, Tarun Bonu, Levin Kuhlmann
, Mirana Ramialison, Sonika Tyagi
Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data. Comput. Biol. Medicine 171: 108068 (2024) - [c23]Yueyang Liu, Artemio Soto-Breceda, Mark J. Cook, Philippa J. Karoly, David B. Grayden, Levin Kuhlmann, Dean R. Freestone, Daniel Schmidt:
Forecasting events in multidimensional electroencephalographic brain data: Application to epileptic seizure prediction. FUSION 2024: 1-8 - [c22]Yun Zhao, David B. Grayden, Mario Boley, Yueyang Liu, Philippa J. Karoly, Mark J. Cook, Levin Kuhlmann:
Inference-based time-resolved chaos analysis of brain models: application to focal epilepsy. FUSION 2024: 1-8 - [c21]Mladen Rakovic
Measuring Affective and Motivational States as Conditions for Cognitive and Metacognitive Processing in Self-Regulated Learning. LAK 2024: 701-712 - [i12]Jarrad Rinaldo, Levin Kuhlmann, Jason Friedman, Gideon Kowadlo:
Left/Right Brain, human motor control and the implications for robotics. CoRR abs/2401.14057 (2024) - [i11]Luke Yang, Levin Kuhlmann, Gideon Kowadlo:
Augmenting Replay in World Models for Continual Reinforcement Learning. CoRR abs/2401.16650 (2024) - [i10]Grant Nicholas, Levin Kuhlmann, Gideon Kowadlo:
Graceful task adaptation with a bi-hemispheric RL agent. CoRR abs/2407.11456 (2024) - 2023
- [j20]D. Nhu, M. Janmohamed, L. Shakhatreh, O. Gonen, Piero Perucca
Automated Interictal Epileptiform Discharge Detection from Scalp EEG Using Scalable Time-series Classification Approaches. Int. J. Neural Syst. 33(1): 2350001:1-2350001:18 (2023) - [j19]Yun Zhao
Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions. Int. J. Neural Syst. 33(5): 2350024:1-2350024:20 (2023) - [c20]Yueyang Liu, Siqi Xia, Artemio Soto-Breceda, Philippa J. Karoly
Model Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset. EMBC 2023: 1-4 - [c19]Yun Zhao
Inference-based time-resolved stability analysis of nonlinear whole-cortex modeling: application to Xenon anaesthesia. EMBC 2023: 1-4 - [i9]Yueyang Liu, Artemio Soto-Breceda, Yun Zhao, Phillipa Karoly, Mark J. Cook, David B. Grayden, Daniel Schmidt, Levin Kuhlmann
Brain Model State Space Reconstruction Using an LSTM Neural Network. CoRR abs/2301.08391 (2023) - 2022
- [j18]Yun Zhao
Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm. NeuroImage 263: 119592 (2022) - [j17]Chuen Rue Ng
Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps. Sensors 22(20): 8079 (2022) - [c18]Yasmin M. Massoud, Mennatallah Sherif, Levin Kuhlmann
Deep Learning Models for Seizure Prediction using the Wavelet Transform. ICM 2022: 164-168 - [c17]Maurice Ntahobari, Levin Kuhlmann
Enhanced Extra Trees Classifier for Epileptic Seizure Prediction. ICSPIS 2022: 175-179 - [c16]Mohamed Gamil, Yasmin M. Massoud, Levin Kuhlmann
Predicting epileptic seizures from raw EEG signals using advanced deep learning techniques. NILES 2022: 10-14 - [c15]Ahmad A. Ahmad, Yasmin M. Massoud, Levin Kuhlmann
FPGA Implementation of Epileptic Seizure Detection using Artificial Neural Network. NILES 2022: 340-344 - 2021
- [c14]Hoai An Nguyen
Evaluation of Channel Performance in Seizure Prediction. ACSW 2021: 21:1-21:4 - [c13]Shiva Maleki Varnosfaderani, Rihat Rahman, Nabil J. Sarhan
A Two-Layer LSTM Deep Learning Model for Epileptic Seizure Prediction. AICAS 2021: 1-4 - [c12]Yasmin M. Massoud, Levin Kuhlmann
Comparison of Patient Specific and General Classification of Epileptic Seizure Prediction. ICM 2021: 116-119 - [c11]Hashini Senaratne, Levin Kuhlmann
A Multimodal Dataset and Evaluation for Feature Estimators of Temporal Phases of Anxiety. ICMI 2021: 52-61 - [i8]Jens Müller, Hongliu Yang, Matthias Eberlein, Georg Leonhardt, Ortrud Uckermann, Levin Kuhlmann, Ronald Tetzlaff:
Coherent False Seizure Prediction in Epilepsy, Coincidence or Providence? CoRR abs/2110.13550 (2021) - 2020
- [j16]Yashpal Ramakrishnaiah, Levin Kuhlmann
Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA). Comput. Biol. Medicine 127: 104028 (2020) - [c10]Yu Liu, Shobi Sivathamboo
Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study. ACSW 2020: 37:1-37:8 - [c9]Sheng Wong, Levin Kuhlmann
Computationally Efficient Epileptic Seizure Prediction based on Extremely Randomised Trees. ACSW 2020: 39:1-39:3 - [c8]Satya Borgohain
Self-organising Neural Network Hierarchy. Australasian Conference on Artificial Intelligence 2020: 359-370 - [i7]Khansa Rasheed, Adnan Qayyum, Junaid Qadir, Shobi Sivathamboo, Patrick Kwan, Levin Kuhlmann, Terence J. O'Brien, Adeel Razi:
