Iatrogenic hypoglycemia is a limiting factor for managing diabetes mellitus and can have severe outcomes such as seizures and coma. Although several studies have investigated the central nervous system consequences of hypoglycemia, the effects of seizures, as well as possible treatment strategies, have yet to be elucidated in juvenile animals. The objective of this thesis was to establish an in vivo model of severe hypoglycemia and seizures in juvenile diabetic and non-diabetic rats. In both groups there existed a similar blood glucose threshold for seizures, and mortality only occurred following severe seizures, particularly with repeated seizures that were unresponsive to treatment. While the administration of anticonvulsants temporarily mitigated seizures, glucose administration was required to prevent mortality. Abnormalities in the hippocampal and brainstem electroencephalograms (EEG) were observed in hypoglycemic animals without a clear correlate to convulsive activity.

Two event-related potential (ERP) experiments were conducted to investigate spatial-numeric associations of response codes (SNARC) and attentional cuing (SNAAC). In the SNARC effect, people respond faster when making a left-hand response to report that a number is small, and when making a right-hand response to report that a number is large. Experiment 1 examined effects of SNARC-compatibility and prior response-probability in a number comparison task. Lateralized readiness potentials (LRPs) showed that SNARC-compatibility influenced an intermediate stage of response-selection, and prior response-probability influenced both earlier and later stages. The P300 ERP component was also modulated by SNARC-compatibility and prior response-probability, suggesting parietal involvement in the SNARC effect. In the SNAAC effect, attention is directed to left-side regions of space upon viewing small-magnitude numbers, and to right-side regions of space upon viewing large-magnitude numbers. Experiment 2 investigated whether ERPs evoked by peripheral visual probes would be enhanced when probes appeared in the left hemifield after small-magnitude digits and when they appeared in the right hemifield after large-magnitude digits. ERPs to peripheral probes were not modulated by numerical magnitude of digit pre-cues.

Cognition is dynamic and complex, requiring specific sets of brain areas to cooperate for specific tasks. Neural synchronization is a proposed mechanism for transient functional integration of specific neural populations, enabling feature flexible binding and dynamic assignment of functional connectivity in the brain according to task demands. This thesis addresses the role of neural synchronization in selective attention and perceptual consciousness. The goals of this thesis are to test the hypothesis that synchronization between brain regions is relevant to network dynamics in selective attention and for perceptual organization, and to elucidate the function of synchronization in different frequency ranges. Using a selective visuospatial cuing paradigm it is shown that deploying attention to one visual hemifield yields transient long-distance gamma-band synchronization between contralateral visual cortex and other, widespread, brain regions. This is interpreted as a mechanism for establishing anticipatory biasing of communication in the cortex. Long-distance gamma synchrony, moreover, is periodically 'refreshed' at a theta rate, possibly serving to maintain this gamma network. While local alpha-band activity was found to be greater ipsilateral to the attended visual hemifield, alpha-band synchronization between primary visual cortex and higher visual areas was greater contralateral to attended locations. This suggests that local alpha synchrony is relevant for inhibition, while long-range alpha synchronization enacts functional coupling. The onset of a new conscious percept during binocular rivalry coincides with large-scale gamma-band synchronization which recurs at a theta rate. This suggests that gamma synchronization integrates features into a unified conscious percept while the theta cycle maintains that network. Using an audiovisual speech integration paradigm it is shown that large-scale gamma synchronization is greater when incongruence is detected between auditory and visual streams. This highlights an important distinction: neural synchronization reflects neural integration, not perceptual integration. Perceptual integration typically requires neural integration (feature binding), however, in this case detection of audiovisual mismatches requires cooperation within a distributed network, whereas audiovisual speech integration is largely accomplished in superior temporal cortex. These studies indicate that long-distance gamma synchronization establishes neural integration, the theta cycle maintains gamma synchronous networks, and local and long-range alpha synchrony reflect sustained inhibition and functional coupling mechanisms, respectively.

