117 research outputs found
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
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Study of the Term Neonatal Brain Injury with combined Diffuse Optical Tomography and Electroencephalography
This thesis describes the application of combined diffuse optical tomography (DOT) and electroencephalography (EEG) in the investigation of neonatal term brain injury. With hypoxic ischaemic encephalopathy (HIE) and perinatal stroke being the most frequent contributors to brain injury in the term neonatal population, the first part of the thesis focuses on the description and ongoing requirement for their further investigation. In continuation to that, the characteristics and unique properties of both DOT and EEG are described and explored.
The combination of these two modalities was utilised in elucidating the relationship between neuronal activity and cerebral haemodynamics both in physiological processes as well as in disease, by the infant’s cot side. This work differs to previous studies using near-infrared technologies and EEG, as a denser whole head array was used, offering the potential of 3-dimensional image reconstruction of the cortical haemodynamic events in relation to electro-cortical activity. These methods were applied in the study of critically ill infants presenting with seizures in the first few days of life.
The relevant results are presented in three separate chapters of the thesis. Distinct neurophysiological phenomena such as seizures and burst suppression were detected and studied in association to underlying HIE. On the grounds of a pre-existing pilot study of our research group, distinct prolonged de-oxygenated cortical areas were identified following electrical seizure activity. Further exploration of infants with seizures provided limited supporting evidence. The investigation of burst suppression in HIE led to the first ever identification of repeated, waveform, cortical haemodynamic events in response to bursts of electrical activity with some spatial correlation to regions of brain injury. Further analysis of the low frequencies within the diffuse optical signal in cases of perinatal stroke, showed a consistent interhemispheric difference between the healthy and stroke-affected brain regions.
The limitations, prospects and conclusions are presented in the final chapter. The use of simultaneous DOT and EEG offers a unique neuro-monitoring and neuro-investigating tool in the neonatal intensive care environment, which is safe, portable, and cost-effective, Ongoing research is required for the exploration and development of the methodology and its potential diagnostic properties
Mean field modelling of human EEG: application to epilepsy
Aggregated electrical activity from brain regions recorded via an electroencephalogram (EEG),
reveal that the brain is never at rest, producing a spectrum of ongoing oscillations that
change as a result of different behavioural states and neurological conditions. In particular,
this thesis focusses on pathological oscillations associated with absence seizures that typically
affect 2–16 year old children. Investigation of the cellular and network mechanisms for absence
seizures studies have implicated an abnormality in the cortical and thalamic activity in the
generation of absence seizures, which have provided much insight to the potential cause of this
disease. A number of competing hypotheses have been suggested, however the precise cause
has yet to be determined. This work attempts to provide an explanation of these abnormal
rhythms by considering a physiologically based, macroscopic continuum mean-field model of
the brain's electrical activity. The methodology taken in this thesis is to assume that many
of the physiological details of the involved brain structures can be aggregated into continuum
state variables and parameters. The methodology has the advantage to indirectly encapsulate
into state variables and parameters, many known physiological mechanisms underlying the
genesis of epilepsy, which permits a reduction of the complexity of the problem. That is, a
macroscopic description of the involved brain structures involved in epilepsy is taken and then
by scanning the parameters of the model, identification of state changes in the system are
made possible. Thus, this work demonstrates how changes in brain state as determined in
EEG can be understood via dynamical state changes in the model providing an explanation
of absence seizures. Furthermore, key observations from both the model and EEG data
motivates a number of model reductions. These reductions provide approximate solutions of
seizure oscillations and a better understanding of periodic oscillations arising from the involved
brain regions. Local analysis of oscillations are performed by employing dynamical systems
theory which provide necessary and sufficient conditions for their appearance. Finally local
and global stability is then proved for the reduced model, for a reduced region in the parameter
space. The results obtained in this thesis can be extended and suggestions are provided for
future progress in this area
Magnetoencephalography
This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician
NOVEL GRAPHICAL MODEL AND NEURAL NETWORK FRAMEWORKS FOR AUTOMATED SEIZURE DETECTION, TRACKING, AND LOCALIZATION IN FOCAL EPILEPSY
Epilepsy is a heterogenous neurological disorder characterized by recurring and unprovoked seizures. It is estimated that 60% of epilepsy patients suffer from focal epilepsy, where seizures originate from one or more discrete locations within the brain. After onset, focal seizure activity spreads, involving more regions in the cortex. Diagnosis and therapeutic planning for patients with focal epilepsy crucially depends on being able to detect epileptic activity as it starts and localize its origin. Due to the subtlety of seizure activity and the complex spatio-temporal propagation patterns of seizure activity, detection and localization of seizure by visual inspection is time-consuming and must be done by highly trained neurologists.
In this thesis, we detail modeling approaches to identify and capture the spatio-temporal ictal propagation of focal epileptic seizures. Through novel multi-scale frameworks, information fusion between signal paths, and hybrid architectures, models that capture the underlying seizure propagation phenomena are developed. The first half relies on graphical modeling approaches to detect seizures and track their activity through the space of EEG electrodes. A coupled hidden Markov model approach to seizure propagation is described. This model is subsequently improved through the addition of convolutional neural network based likelihood functions, removing the reliance on hand designed feature extraction. Through the inclusion of a hierarchical switching chain and localization variables, the model is revised to capture multi-scale seizure onset and spreading information.
In the second half of this thesis, end-to-end neural network architectures for seizure detection and localization are developed. First, combination convolutional and recurrent neural networks are used to identify seizure activity at the level of individual EEG channels. Through novel aggregation, the network is trained to recognize seizure activity, track its evolution, and coarsely localize seizure onset from lower resolution labels. Next, a multi-scale network capable of analyzing the global and electrode level signals is developed for challenging task of end-to-end seizure localization. Onset location maps are defined for each patient and an ensemble of weakly supervised loss functions are used in a multi-task learning framework to train the architecture
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