Research Information System
BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS, vol.22, no.1, pp.19-24, 2010 (SCI-Expanded, Scopus)
ABSTRACT
In the present study, linear orthogonal projection
algorithms (least square sense linear mapping (LSLM), minimum variance
estimation (MVE), spectral domain estimation (SDC) and time domain constraint
(TDC)) have been applied to reduce the background EEG noise on small number of
trials elicited by auditory stimuli. These methods are compared to each other
with respect to eigendecomposition based spectral signal-to-noise-ratio (SSNR)
in tests where the grand average of experimental observations is considered as
the template evoked potential (EP) signal. The actual ongoing EEG series and
single-sweep EP are summed in pseudosimulations. The LSLM having simplest
formulation is found to be most useful pre-filter among those methods in
removing large amount of the noise without loss of information about EP
components since both EEG noise level and EP component variations are highly
correlated with eigenspectra of the raw data.