Brain computer interface (BCI) systems measure brain signal and translate it into control commands in an attempt to mimic specific human thinking activities. In recent years, many researchers have shown their interests in BCI systems, which has resulted in many experiments and applications. However, most methods are just based on a specific selected dataset or a typical feature. As a result, there are questions about whether some methods generalise well on other datasets. Therefore, the major motivation of this thesis is to compare various features and classifiers described in the literature. Pattern recognition is considered as the core part of a BCI system in our research. In this thesis, a number of different features and classifiers are compared in terms of classification accuracy and computation time. The studied features are: time series waveform, autoregressive (AR) components, spectral components; these are used with different classifiers: such as template matching, nearest neighbour, linear discriminant analysis (LDA), Bayesian statistical and fuzzy logic decision classifiers. In order to assess and compare these different features and classifiers, an extensive investigation was carried out on a public dataset (imagined left or right hand movement) from an international BCI competition and the results are reported in this thesis. The classification was done in a continuous fashion, to match a real time application. In this process, the average and best accuracy, as well as the computation time, were analysed and compared. The results showed that most classifiers achieved very high accuracies and short computation times for most features. A BCI experiment based on imagined left or right hand movement was carried out at the University of Adelaide and some investigations on the data from this experiment are discussed. The result shows that the selected classifiers can work well with this new dataset without much additional preprocessing or modifications. Finally, this thesis culminates with some conclusions based on our research, and discusses some further potential work. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1415396 / Thesis (M.Eng.Sc.) - University of Adelaide, School of Electrical and Electronic Engineering, 2010

This work began with a review of visual spatial selective attention, from a behavioural perspective with particular emphasis placed upon the spotlight model. To complement the behavioural review, the physiological aspects of the visual system were studied to find possible loci of the spotlight. The literature pointed to the pulvinar nucleus of the thalamus, interacting with the parietal and frontal cortices. Some experimental work examined relationships between visual spatial selective attention and event-related potentials (ERPs) recorded from the scalp. The second section of this thesis reviewed the ERP measures relating specifically to the visual modality for their possible application in a visual attentional task. This yielded two independent findings. First, the Probe-ERP paradigm comprising an attentional task being performed by the subject, with a separate stimulus to probe the unused resources within the system. Second, the steady-state evoked response, with the stimulus presented as a small sinusoidal variation around a mean level of contrast. The combination of the Probe-ERP paradigm and the steady-state visually evoked potential (SSVEP) warranted experimental evaluation.

Modeling of brain activity is often seen as requiring great computing power. However in the special case of modeling scalp EEG it is possible to adopt a continuum approximation for the cortex, and then to use the techniques of wave physics to describe its consequent large-scale dynamics. The model incorporates the following critical components: two classes of neurons (excitatory and inhibitory), the typical number and strength of connections between these two classes, the corresponding connections within the thalamus and between the thalamus and cortex, the time constants and basic physiology of neurons, and the propagation of activity between neurons. Representing the immense intricacy of brain anatomy and physiology with suitable summary equations and average parameter values has meant that the model is able to capture the essential characteristics of EEG and ERPs, and to do so in a computationally manageable way.

