Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records.This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records.  We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning.  We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately.  We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal.  The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.
ICA
Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records.This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records.  We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning.  We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately.  We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal.  The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.
ICA
Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records.This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records.  We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning.  We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately.  We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal.  The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.
ICA
Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records.This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records.  We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning.  We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately.  We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal.  The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.
ICA