scholarly journals Increased delta power as a scalp marker of epileptic activity: a simultaneous scalp and intracranial EEG study

2021 ◽  
Author(s):  
Pia De Stefano ◽  
Margherita Carboni ◽  
Renaud Marquis ◽  
Laurent Spinelli ◽  
Margitta Seeck ◽  
...  
Keyword(s):  
Epileptic Activity ◽  
Intracranial Eeg ◽  
Delta Power

A skew-based method for identifying intracranial EEG channels with epileptic activity without detecting spikes, ripples, or fast ripples

2020 ◽  
Vol 131 (1) ◽  
pp. 183-192 ◽  
Author(s):  
Anne H. Mooij ◽  
Birgit Frauscher ◽  
Jean Gotman ◽  
Geertjan J.M. Huiskamp
Keyword(s):  
Epileptic Activity ◽  
Intracranial Eeg

scholarly journals Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Petr Nejedly ◽  
Vaclav Kremen ◽  
Vladimir Sladky ◽  
Jan Cimbalnik ◽  
Petr Klimes ◽  
...  
Keyword(s):  
Signal Processing ◽  
Physiological Activity ◽  
Digital Signal ◽  
Epileptic Activity ◽  
University Hospital ◽  
Clinical Neurology ◽  
Intracranial Eeg ◽  
Eeg Data ◽  
Signal Processing Methods

Abstract EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable. In this study, we publicly share annotated intracranial EEG data clips from two institutions: Mayo Clinic, MN, USA and St. Anne’s University Hospital Brno, Czech Republic. The dataset contains intracranial EEG that are labeled into three groups: physiological activity, pathological/epileptic activity, and artifactual signals. The dataset published here should support and facilitate training of generalized machine learning and digital signal processing methods for intracranial EEG and promote research reproducibility. Along with the data, we also propose a statistical method that is recommended for comparison of candidate classifier performance utilizing out-of-institution/out-of-patient testing.

Simultaneous Magnetoencephalography and Intracranial EEG Registration: Technical and Clinical Aspects

2008 ◽  
Vol 25 (6) ◽  
pp. 331-339 ◽  
Author(s):  
Marta Santiuste ◽  
Rafal Nowak ◽  
Antonio Russi ◽  
Thais Tarancon ◽  
Bartolome Oliver ◽  
...  
Keyword(s):  
Clinical Aspects ◽  
Intracranial Eeg
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