Είναι γεγονός ότι τόσο το ευθύ όσο & το αντίστροφο EEG & MEG πρόβλημα έχει αποτελέσει αντικείμενο μελέτης ερευνητών διαφόρων ειδικοτήτων (των Μαθηματικών, της Πληροφορικής, των Φυσικών, των Ηλεκτρολόγων Μηχανικών & φυσικά της Ιατρικής), ήδη από τις δεκαετίες του 1950 & 1960, που αναζητούσαν τρόπους υπολογισμού του ηλεκτρικού & του μαγνητικού πεδίου που παράγουν στο εξωτερικό του εγκεφάλου δεδομένες πηγές που βρίσκονται στο εσωτερικό του (ευθέα προβλήματα EEG - MEG), είτε τρόπους προσδιορισμού των πηγών από μετρήσεις των πεδίων αυτών εξωτερικά του εγκεφάλου (αντίστροφα προβλήματα EEG - MEG). Όλα τα νευρικά σήματα του εγκεφάλου διαδίδονται μέσω μικρών ηλεκτρικών ρευμάτων, τα οποία παράγουν ηλεκτρικό & μαγνητικό πεδίο εντός & εκτός του εγκεφάλου λόγω του συζευγμένου χαρακτήρα του ηλεκτρομαγνητισμού για τα χρονικώς μεταβαλλόμενα φαινόμενα. Τα ηλεκτρικά πεδία & τα μαγνητικά πεδία που παράγονται καταγράφονται από το Ηλεκτροεγκεφαλογράφημα (EEG) & το Μαγνητοεγκεφαλογράφημα (MEG) αντίστοιχα. Στην παρούσα εργασία παρουσιάζουμε τα αποτελέσματα των ερευνών που έχουν καταγραφεί τα τελευταία χρόνια & έχουν δώσει χρήσιμες σχέσεις για το ηλεκτρικό & μαγνητικό πεδίο για τα διάφορα πρότυπα του εγκεφάλου & συγκεκριμένα του σφαιρικού, του σφαιροειδούς & του ελλειψοειδούς προτύπου. Επίσης αναλύεται η αναγωγή προς το σφαιρικό πρότυπο από τα άλλα δύο πρότυπα για να αποδειχθεί ότι τελικά η σφαιρική συμπεριφορά αποκαθίσταται & αναφέρονται ποιες είναι οι σιωπηλές πηγές που δεν συνεισφέρουν στην δημιουργία του μαγνητικού πεδίου στα διάφορα πρότυπα. Τέλος γίνεται αναφορά στο αντίστροφο πρόβλημα του ΗΕΓ που δεν έχει μοναδική λύση & αναφέρουμε πως μπορεί να επιλυθεί αρκεί από τα δεδομένα που έχουμε για το ηλεκτρικό πεδίο να έχει εξαλειφθεί ο θόρυβος με κάποιες μεθόδους που εξηγούνται. Αντικείμενο της εργασίας είναι αρχικά να περιγράψει τη μεθοδολογία για την ανάλυση του ηλεκτρικού πεδίου σε καρτεσιανές συντεταγμένες όσον αφορά το ελλειψοειδές πρότυπο του εγκεφάλου, που είναι & το βέλτιστο δεδομένου ότι ο μέσος εγκέφαλος είναι ελλειψοειδής με άξονες 9, 6.5, 6 cm. Στη συνέχεια δημιουργήσαμε ένα πρόγραμμα σε matlab με σκοπό την καταγραφή των πειραματικών αποτελεσμάτων (που είναι ο προσδιορισμός της πηγής & της θέσης της) & την τρισδιάστατη γραφική παράστασή τους από την παραμετρική ανάλυσή του ηλεκτρικού πεδίου. Στόχος μας ήταν η εύρεση & η σύγκριση των πηγών & των θέσεων που αυτές εντοπίζονται στο εσωτερικό του εγκεφάλου υπό διάφορες συνθήκες που καθορίζονται από την μεταβολή διαφόρων παραμέτρων όπως το πεδίο τιμών των μετρήσεων & των σφαλμάτων τους. / The fact is that both the EEG & MEG problems and the EEG & MEG inverse problems have been studied by researchers of various disciplines (Mathematics, Informatics, Physics, Electrical Engineering & Medicine), since 1950 and 1960, that have been seeking for ways of calculating the electrical and the magnetic field that are produced outside the brain by given sources located within the brain (EEG – MEG problems), or for methods of determining the sources from the measurement of these fields outside the brain (EEG – MEG inverse problems). All the nerve signals of the brain propagate via small electric currents, which produce electric and magnetic fields within and outside the brain due to the coupled nature of electromagnetism for the time-varying phenomena. Electric and magnetic fields, that are generated, are recorded by EEG recording (EEG) & the MEG recording (MEG), respectively. In this work we present the results of surveys in the recent years that have given useful relations for the electric and magnetic field for various models of the brain and in particular the spherical, the spheroid and the ellipsoid model. We also analyzed the reduction in the spherical model from the other two models in order to demonstrate that finally the spherical behavior is restored. Moreover, we listed the silent sources that do not contribute to the creation of the magnetic field in the various models. Finally, we refer to the EEG inverse problem that has not a unique solution and we refer how it can be solved provided that the noise is removed from the data we have - regarding the electric field- with some methods that are explained. The scope of this work is to describe at first the methodology for the analysis of the electric field for the ellipsoid model of the brain (that is the optimal model since the average brain is ellipsoid with axes 9, 6.5, 6 cm) in cartesian coordinates. Then we create a program in matlab in order to record the results, as far as the source and the position of the source are concerned, and their three-dimensional graphics. Our goal is to find and compare the vectors of the sources and the vectors of the positions that these sources are located within the brain, under various conditions determined by changing the various parameters such as the measurement parameters and the errors of the measurement parameters.

