An Energy Efficient non-volatile FPGA Digital Processor for Brain Neuromodulation

PhD ThesisBrain stimulation technologies have the potential to provide considerable clinical benefits for people with a range of neurological disorders. Recent neuroscience studies have shown that considerable information of brain states is contained in the low frequency local field potential (If-LFP; below 5Hz) recordings with application in real-time closed-loop neurostimulation for treating neurological disorders. Given these signals can be sampled at low sampling rate and hence provide sparse data streams, there is an opportunity to design implantable neuroprosthesis with long battery lifecycles which enables enough processing power to implement long-term, real-time closed loop control algorithms. In this thesis, a closed-loop embedded digital processor has been created for use in rodent neuroscience experiments. The first contribution of this work is to develop a mathematical analytical design approach of feedback controller for suppressing high-amplitude epileptic activity in the neuron mass model to form a better understanding of how to perform a better closed-loop stimulation to control seizures. The second contribution and the third contribution are combined to present an exploratory energy-efficient digital processor architecture built with commercial off-the-shelf non-volatile FPGAs and microcontroller for sparse data processing of brain neuromodulation. A digital hardware design of an exemplar PID control algorithm has been implemented on this proposed digital architecture. A new power computing diagram of this time-driven approach significantly reduced the power consumption which suggests that a digital combined control system of non-volatile FPGAs and microcontroller outweighs a digital control system of microcontroller with microcontroller regarding computing time cost and energy consumption supposing one microcontroller is always required. Taken together, this digital energy-efficient processor architecture gives important insights and viewpoints for the further advancements of neuroprosthesis for brain neurostimulation to achieve lower power consumption for sparse sampling data rate

Identification of the parameters of an electroencephalographic recording system

Elektroencefalografický záznamový systém slouží k vyšetření mozkové aktivity. Na základě tohoto vyšetření lze stanovit diagnózu některých nemocí, například epilepsie. Účelem této práce bylo zpracování signálu z toho systému a vytvoření modelového signálu, který bude s reálným signálem porovnán. Uměle vytvořený signál vychází z Jansenova matematického modelu, který byl dále implementován v prostředí MATLAB a rozšířen ze základního modelu na komplexnější zahrnující nelinearity a model rozhraní elektroda – elektrolyt. Dále bylo provedeno měření signálů na EEG fantomu a následná identifikace parametrů naměřených signálu. V první fázi byly testovány jednoduché signály. Identifikace parametrů těchto signálů sloužila k validaci daného EEG fantomu. V druhé fázi bylo přistoupeno k testování EEG signálů navržených podle matematického Jansenova modelu. Analýza veškerých signálů zahrnuje mimo jiné časově frekvenční analýzu či ověření platnosti principu superpozice.Electroencephalographic recording system is used to examine a brain activity. Based on this examination we can establish the diagnosis of certain diseases, such as epilepsy. The purpose of this study has been the signal processing and a signal generation which were compared with the real signal. Artificially generated signal is based on Jansen‘s mathematical model which has been also implemented in MATLAB and then that model has been extended to more complex model including nonlinearities and model electrode – electrolyte. Also a signal measurements on EEG phantom and identification of the parameters of these signals were performed. Firstly a simply signals were tested and the identification of their parameters has served to validate the EEG phantom. Secondly the created signal designed by Jansen model were tested also. Besides an analysis of all signals includes the time frequency analysis or a superposition principle testing.

Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling

We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory-inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis

Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating

AbstractSeizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5–10min compared to approximately 1–2h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated

Brain network, modelling and corresponding EEG patterns for health and disease states

EEG is a significant tool used to capture normal and abnormal cerebral electrical activities in human brain. To understand and test complex hypotheses about the mechanisms of their generation, various model and modelling approaches have been proposed and developed. Among these models and approaches, a new type of network model has emerged known as large-scale brain network model (LSBNM). LSBNM is becoming increasingly important in understanding, studying and testing the mechanisms of the generation of normal and abnormal oscillatory activities of the human brain. It also offers unique predictive tools for studying disease states and brain abnormalities. However, there are still many limitations in the existing LSBNM approaches. Hence, developing novel methods for LSBNM leads to the exploration, generation and prediction of a new and rich repertoire of healthy and disease rhythmic activities in the human brain. The aim of this project is to develop LSBNM to include new versions of network models comprising various human cerebral areas in the left and right hemispheres. First, two network models at multi scale are developed to generate EEG patterns for health states: alpha rhythms with a low frequency at 7Hz and, and the alpha band of EEG rhythms at different ranges of frequencies 7–8 Hz, 8 9 Hz and 10–11 Hz. Second, a new network model for simulating multi-bands of EEG patterns: delta–range frequency of (1-4 Hz), theta at a frequency of (4-7Hz) and diverse narrowband oscillations ranging from delta to theta (0-5Hz) is introduced. Third, novel brain network models are simulated and used to predict the abnormal electrical activity such as oscillations observed in the epileptic brain. The design and simulation of each of the network models are implemented using the unique neuro informatics platform: The Virtual Brain (TVB). This project made significant contributions to brain modelling, in particularly to the understanding of neural activity in the human brain at multi levels of scale. Further, it emphasises the role of structural connectivity of the connectome on emerging normal and abnormal dynamics of brain oscillations, as well as affirming that modelling with TVB can provide reliable neuroimaging data such as EEGS for the healthy and diseased brain. In particular, the results of this study help researchers and physicians studying large-scale brain activity associated with lower and higher alpha oscillations and the delta waves of Stages 3 and 4 of the sleep and theta waves of Stages 1 and 2 of sleep. Moreover, they will be able to assist researchers and clinical doctors in the field of epilepsy to understand the complex neural mechanisms generating abnormal oscillatory activities and, thus, may open up new avenues towards the discovery of new clinical interventions related to these types of activities

