scholarly journals Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively

2018 ◽  
Vol 129 (1) ◽  
pp. 296-307 ◽  
Author(s):  
Shoichi Shimamoto ◽  
Zachary J. Waldman ◽  
Iren Orosz ◽  
Inkyung Song ◽  
Anatol Bragin ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Intracranial Eeg ◽  
Eeg Recordings

scholarly journals Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

2018 ◽  
Vol 307 ◽  
pp. 125-137 ◽  
Author(s):  
Sebastian Michelmann ◽  
Matthias S. Treder ◽  
Benjamin Griffiths ◽  
Casper Kerrén ◽  
Frédéric Roux ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven ◽  
Intracranial Eeg

Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties

NeuroImage ◽  
2014 ◽  
Vol 101 ◽  
pp. 425-439 ◽  
Author(s):  
Nigel C. Rogasch ◽  
Richard H. Thomson ◽  
Faranak Farzan ◽  
Bernadette M. Fitzgibbon ◽  
Neil W. Bailey ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Network Properties ◽  
Eeg Recordings

Independent component analysis applied to the removal of artifacts in combined TMS/EEG recordings

2007 ◽  
Vol 58 ◽  
pp. S243
Author(s):  
Shigeki Takauchi ◽  
Yasoichi Nakajima ◽  
Hiroshi Kadota ◽  
Hirofumi Sekiguchi ◽  
Kenji Kansaku
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Recordings

scholarly journals Artifact Removing From Eeg Recordings Using Independent Component Analysis with High-order Statistics

2021 ◽  
Vol 15 ◽  
pp. 76-85
Author(s):  
Theodor D. Popescu
Keyword(s):  
Principal Component Analysis ◽  
Eye Movements ◽  
Independent Component Analysis ◽  
Principal Component ◽  
Event Related Potentials ◽  
Component Analysis ◽  
Independent Component ◽  
Related Potentials ◽  
Eeg Recordings ◽  
High Order Statistics

Many methods have been proposed to remove artifacts from EEG recordings especially those arising from eye movements and blinks. Often regression in time and frequency domain on parallel EEG and electrooculographic recordings is used, but this approach can become problematic in some cases. Use of Principal Component Analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. This method is not effective when the activations from cerebral activity and artifacts have comparable amplitudes. In this paper it is presented a generally applicable method for removing a wide variety of artifacts from EEG recordings based on Independent Component Analysis (ICA) with highorder statistics. The method is applied with good results in the analysis of a sample lowpass event -related potentials (ERP) data.

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