We present two techniques utilizing independent component analysis (ICA) to remove large muscle artifacts from @entity9808 ( @entity9808 )-evoked EEG signals. The first one is a novel semi-automatic technique, called @entity14782 ( @entity14782 ). @entity14782 is a modification of the deflation mode of the FastICA algorithm; with an enhanced independent component search, @entity14782 is an effective tool for removing the large, spiky muscle artifacts. The second technique, called manual method (MaM) makes use of the symmetric mode of FastICA and the artifactual components are visually selected by the user. In order to evaluate the success of the artifact removal methods, four different quality parameters, based on curve comparison and frequency analysis, were studied. The dorsal premotor cortex (dPMC) and Broca's area (BA) were stimulated with @entity9808 . Both methods removed the very large muscle artifacts recorded after stimulation of these brain areas. However, @entity14782 was more stable, less subjective, and thus also faster to use than MaM. Until now, examining lateral areas of the cortex with @entity9808 -EEG has been restricted because of strong muscle artifacts. The methods described here can remove those muscle artifacts, allowing one to study lateral areas of the @entity1 brain, e.g., BA, with @entity9808 -EEG.

Removal of large muscle artifacts from XXXX -evoked EEG by independent component analysis.

@entity1 :: ('9606', 'Species') :: ['human'] @entity9808 :: ('MESH:C536917', 'Disease') :: ['TMS', 'transcranial magnetic stimulation'] @entity14782 :: ('MESH:C564835', 'Disease') :: ['enhanced deflation method', 'EDM']
@entity9808 :: (MESH:C536917,Disease) :: ['transcranial magnetic stimulation']