Dynamical Systems; Proceedings of an IIASA Workshop, Sopron, Hungary, September 9-13, 1985

The investigation of special topics in systems dynamics -- uncertain dynamic processes, viability theory, nonlinear dynamics in models for biomathematics, inverse problems in control systems theory -- has become a major issue at the System and Decision Sciences Research Program of IIASA. The above topics actually reflect two different perspectives in the investigation of dynamic processes. The first, motivated by control theory, is concerned with the properties of dynamic systems that are stable under variations in the systems' parameters. This allows us to specify classes of dynamic systems for which it is possible to construct and control a whole "tube" of trajectories assigned to a system with uncertain parameters and to resolve some inverse problems of control theory within numerically stable solution schemes. The second perspective is to investigate generic properties of dynamic systems that are due to nonlinearity (as bifurcations theory, chaotic behavior, stability properties, and related problems in the qualitative theory of differential systems). Special stress is given to the applications of nonlinear dynamic systems theory to biomathematics and ecology. The proceedings of a workshop on the "Mathematics of Dynamic Processes", dealing with these topics is presented in this volume

Severe slugging with multiphase fluid transport and phase separation in subsea pipelines

Flow assurance and separation of multiphase oil and gas flows are becoming more challenging as offshore development advances into more difficult environments. Petroleum fluids are required to be transported to processing facilities over long distances and through difficult terrain, as well as under varying temperatures and pressures. Severe slugging is a well-known flow assurance problem that limits effective offshore petroleum production. Gas-lifting and topside choking have been used for decades to mitigate this flow problem in fluid transport and processing. However, the application of these techniques has been challenged by design, cost, and footprint constraints. The introduced complexities require detailed understanding of the detailed dynamics of fluid flow in highly compact designs. Systematic experimental and modeling investigations of severe slugging in pipeline-riser systems can provide useful information on these slugging mechanisms and characteristics. Experimental studies are conducted in this thesis to determine the mechanisms and key parameters involved in the slugging performance in fluid processing installations. Through well designed experimental tests, the impacts of actuators on slugging are examined. This thesis also focuses on control systems to suppress slugging in the pipeline-riser system using experimental analysis and control models. The research also presents new models that predict phenomena of slugging behaviour, and more importantly, fitting for control designs in offshore flow separation. Furthermore, this thesis identifies and quantifies the process variables and conditions associated with both slugging and non-slugging regions, leading to the development of more effective solution methods and correlations. Relevant correlations (including slugging frequency, production rates, gas injection rate, compression requirements, and operating pressures) are developed by dimensional analysis techniques. The study develops new models, data and useful information for better understanding of slugging in pipeline systems. Sensitivity analyses to assist in the selection and design of more accurate control methods are also presented. The research outcomes provide improved and more robust models and guidelines for implementing slugging mitigation measures

Where is cognition? Towards an embodied, situated, and distributed interactionist theory of cognitive activity

In recent years researchers from a variety of cognitive science disciplines have begun to challenge some of the core assumptions of the dominant theoretical framework of cognitivism including the representation-computational view of cognition, the sense-model-plan-act understanding of cognitive architecture, and the use of a formal task description strategy for investigating the organisation of internal mental processes. Challenges to these assumptions are illustrated using empirical findings and theoretical arguments from the fields such as situated robotics, dynamical systems approaches to cognition, situated action and distributed cognition research, and sociohistorical studies of cognitive development. Several shared themes are extracted from the findings in these research programmes including: a focus on agent-environment systems as the primary unit of analysis; an attention to agent-environment interaction dynamics; a vision of the cognizer's internal mechanisms as essentially reactive and decentralised in nature; and a tendency for mutual definitions of agent, environment, and activity. It is argued that, taken together, these themes signal the emergence of a new approach to cognition called embodied, situated, and distributed interactionism. This interactionist alternative has many resonances with the dynamical systems approach to cognition. However, this approach does not provide a theory of the implementing substrate sufficient for an interactionist theoretical framework. It is suggested that such a theory can be found in a view of animals as autonomous systems coupled with a portrayal of the nervous system as a regulatory, coordinative, and integrative bodily subsystem. Although a number of recent simulations show connectionism's promise as a computational technique in simulating the role of the nervous system from an interactionist perspective, this embodied connectionist framework does not lend itself to understanding the advanced 'representation hungry' cognition we witness in much human behaviour. It is argued that this problem can be solved by understanding advanced cognition as the re-use of basic perception-action skills and structures that this feat is enabled by a general education within a social symbol-using environment