1,773 research outputs found
Detection and Classification of EEG Epileptiform Transients with RBF Networks using Hilbert Huang Transform-derived Features
Diagnosis of epilepsy or epileptic transients AEP (Abnormal Epileptiform Paroxysmal) is tedious, but important, and an expensive process. The process involves trained neurologists going over the patient\u27s EEG records looking for epileptiform discharge like events and classifying it as AEP (Abnormal Epileptiform Paroxysmal) or non-AEP. The objective of this research is to automate the process of detecting such events and classifying them into AEP(definitely an Epileptiform Transient) and non-AEPs (unlikely an epileptiform transient). The problem is approached in two separate steps and cascaded to validate and analyze the performance of the overall system. The first step is a detection problem to find the Epileptiform like transients (ETs) from the Electroencephalograph (EEG) of a patient. A Radial basis function-based neural network has been trained using a training set consisting of examples from both classes (ETs and non-ETs). The ETs are the yellow boxes which are marked by expert neurologists. There are no particular examples of non-ETs and any data not annotated by experts can be considered to be examples of non-ETs. The second step is classification of the detected ETs also known as yellow boxes, into AEPs or non-AEPs. A similar Radial basis function-based neural network has been trained using the ETs marked and classified into AEPs and non-AEPs manually by seven expert neurologists. The annotations or yellow boxes along with the contextual signal was used to extract features using the Hilbert Huang Transform. The system is validated by considering an entire epoch of the patient EEG and potential ETs are identified using the detector. The potential ETs marked by the detector are classified into AEPs and non-AEPs and compared against the annotations marked by the experts
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
The primate visual system achieves remarkable visual object recognition
performance even in brief presentations and under changes to object exemplar,
geometric transformations, and background variation (a.k.a. core visual object
recognition). This remarkable performance is mediated by the representation
formed in inferior temporal (IT) cortex. In parallel, recent advances in
machine learning have led to ever higher performing models of object
recognition using artificial deep neural networks (DNNs). It remains unclear,
however, whether the representational performance of DNNs rivals that of the
brain. To accurately produce such a comparison, a major difficulty has been a
unifying metric that accounts for experimental limitations such as the amount
of noise, the number of neural recording sites, and the number trials, and
computational limitations such as the complexity of the decoding classifier and
the number of classifier training examples. In this work we perform a direct
comparison that corrects for these experimental limitations and computational
considerations. As part of our methodology, we propose an extension of "kernel
analysis" that measures the generalization accuracy as a function of
representational complexity. Our evaluations show that, unlike previous
bio-inspired models, the latest DNNs rival the representational performance of
IT cortex on this visual object recognition task. Furthermore, we show that
models that perform well on measures of representational performance also
perform well on measures of representational similarity to IT and on measures
of predicting individual IT multi-unit responses. Whether these DNNs rely on
computational mechanisms similar to the primate visual system is yet to be
determined, but, unlike all previous bio-inspired models, that possibility
cannot be ruled out merely on representational performance grounds.Comment: 35 pages, 12 figures, extends and expands upon arXiv:1301.353
Representation of Object-Centered Space by Neurons of the Supplementary Eye Field
The supplementary eye field (SEF) is a region of cortex located on the dorsomedial shoulder of the frontal lobe, considered to be involved in the control of eye movements. SEF neurons show spatially selective activity during visually- and memory-guided saccades. The selectivity exhibited by SEF neurons has been described as being related to an eye- or head-centered reference frame. We have previously shown that SEF neurons exhibit selectivity in an object-centered reference frame: neurons will fire selectively when saccades are directed to one end of a bar or another, irrespective of the absolute location of the bar in space.It is not well known how SEF neurons display selectivity for object-centered locations. In order to better understand the mechanism of this phenomenon, we performed three studies. In the first study, we asked how SEF neurons encode locations in both egocentric and object-centered reference frames. We recorded from single SEF neurons while monkeys performed tasks requiring spatial representation in either eye-centered or object-centered reference frames. Different SEF neurons encoded locations in eye-centered coordinates only, object-centered coordinates only, or in complex combinations of the two.In the second study, we tested whether object-centered selectivity is an