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Analysis of Local Field Potential and Gamma Rhythm Using Matching Pursuit Algorithm

Electronic Theses of Indian Institute of Science

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Title Analysis of Local Field Potential and Gamma Rhythm Using Matching Pursuit Algorithm
 
Creator Chandran, Subash K S
 
Subject Brain Signals
Brain Rhythms
Matching Pursuit Algorithm
Signal Processing
Cortical Computation
Brain Signal Time Frequency Spectrum
Neural Signals
Local Field Potential
Gamma Rhythm(Brain)
Brain Signal Processing
Electroencephalography
Neuroscience
Wavelet Transform (WT)
Multitaper Method (MTM)
Local Field potential (LFP)
Hilbert-Huang Transform (HHT)
Matching Pursuit (MP)
Electrical Engineering
 
Description Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. These signals also have transient structures related to spiking or sudden onset of a stimulus, which have a duration not exceeding tens of milliseconds. Further, brain signals are highly non-stationary because both behavioral state and external stimuli can change over a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal. In Chapter 2, we describe a multi-scale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both sharp stimulus-onset transient and sustained gamma rhythm in local field potential recorded from the primary visual cortex.

Gamma rhythm (30 to 80 Hz), often associated with high-level cortical functions, has been proposed to provide a temporal reference frame (“clock”) for spiking activity, for which it should have least center frequency variation and consistent phase for extended durations. However, recent studies have proposed that gamma occurs in short bursts and it cannot act as a reference. In Chapter 3, we propose another gamma duration estimator based on matching pursuit (MP) algorithm, which is tested with synthetic brain signals and found to be estimating the gamma duration efficiently. Applying this algorithm to real data from awake monkeys, we show that the median gamma duration is more than 330 ms, which could be long enough to support some cortical computations.
 
Contributor Ray, Supratim
Seelamantula, Chandra Sekhar
 
Date 2017-11-17T09:48:28Z
2017-11-17T09:48:28Z
2017-11-17
2016
 
Type Thesis
 
Identifier http://etd.iisc.ernet.in/handle/2005/2771
http://etd.ncsi.iisc.ernet.in/abstracts/3607/G28211(Abs).pdf
 
Language en_US
 
Relation G28211