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Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)

Graupe Daniel, Graupe

Fourth edition., 2019

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Principles Of Artificial Neural Networks

Graupe, Daniel

1, 2019

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Deep learning neural networks: Design and case studies

Graupe, Daniel

2016

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Recognition and prediction of individual and combined muscular activation modes via surface EMG analysis

European journal of translational myology, 2010-09, Vol.20 (3), p.131-138

Daniel Graupe

2010

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A model for simulating Local Field Potential in the thalamus of Essential Tremor patient during deep brain stimulation

BMC neuroscience, 2012-07, Vol.13 (S1), p.P40-P40, Article P40

2012

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Relating thalamic neuronal activity and EMG for validating predictive control of deep-brain stimulation in Essential Tremor patients

BMC neuroscience, 2011-07, Vol.12 (S1), p.P129-P129, Article P129

2011

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A neural-network-based detection of epilepsy

Neurological research (New York), 2004-01, Vol.26 (1), p.55-60

2004

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Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design

Journal of neural engineering, 2013-06, Vol.10 (3), p.036019-036019

2013

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Automated Prediction of Apnea and Hypopnea, Using a LAMSTAR Artificial Neural Network

American journal of respiratory and critical care medicine, 2010-04, Vol.181 (7), p.727-733

2010

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Letters to the Editor. Closed-loop electrochemical feedback system for DBS

Journal of neurosurgery, 2014-09, Vol.121 (3), p.762-763

2014

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Closed-loop electrochemical feedback system for DBS

Journal of neurosurgery, 2014-09, Vol.121 (3), p.762-763

2014

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Adaptively controlling deep brain stimulation in essential tremor patient via surface electromyography

Neurological research (New York), 2010-11, Vol.32 (9), p.899-904

2010

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Recognition and prediction of individual and combined muscular activation modes via surface EMG analysis

European journal of translational myology, 2010-09, Vol.20 (3), p.131

Graupe, Daniel

2010

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Who May Benefit From On‐Demand Control of Deep Brain Stimulation? Noninvasive Evaluation of Parkinson Patients

Neuromodulation (Malden, Mass.), 2018-08, Vol.21 (6), p.611-616

2018

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An overview of the state of the art of noninvasive FES for independent ambulation by thoracic level paraplegics

Neurological research (New York), 2002-07, Vol.24 (5), p.431-442

Graupe, Daniel

2002

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Real-time prediction of disordered breathing events in people with obstructive sleep apnea

Sleep & breathing, 2015-03, Vol.19 (1), p.205-212

2015

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Walking performance, medical outcomes and patient training in FES of innervated muscles for ambulation by thoracic-level complete paraplegics

Neurological research (New York), 2008-03, Vol.30 (2), p.123-130

2008

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A novel large-memory neural network as an aid in medical diagnosis applications

IEEE journal of biomedical and health informatics, 2001-09, Vol.5 (3), p.202-209

2001

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The role of neuroengineering in neurological research and in neurological practice

Neurological research (New York), 2004-09, Vol.26 (6), p.611-612

Graupe, Daniel

2004

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Fetal electrocardiogram extraction

American journal of obstetrics and gynecology, 2005-12, Vol.193 (6), p.S136-S136

2005

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Pathological tremor prediction using surface EMG and acceleration: potential use in “ON-OFF” demand driven deep brain stimulator design

Journal of neural engineering, 2013-05, Vol.10 (3), p.036019-036019

2013

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Special review section on neural prostheses

Neurological research (New York), 2002-07, Vol.24 (5), p.411-412

Graupe, Daniel

2002

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On the need for adaptive learning in on-demand Deep Brain Stimulation for Movement Disorders

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, Vol.2018, p.2190-2193

2018

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Stochastic modeling of the neuronal activity in the subthalamic nucleus and model parameter identification from Parkinson patient data

Biological cybernetics, 2010-10, Vol.103 (4), p.273-283

2010

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Towards fully automated closed-loop Deep Brain Stimulation in Parkinson's disease patients: A LAMSTAR-based tremor predictor

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, Vol.2015, p.2616-2619

2015

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Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)

Principles Of Artificial Neural Networks

Deep learning neural networks: Design and case studies

Recognition and prediction of individual and combined muscular activation modes via surface EMG analysis

A model for simulating Local Field Potential in the thalamus of Essential Tremor patient during deep brain stimulation

Relating thalamic neuronal activity and EMG for validating predictive control of deep-brain stimulation in Essential Tremor patients

A neural-network-based detection of epilepsy

Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design

Automated Prediction of Apnea and Hypopnea, Using a LAMSTAR Artificial Neural Network

Letters to the Editor. Closed-loop electrochemical feedback system for DBS

Closed-loop electrochemical feedback system for DBS

Adaptively controlling deep brain stimulation in essential tremor patient via surface electromyography

Recognition and prediction of individual and combined muscular activation modes via surface EMG analysis

Who May Benefit From On‐Demand Control of Deep Brain Stimulation? Noninvasive Evaluation of Parkinson Patients

An overview of the state of the art of noninvasive FES for independent ambulation by thoracic level paraplegics

Real-time prediction of disordered breathing events in people with obstructive sleep apnea

Walking performance, medical outcomes and patient training in FES of innervated muscles for ambulation by thoracic-level complete paraplegics

A novel large-memory neural network as an aid in medical diagnosis applications

The role of neuroengineering in neurological research and in neurological practice

Fetal electrocardiogram extraction

Pathological tremor prediction using surface EMG and acceleration: potential use in “ON-OFF” demand driven deep brain stimulator design

Special review section on neural prostheses

On the need for adaptive learning in on-demand Deep Brain Stimulation for Movement Disorders

Stochastic modeling of the neuronal activity in the subthalamic nucleus and model parameter identification from Parkinson patient data

Towards fully automated closed-loop Deep Brain Stimulation in Parkinson's disease patients: A LAMSTAR-based tremor predictor

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