innate property of SEF neurons or whether it is acquired through learning. We recorded the activity of SEF neurons before and after training monkeys to perform an object-centered task. Some SEF neurons exhibited object-centered selectivity before training. Following training, this number was increased, as was the intensity of object-centered spatial selectivity.In the third study, we investigated whether the object-centered selectivity seen in SEF neurons during performance of an object-centered task is reduced during performance of a non-object-centered task. We recorded from SEF neurons while monkeys performed either an object-centered task or a color matching task with an object as a target. An equivalent number of neurons showed object-centered selectivity in both tasks, but the strength of selectivity was slightly higher during performance of the object-centered task. We conclude from the results of these studies that neurons in the SEF are critically involved in the dynamic representation of locations using multiple spatial reference frames
Investigating neural correlates of stimulus repetition using fMRI
Examining the effect of repeating stimuli on brain activity is important for theories of perception, learning and memory. Functional magnetic resonance imaging (fMRI) is a non-invasive way to examine repetition-related effects in the human brain. However the Blood-Oxygenation Level-Dependent (BOLD) signal measured by fMRI is far removed from the electrical activity recorded from single cells in animal studies of repetition effects. Despite that, there have been many claims about the neural mechanisms associated with fMRI repetition effects. However, none of these claims has adequately considered the temporal and spatial resolution limitations of fMRI. In this thesis, I tackle these limitations by combining simulations and modelling in order to infer repetition-related changes at the neural level. I start by considering temporal limitations in terms of the various types of general linear model (GLM) that have used to deconvolve single-trial BOLD estimates. Through simulations, I demonstrate that different GLMs are best depending on the relative size of trial-variance versus scan-variance, and the coherence of those variabilities across voxels. To address the spatial limitations, I identify six univariate and multivariate properties of repetition effects measured by event-related fMRI in regions of interest (ROI), including how repetition affects the ability to classify two classes of stimuli. To link these properties to underlying neural mechanisms, I create twelve models, inspired by single-cell studies. Using a grid search across model parameters, I find that only one model (“local scaling”) can account for all six fMRI properties simultaneously. I then validate this result on an independent dataset that involves a different stimulus set, protocol and ROI. Finally, I investigate classification of initial versus repeated presentations, regardless of the stimulus class. This work provides a better understanding of the neural correlates of stimulus repetition effects, as well as illustrating the importance of formal modelling
Virtual metrology for plasma etch processes.
Plasma processes can present dicult control challenges due to time-varying dynamics
and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the
use of mathematical models with accessible measurements from an operating process to
estimate variables of interest. This thesis addresses the challenge of virtual metrology
for plasma processes, with a particular focus on semiconductor plasma etch.
Introductory material covering the essentials of plasma physics, plasma etching, plasma
measurement techniques, and black-box modelling techniques is rst presented for readers
not familiar with these subjects. A comprehensive literature review is then completed
to detail the state of the art in modelling and VM research for plasma etch processes.
To demonstrate the versatility of VM, a temperature monitoring system utilising a
state-space model and Luenberger observer is designed for the variable specic impulse
magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The
temperature monitoring system uses optical emission spectroscopy (OES) measurements
from the VASIMR engine plasma to correct temperature estimates in the presence of
modelling error and inaccurate initial conditions. Temperature estimates within 2% of
the real values are achieved using this scheme.
An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate
plasma etch rate for an industrial plasma etch process is presented. The VM
models estimate etch rate using measurements from the processing tool and a plasma
impedance monitor (PIM). A selection of modelling techniques are considered for VM
modelling, and Gaussian process regression (GPR) is applied for the rst time for VM
of plasma etch rate. Models with global and local scope are compared, and modelling
schemes that attempt to cater for the etch process dynamics are proposed. GPR-based
windowed models produce the most accurate estimates, achieving mean absolute percentage
errors (MAPEs) of approximately 1:15%. The consistency of the results presented
suggests that this level of accuracy represents the best accuracy achievable for
the plasma etch system at the current frequency of metrology.
