In estimating auditory Evoked Potentials (EPs) from ongoing EEG the
number of sweeps should be reduced to decrease the experimental time and to
increase the reliability of diagnosis. The ¯ / rst goal of this study is to demon-
strate the use of basic estimation techniques in extracting auditory EPs
(AEPs) from small number of sweeps relative to ensemble averaging (EA).
For this purpose, three groups of basic estimation techniques are compared
to the traditional EA with respect to the signal-to-noise ratio(SNR) improve-
ments in extracting the template AEP. Group A includes the combinations
of the Subspace Method (SM) with the Wiener Filtering (WF) approaches
(the conventional WF and coherence weighted WF (CWWF). Group B con-
sists of standard adaptive algorithms (Least Mean Square (LMS), Recursive
Least Square (RLS), and one-step Kalman ¯ / ltering (KF). The regularization
techniques (the Standard Tikhonov Regularization (STR) and the Subspace
Regularization (SR) methods) forms Group C. All methods are tested in sim-
ulations and pseudo-simulations which are performed with white noise and
EEG measurements, respectively. The same methods are also tested with
experimental AEPs. Comparisons based on the output signal-to-noise ratio
(SNR) show that: 1) the KF and STR methods are the best methods among
the algorithms tested in this study,2) the SM can reduce the large amount
of the background EEG noise from the raw data, 3) the LMS and WF algo-
rithms show poor performance compared to EA. The SM should be used as
1
a pre-¯ / lter to increase their performance. 4) the CWWF works better than
the WF when it is combined with the SM, 5) the STR method is better than
the SR method. It is observed that, most of the basic estimation techniques
show de¯ / nitely better performance compared to EA in extracting the EPs.
The KF or the STR e® / ectively reduce the experimental time (to one-fourth of
that required by EA). The SM is a useful pre-¯ / lter to signi¯ / cantly reduce the
noise on the raw data. The KF and STR are shown to be computationally
inexpensive tools to extract the template AEPs and should be used instead
of EA. They provide a clear template AEP for various analysis methods. To
reduce the noise level on single sweeps, the SM can be used as a pre-¯ / lter
before various single sweep analysis methods.
The second goal of this study is to to present a new approach to extract
single sweep AEPs without using a template signal. The SM and a modi-
¯ / ed scale-space ¯ / lter (MSSF) are applied consecutively. The SM is applied
to raw data to increase the SNR. The less-noisy sweeps are then individu-
ally ¯ / ltered with the MSSF. This new approach is assessed in both pseudo-
simulations and experimental studies. The MSSF is also applied to actual
auditory brainstem response (ABR) data to obtain a clear ABR from a rel-
atively small number of sweeps. The wavelet transform coe± / cients (WTCs)
corresponding to the signal and noise become distinguishable after the SM.
The MSSF is an e® / ective ¯ / lter in selecting the WTCs of the noise. The esti-
mated single sweep EPs highly resemble the grand average EP although less
number of sweeps are evaluated. Small amplitude variations are observed
among the estimations. The MSSF applied to EA of 50 sweeps yields an
ABR that best ¯ / ts to the grand average of 250 sweeps. We concluded that
the combination of SM and MSSF is an e± / cient tool to obtain clear single
sweep AEPs. The MSSF reduces the recording time to one-¯ / fth of that re-
quired by EA in template ABR estimation. The proposed approach does not
use a template signal (which is generally obtained using the average of small
number of sweeps). It provides unprecedented results that support the basic
assumptions in the additive signal model.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12606641/index.pdf |
| Date | 01 September 2005 |
| Creators | Aydin, Serap |
| Contributors | Gencer, Nevzat Guneri |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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