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Differentiation of specific ripple patterns helps to identify epileptogenic areas for surgical procedures

Clinical neurophysiology, 2014-07, Vol.125 (7), p.1339-1345

2014

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Automatic 80–250Hz “ripple” high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network

Clinical neurophysiology, 2012-09, Vol.123 (9), p.1721-1731

2012

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Spatio-temporal patient–individual assessment of synchronization changes for epileptic seizure prediction

Clinical neurophysiology, 2006-11, Vol.117 (11), p.2399-2413

2006

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Seizure prediction: Time for new, multimodal and ultra-long-term approaches

Clinical neurophysiology, 2022-01, Vol.133, p.152-153

Schulze-Bonhage, Andreas

2022

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A realistic and patient-specific perspective on EEG-based seizure detection

Clinical neurophysiology, 2022-06, Vol.138, p.191-192

Schulze-Bonhage, Andreas

2022

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WE-134. Invasive identification of the epileptogenic zone: Differential diagnostic yield of subdural vs. depth electrode recordings

Clinical neurophysiology, 2022-09, Vol.141, p.S67-S68

2022

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WE-133. Distinguishing subclinical and clinical electroencephalographic patterns in temporal EEG recordings

Clinical neurophysiology, 2022-09, Vol.141, p.S67-S67

2022

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Intracranial correlates of small sharp spikes

Clinical neurophysiology, 2021-09, Vol.132 (9), p.2146-2151

2021

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Automatic 80–250 Hz “ripple” high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network

Clinical neurophysiology, 2012-09, Vol.123 (9), p.1721-1731

2012

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Distinct ictal hippocampal sharp transients in scalp EEG

Clinical neurophysiology, 2020-08, Vol.131 (8), p.1925-1927

2020

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Marker-based ballistocardiographic artifact correction improves spike identification in EEG-fMRI of focal epilepsy patients

Clinical neurophysiology, 2016-08, Vol.127 (8), p.2802-2811

2016

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Source reconstruction based on subdural EEG recordings adds to the presurgical evaluation in refractory frontal lobe epilepsy

Clinical neurophysiology, 2013-03, Vol.124 (3), p.481-491

2013

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How large must an epileptic focus be to cause an electrographic status epilepticus—a case report

Clinical neurophysiology, 2004-10, Vol.115 (10), p.2274-2279

2004

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Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings

Clinical neurophysiology, 2008-01, Vol.119 (1), p.197-211

2008

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P3.18 Computational modeling of epileptogenic zones and its contribution to EEG source localization

Clinical neurophysiology, 2011, Vol.122, p.S70-S70

2011

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Differentiation of specific ripple patterns helps to identify epileptogenic areas for surgical procedures

Automatic 80–250Hz “ripple” high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network

Spatio-temporal patient–individual assessment of synchronization changes for epileptic seizure prediction

Seizure prediction: Time for new, multimodal and ultra-long-term approaches

A realistic and patient-specific perspective on EEG-based seizure detection

WE-134. Invasive identification of the epileptogenic zone: Differential diagnostic yield of subdural vs. depth electrode recordings

WE-133. Distinguishing subclinical and clinical electroencephalographic patterns in temporal EEG recordings

Intracranial correlates of small sharp spikes

Automatic 80–250 Hz “ripple” high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network

Distinct ictal hippocampal sharp transients in scalp EEG

Marker-based ballistocardiographic artifact correction improves spike identification in EEG-fMRI of focal epilepsy patients

Source reconstruction based on subdural EEG recordings adds to the presurgical evaluation in refractory frontal lobe epilepsy

How large must an epileptic focus be to cause an electrographic status epilepticus—a case report

Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings

P3.18 Computational modeling of epileptogenic zones and its contribution to EEG source localization

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