Abstract
The auditory evoked potential (AEP) is an electric potential generated in the brain in response to auditory stimuli. It has clinical importance in the detection of newborn infant hearing loss. The signal to noise ratio (SNR) of the AEP is low, so signal averaging is typically employed to estimate it. Often, thousands of trials must be averaged before a sufficiently high SNR estimate is obtained.
In this research, we have developed a new AEP averaging method called subspace averaging. The subspace averaging method projects onto the signal subspace: the span of the principal eigenvectors of the signal correlation matrix. The signal subspace has low dimensionality and captures the key features of the signal. We compare the signal-to-noise ratio (SNR) estimates of the conventional averaging method and the subspace averaging method. The subspace average has lower noise power and therefore has a higher SNR compared to the conventional average. Further, we have demonstrated that a discrete wavelet transform (DWT) filter bank can be employed to further boost the performance of subspace averaging.
Degree Date
Spring 5-19-2018
Document Type
Thesis
Degree Name
M.S.E.E.
Department
Electrical and Computer Engineering
Advisor
Carlos E. Davila
Number of Pages
51
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
wang, Xiaoliang, "Subspace Averaging of Auditory Evoked Potentials" (2018). Electrical Engineering Theses and Dissertations. 6.
https://scholar.smu.edu/engineering_electrical_etds/6
