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Abstract:
Severe contamination of electroencephalographic (EEG)
activity by eye movements, blinks, and muscle, heart and line noise
presents a serious problem for EEG interpretation and analysis.
Rejecting contaminated EEG segments results in a considerable loss
of data and may be impractical for clinical data. Many methods have
been proposed to remove eye movement and blink artifacts from EEG
recordings. Often regression in the time or frequency domain is
performed on simultaneous EEG and electrooculographic (EOG)
recordings to derive parameters characterizing the appearance and
spread of EOG artifacts in the EEG channels. However, EOG also
carries brain signal (Peters, 1967; Oster Stern, 1980), so
regressing out eye artifacts inevitably involves subtracting a
portion of the relevant EEG signal from each record as well. This
method cannot be used for muscle noise or line noise for which
there is no reference channel for regression. Here, we propose a
new and generally applicable method for removing a wide variety of
artifacts from EEG records. The method is based on an extended
version of an Independent Component Analysis (ICA) algorithm (Bell
Sejnowski, 1995) for performing blind source separation on linear
mixtures of independent source signals that can be sub- or
super-Gaussian. Our results show that ICA can effectively detect,
separate and remove the activity of a wide variety of artifactual
sources in EEG records, with results comparing favorably to those
obtained using regression methods.
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