Finally, a real-time VM and model predictive control (MPC) scheme for control of
plasma electron density in an industrial etch chamber is designed and tested. The VM
scheme uses PIM measurements to estimate electron density in real time. A predictive
functional control (PFC) scheme is implemented to cater for a time delay in the VM
system. The controller achieves time constants of less than one second, no overshoot,
and excellent disturbance rejection properties. The PFC scheme is further expanded by
adapting the internal model in the controller in real time in response to changes in the
process operating point
The neurophysiology of stereoscopic vision
PhD ThesisMany animals are able to perceive stereoscopic depth owing to the disparity information that
arises from the left and right eyes' horizontal displacement on the head. The initial computation of
disparity happens in primary visual cortex (V1) and is largely considered to be a correlation-based
computation. In other words, the computational role of V1 as it pertains to stereoscopic vision can
be seen to roughly perform a binocular cross-correlation between the images of the left and right
eyes. This view is based on the unique success of a correlation-based model of disparity-selective
cells { the binocular energy model (BEM). This thesis addresses two unresolved challenges to this
narrative. First, recent evidence suggests that a correlation-based view of primary visual cortex
is unable to account for human perception of depth in a stimulus where the binocular correlation
is on average zero. Chapters 1 and 2 show how a simple extension of the BEM which better
captures key properties of V1 neurons allows model cells to signal depth in such stimuli. We
also build a psychophysical model which captures human performance closely, and recording from
V1 in the macaque, we then show that these predicted properties are indeed observed in real
V1 neurons. The second challenge relates to the long-standing inability of the BEM to capture
responses to anticorrelated stimuli: stimuli where the contrast is reversed in the two eyes (e.g.
black features in the left eye are matched with identical white features in the right eye). Real
neurons respond less strongly to these stimuli than model cells. In Chapter 3 and 4, we make
use of recent advances in optimisation routines and exhaustively test the ability of a generalised
BEM to capture this property. We show that even the best- tting generalised BEM units only go
some way towards describing neuronal responses. This is the rst exhaustive empirical test of this
in
uential modelling framework, and we speculate on what is needed to develop a more complete
computational account of visual processing in primary visual cortex
Virtual metrology for plasma etch processes.
Plasma processes can present dicult control challenges due to time-varying dynamics
and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the
use of mathematical models with accessible measurements from an operating process to
estimate variables of interest. This thesis addresses the challenge of virtual metrology
for plasma processes, with a particular focus on semiconductor plasma etch.
Introductory material covering the essentials of plasma physics, plasma etching, plasma
measurement techniques, and black-box modelling techniques is rst presented for readers
not familiar with these subjects. A comprehensive literature review is then completed
to detail the state of the art in modelling and VM research for plasma etch processes.
To demonstrate the versatility of VM, a temperature monitoring system utilising a
state-space model and Luenberger observer is designed for the variable specic impulse
magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The
temperature monitoring system uses optical emission spectroscopy (OES) measurements
from the VASIMR engine plasma to correct temperature estimates in the presence of
modelling error and inaccurate initial conditions. Temperature estimates within 2% of
the real values are achieved using this scheme.
An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate
plasma etch rate for an industrial plasma etch process is presented. The VM
models estimate etch rate using measurements from the processing tool and a plasma
impedance monitor (PIM). A selection of modelling techniques are considered for VM
modelling, and Gaussian process regression (GPR) is applied for the rst time for VM
of plasma etch rate. Models with global and local scope are compared, and modelling
schemes that attempt to cater for the etch process dynamics are proposed. GPR-based
windowed models produce the most accurate estimates, achieving mean absolute percentage
errors (MAPEs) of approximately 1:15%. The consistency of the results presented
suggests that this level of accuracy represents the best accuracy achievable for
the plasma etch system at the current frequency of metrology.
Finally, a real-time VM and model predictive control (MPC) scheme for control of
plasma electron density in an industrial etch chamber is designed and tested. The VM
scheme uses PIM measurements to estimate electron density in real time. A predictive
functional control (PFC) scheme is implemented to cater for a time delay in the VM
system. The controller achieves time constants of less than one second, no overshoot,
and excellent disturbance rejection properties. The PFC scheme is further expanded by
adapting the internal model in the controller in real time in response to changes in the
process operating point
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