Alonso, J.F.; Poza, J.; Mañanas, M.A.; Romero, S.; Fernández, A.; Hornero, R. Annals of biomedical engineering Vol. 39, num. 1, p. 524-536 DOI: 10.1007/s10439-010-0155-7 Data de publicació: 2011-01 Article en revista
Quantitative electroencephalographic (EEG) analysis
is very useful for diagnosing dysfunctional neural states
and for evaluating drug effects on the brain, among others.
However, the bidirectional contamination between electrooculographic
(EOG) and cerebral activities can mislead and
induce wrong conclusions from EEG recordings. Different
methods for ocular reduction have been developed but only
few studies have shown an objective evaluation of their
performance. For this purpose, the following approaches
were evaluated with simulated data: regression analysis,
adaptive filtering, and blind source separation (BSS). In the
first two, filtered versions were also taken into account by
filtering EOG references in order to reduce the cancellation
of cerebral high frequency components in EEG data.
Performance of these methods was quantitatively evaluated
by level of similarity, agreement and errors in spectral
variables both between sources and corrected EEG recordings.
Topographic distributions showed that errors were
located at anterior sites and especially in frontopolar and
lateral–frontal regions. In addition, these errors were higher
in theta and especially delta band. In general, filtered versions
of time-domain regression and of adaptive filtering with RLS
algorithm provided a very effective ocular reduction. However,
BSS based on second order statistics showed the highest
similarity indexes and the lowest errors in spectral variables.