Research Information System
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.16, pp.111-123, 2008 (SCI-Expanded)
https://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=3550&context=elektrik
In the present study, the performances
of two well-known linear filtering techniques are compared for extraction of
auditory Evoked Potential (EP) from a relatively small number of sweeps. Both
experimental and simulated data are filtered by the two algorithms into two
groups. Group A consists of Wiener filtering (WF) applications, where
conventional WF and Coherence Weighted WF (CWWF)) have been assessed in
combination with the Subspace Method (SM). Group B consists of the well-known
adaptive filtering algorithms Least Mean Square (LMS), Recursive Least Square
(RLS), and one-step Kalman filtering (KF). Both groups are tested with respect
to signal-to-noise ratio (SNR) enhancement by comparing to the traditional
ensemble averaging (EA). We observed that KF is the best method among them. The
application of the SM before filtering improves the performance of the LMS and
the assessments of WF where the CWWF works better than the conventional WF in
that case.
Conclusion
and Discussion: The results show that, the SM can remove a large amount of EEG
noise. In addition, the characteristic of the EEG noise remaining on the
projections renders white noise. Thus, the SMCWWF was found to be better than
the SMWF for all data sets. Thus CWWF is the better alternative to the WF in
spite of its higher computational complexity. In the Group B, the RLS and KF
were better compared to EA. The RLS is the best filter in simulations, whereas
the KF provides the highest performance in experimental studies. When we
analyze the KF after 128 sweeps, it shows characteristic of a low-pass filter,
which has a narrower bandwidth, compared to the RLS filter, indicating the
possibility of better performance. The LMS filter performance depends on 1) the
number of sweeps, 2) the step size parameter, 3) the filter length, and 4) the
input SNR of single sweeps. It was, in general, found unsuccessful for low
input SNR cases. The selection of step size parameter was assumed to be the
crucial factor in the performance. To obtain a better performance with the LMS
filtering, various methods were proposed which explore an optimum stepsize at
each iteration [8]. In another study, the optimum value was determined
methodologically considering the filter length, input signal variance and the
desired signal [2]. In the present study, these approaches are not attempted;
instead the performance of the SMLMS algorithm is tested. The SMLMS algorithm
appeared to be relatively less sensitive to the step size and showed better
performance compared to the EA in both experimental and simulation trials.
However, its performance in the pseudo-simulations proves again unsatisfactory.
In conclusion, we leave the following matters for future work: 1) the use of
optimal step size, and, 2) exploring further properties of the SMLMS algorithm
for better performance. In conclusion, most of the basic linear estimation
techniques show definitely better performance than EA in extracting the EPs.
The KF effectively reduce the experimental time (to one-fourth of that required
by EA). The SM proves to be a useful pre-EEG filter.