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review. CoRR abs/2002.01925 (2020) - [i6]Khansa Rasheed, Junaid Qadir, Terence J. O'Brien, Levin Kuhlmann, Adeel Razi:
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction. CoRR abs/2012.00430 (2020)
2010 – 2019
- 2019
- [j15]Nhan Duy Truong, Levin Kuhlmann
Epileptic Seizure Forecasting With Generative Adversarial Networks. IEEE Access 7: 143999-144009 (2019) - [i5]Ramy Hussein, Mohamed Osama Ahmed, Rabab Ward, Z. Jane Wang, Levin Kuhlmann, Yi Guo:
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction. CoRR abs/1904.03603 (2019) - 2018
- [j14]Nhan Duy Truong
Integer Convolutional Neural Network for Seizure Detection. IEEE J. Emerg. Sel. Topics Circuits Syst. 8(4): 849-857 (2018) - [j13]Nhan Duy Truong
Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105: 104-111 (2018) - [j12]Philippa J. Karoly
Seizure pathways: A model-based investigation. PLoS Comput. Biol. 14(10) (2018) - [c7]Matthias Eberlein, Raphael Hildebrand, Ronald Tetzlaff
Convolutional Neural Networks for Epileptic Seizure Prediction. BIBM 2018: 2577-2582 - [i4]Nhan Duy Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Omid Kavehei:
Semi-supervised Seizure Prediction with Generative Adversarial Networks. CoRR abs/1806.08235 (2018) - [i3]Matthias Eberlein, Raphael Hildebrand, Ronald Tetzlaff, Nico Hoffmann, Levin Kuhlmann, Benjamin H. Brinkmann, Jens Müller:
Convolutional Neural Networks for Epileptic Seizure Prediction. CoRR abs/1811.00915 (2018) - 2017
- [j11]Nhan Duy Truong
Supervised learning in automatic channel selection for epileptic seizure detection. Expert Syst. Appl. 86: 199-207 (2017) - [j10]Elma O'Sullivan-Greene, Levin Kuhlmann
Probing to Observe Neural Dynamics Investigated with Networked Kuramoto Oscillators. Int. J. Neural Syst. 27(1): 1650038:1-1650038:13 (2017) - [j9]Dean R. Freestone, Kelvin J. Layton
Statistical Performance Analysis of Data-Driven Neural Models. Int. J. Neural Syst. 27(1): 1650045:1-1650045:16 (2017) - [j8]Levin Kuhlmann
Introduction. Int. J. Neural Syst. 27(1): 1702001:1-1702001:2 (2017) - [j7]Levin Kuhlmann
Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based Depth of Anesthesia Monitoring. IEEE Trans. Biomed. Eng. 64(4): 870-881 (2017) - [i2]Nhan Duy Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Andrew Faulks, Omid Kavehei:
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection. CoRR abs/1701.08968 (2017) - [i1]Nhan Duy Truong, Anh Duy Nguyen, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Omid Kavehei:
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis. CoRR abs/1707.01976 (2017) - 2016
- [j6]Levin Kuhlmann
Neural mass model-based tracking of anesthetic brain states. NeuroImage 133: 438-456 (2016) - 2015
- [j5]Michelle S. Chong
Parameter and State Estimation of Nonlinear Systems Using a Multi-Observer Under the Supervisory Framework. IEEE Trans. Autom. Control. 60(9): 2336-2349 (2015) - 2014
- [j4]Elma O'Sullivan-Greene
Observability limits for networked oscillators. Autom. 50(4): 1087-1099 (2014) - [j3]Levin Kuhlmann
Approximate, Computationally Efficient Online Learning in Bayesian Spiking Neurons. Neural Comput. 26(3): 472-496 (2014) - [c6]Michelle S. Chong
State and parameter estimation of nonlinear systems: A multi-observer approach. CDC 2014: 1067-1072 - 2012
- [j2]Michelle Chong
A robust circle criterion observer with application to neural mass models. Autom. 48(11): 2986-2989 (2012) - [c5]Romain Postoyan
Parameter and state estimation for a class of neural mass models. CDC 2012: 2322-2327 - [c4]Levin Kuhlmann
Online learning in Bayesian Spiking Neurons. IJCNN 2012: 1-6 - 2011
- [c3]Michelle Chong, Romain Postoyan, Dragan Nesic, Levin Kuhlmann, Andrea Varsavsky:
A circle criterion observer for estimating the unmeasured membrane potential of neuronal populations. AuCC 2011: 493-498 - [c2]Dean R. Freestone
Probing for cortical excitability. EMBC 2011: 1644-1647
2000 – 2009
- 2009
- [c1]Elma O'Sullivan-Greene
Observability issues in networked clocks with applications to epilepsy. CDC 2009: 3527-3532 - 2002
- [j1]Levin Kuhlmann
Summation of Spatiotemporal Input Patterns in Leaky Integrate-and-Fire Neurons: Application to Neurons in the Cochlear Nucleus Receiving Converging Auditory Nerve Fiber Input. J. Comput. Neurosci. 12(1): 55-73 (2002)
Coauthor Index
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