|dc.description.abstract||Artifact reduction in electroencephalogram (EEG) signals is usually
necessary to carry out data analysis appropriately. Despite the large amount of
denoising techniques available with a multichannel setup, there is a lack of e cient
algorithms that remove (not only detect) blink-artifacts from a single channel EEG,
which is of interest in many clinical and research applications. This paper describes
and evaluates the Iterative Template Matching and Suppression (ITMS), a new method
proposed for detecting and suppressing the artifact associated with the blink activity
from a single channel EEG.
Approach: The approach of ITMS consists of (a) an iterative process in which
blink-events are detected and the blink-artifact waveform of the analyzed subject is
estimated, (b) generation of a signal modeling the blink-artifact, and (c) suppression
of this signal from the raw EEG. The performance of ITMS is compared with the
Multi-window Summation of Derivatives within a Window (MSDW) technique using
both synthesized and real EEG data.
Main results: Results suggest that ITMS presents an adequate performance in
detecting and suppressing blink-artifacts from a single channel EEG. When applied
to the analysis of cortical auditory evoked potentials (CAEPs), ITMS provides a
signi cant quality improvement in the resulting responses, i.e. in a cohort of 30 adults,
the mean correlation coe cient improved from 0.37 to 0.65 when the blink-artifacts
were detected and suppressed by ITMS.
Signi cance: ITMS is an e cient solution to the problem of denoising blinkartifacts
in single-channel EEG applications, both in clinical and research elds.
The proposed ITMS algorithm is stable; automatic, since it does not require human
intervention; low-invasive, because the EEG segments not contaminated by blinkartifacts
remain unaltered; and easy to implement, as can be observed in the Matlab
script implemeting the algorithm provided as supporting material.||en_US