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Biophysical Sources of 1/f Noises in Neurological Systems
 Date Issued:
 2016
 Abstract/Description:
 High levels of random noise are a defining characteristic of neurological signals at all levels, from individual neurons up to electroencephalograms (EEG). These random signals degrade the performance of many methods of neuroengineering and medical neuroscience. Understanding this noise also is essential for applications such as realtime braincomputer interfaces (BCIs), which must make accurate control decisions from very short data epochs. The major type of neurological noise is of the socalled 1/ftype, whose origins and statistical nature has remained unexplained for decades. This research provides the first simple explanation of 1/ftype neurological noise based on biophysical fundamentals. In addition, noise models derived from this theory provide validated algorithm performance improvements over alternatives.Specifically, this research defines a new class of formal latentvariable stochastic processes called hidden quantum models (HQMs) which clarify the theoretical foundations of ion channel signal processing. HQMs are based on quantum state processes which formalize timedependent observation. They allow the quantumbased calculation of channel conductance autocovariance functions, essential for frequencydomain signal processing. HQMs based on a particular type of observation protocol called independent activated measurements are shown to be distributionally equivalent to hidden Markov models yet without an underlying physical Markov process. Since the formal Markov processes are nonphysical, the theory of activated measurement allows merging energybased Eyring rate theories of ion channel behavior with the more common phenomenological Markov kinetic schemes to form energymodulated quantum channels. These unique biophysical concepts developed to understand the mechanisms of ion channel kinetics have the potential of revolutionizing our understanding of neurological computation.To apply this theory, the simplest quantum channel model consistent with neuronal membrane voltageclamp experiments is used to derive the activation eigenenergies for the HodgkinHuxley K+ and Na+ ion channels. It is shown that maximizing entropy under constrained activation energy yields noise spectral densities approximating S(f) = 1/f, thus offering a biophysical explanation for this ubiquitous noise component. These new channelbased noise processes are called generalized van der ZielMcWhorter (GVZM) power spectral densities (PSDs). This is the only known EEG noise model that has a small, fixed number of parameters, matches recorded EEG PSD's with high accuracy from 0 Hz to over 30 Hz without infinities, and has approximately 1/f behavior in the midfrequencies. In addition to the theoretical derivation of the noise statistics from ion channel stochastic processes, the GVZM model is validated in two ways. First, a class of mixed autoregressive models is presented which simulate brain background noise and whose periodograms are proven to be asymptotic to the GVZM PSD. Second, it is shown that pairwise comparisons of GVZMbased algorithms, using real EEG data from a publiclyavailable data set, exhibit statistically significant accuracy improvement over two wellknown and widelyused steadystate visual evoked potential (SSVEP) estimators.
Title:  Biophysical Sources of 1/f Noises in Neurological Systems. 
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Name(s): 
Paris, Alan, Author Vosoughi, Azadeh, Committee Chair Atia, George, Committee CoChair Wiegand, Rudolf, Committee Member Douglas, Pamela, Committee Member Berman, Steven, Committee Member University of Central Florida, Degree Grantor 

Type of Resource:  text  
Date Issued:  2016  
Publisher:  University of Central Florida  
Language(s):  English  
Abstract/Description:  High levels of random noise are a defining characteristic of neurological signals at all levels, from individual neurons up to electroencephalograms (EEG). These random signals degrade the performance of many methods of neuroengineering and medical neuroscience. Understanding this noise also is essential for applications such as realtime braincomputer interfaces (BCIs), which must make accurate control decisions from very short data epochs. The major type of neurological noise is of the socalled 1/ftype, whose origins and statistical nature has remained unexplained for decades. This research provides the first simple explanation of 1/ftype neurological noise based on biophysical fundamentals. In addition, noise models derived from this theory provide validated algorithm performance improvements over alternatives.Specifically, this research defines a new class of formal latentvariable stochastic processes called hidden quantum models (HQMs) which clarify the theoretical foundations of ion channel signal processing. HQMs are based on quantum state processes which formalize timedependent observation. They allow the quantumbased calculation of channel conductance autocovariance functions, essential for frequencydomain signal processing. HQMs based on a particular type of observation protocol called independent activated measurements are shown to be distributionally equivalent to hidden Markov models yet without an underlying physical Markov process. Since the formal Markov processes are nonphysical, the theory of activated measurement allows merging energybased Eyring rate theories of ion channel behavior with the more common phenomenological Markov kinetic schemes to form energymodulated quantum channels. These unique biophysical concepts developed to understand the mechanisms of ion channel kinetics have the potential of revolutionizing our understanding of neurological computation.To apply this theory, the simplest quantum channel model consistent with neuronal membrane voltageclamp experiments is used to derive the activation eigenenergies for the HodgkinHuxley K+ and Na+ ion channels. It is shown that maximizing entropy under constrained activation energy yields noise spectral densities approximating S(f) = 1/f, thus offering a biophysical explanation for this ubiquitous noise component. These new channelbased noise processes are called generalized van der ZielMcWhorter (GVZM) power spectral densities (PSDs). This is the only known EEG noise model that has a small, fixed number of parameters, matches recorded EEG PSD's with high accuracy from 0 Hz to over 30 Hz without infinities, and has approximately 1/f behavior in the midfrequencies. In addition to the theoretical derivation of the noise statistics from ion channel stochastic processes, the GVZM model is validated in two ways. First, a class of mixed autoregressive models is presented which simulate brain background noise and whose periodograms are proven to be asymptotic to the GVZM PSD. Second, it is shown that pairwise comparisons of GVZMbased algorithms, using real EEG data from a publiclyavailable data set, exhibit statistically significant accuracy improvement over two wellknown and widelyused steadystate visual evoked potential (SSVEP) estimators.  
Identifier:  CFE0006485 (IID), ucf:51418 (fedora)  
Note(s): 
20161201 Ph.D. Engineering and Computer Science, Dean's Office GRDST Doctoral This record was generated from author submitted information. 

Subject(s):  Ion channels  HodgkinHuxley  1/f  quantum stochastic processes  hidden Markov models  braincomputer interface  BCI  steadystate visual evoked potentials  SSVEP  
Persistent Link to This Record:  http://purl.flvc.org/ucf/fd/CFE0006485  
Restrictions on Access:  public 20161215  
Host Institution:  UCF 