Many cognitive and neurological disorders today, such as Autism Spectrum Disorders (ASD) and various forms of epilepsy such as infantile spasms (IS), manifest as changes in voltage activity recorded in scalp electroencephalograms (EEG). Diagnosis of brain disease often relies on the interpretation of complex EEG features through visual inspection by clinicians. Although clinically useful, such interpretation is subjective and suffers from poor inter-rater reliability, which affects clinical care through increased variability and uncertainty in diagnosis. In addition, such qualitative assessments are often binary, and do not parametrically measure characteristics of disease manifestations. Many cognitive disorders are grouped by similar behaviors, but may arise from distinct biological causes, possibly represented by subtle electrophysiological differences. To address this, quantitative analytical tools - such as functional network connectivity, frequency-domain, and time-domain features - are being developed and applied to clinically obtained EEG data to identify electrophysiological biomarkers. These biomarkers enhance a clinician’s ability to accurately diagnose, categorize, and select treatment for various neurological conditions. In the first study, we use spectral and functional network analysis of clinical EEG data recorded from a population of children to propose a cortical biomarker for autism. We first analyze a training set of age-matched (4–8 years) ASD and neurotypical children to develop hypotheses based on power spectral features and measures of functional network connectivity. From the training set of subjects, we derive the following hypotheses: 1) The ratio of the power of the posterior alpha rhythm (8–14 Hz) peak to the anterior alpha rhythm peak is significantly lower in ASD than control subjects. 2) The functional network density is lower in ASD subjects than control subjects. 3) A select group of edges provide a more sensitive and specific biomarker of ASD. We then test these hypotheses in a validation set of subjects and show that both the first and third hypotheses, but not the second, are validated. The validated features successfully classified the data with significant accuracy. These results provide a validated study for EEG biomarkers of ASD based on changes in brain rhythms and functional network characteristics. We next perform a follow-up study that utilizes the same group of ASD and neurotypical subjects, but focuses on differences between these two groups in the sleep state. Motivated by the results from the previous study, we utilize the previously validated biomarkers, including the alpha ratio and the subset of edges found to be a sensitive biomarker of ASD, and test their effectiveness in the sleep state. To complement these frequency domain features, we also investigate the efficacy of several time domain measures. This investigation did not lead to significant findings, which may have important implications for the differences between sleep and wake states in ASD, or perhaps generally for clinical assessment, as well as for the effect of noise on signal in clinically obtained data. Finally, we design a similar analysis framework to investigate a set of clinical EEG data recorded from a population of children with active infantile spasms (IS) (2-16 months), and age-matched neurotypical children, in both wake and sleep states. The goal of this analysis is to develop a quantitative biomarker from the EEG signal, which ultimately we will apply to predict the clinical outcome of children with IS. In addition to spectral and functional network analysis, we calculate time domain features previously found to correlate with seizures. We compare the two populations by each feature individually, test the effects of age on these features, use all features in a linear discriminant model to categorize IS versus neurotypical EEG, and test the findings using a leave-one-out validation test. We find almost every feature tested shows significant population differences between IS and control groups, and that taken together they serve as an effective classifier, with potential to be informative as to disease severity and long-term outcome. Furthermore, analysis of these features reveals two groups, indicating a possibility that these features reflect two distinct qualitative characteristics of IS and seizures.

Value based decisions are among the most common types of decisions made by humans. A considerable body of work has investigated how different types of information guide such decisions, as well as how evaluations of their outcomes retroactively inform the parameters that were used to inform them. Several open questions remain regarding the nature of the underlying representations of decision-relevant information. Of particular relevance is whether or not positive and negative information (i.e. rewards/gains vs. punishments/losses/costs) are treated as categorically distinct, or whether they are represented on a common scale. This question was examined across three different studies utilizing a variety of methods (traditional event-related potentials, multivariate pattern classification, and eye tracking) to obtain a more comprehensive picture of how decision-relevant information is represented A common theme among the three studies was that positive and negative types of information seems to be, at least initially, represented as categorically distinct (whether it be information about gains vs. losses, or value vs. effort). Additionally, integration of different types of information appears to take place during the later phases of the decision period, which may also be when distortions in the representation of value information (ex. loss aversion) may occur. Overall, this body of work advances our understanding of the underpinnings of value based decisions by providing additional insight about how decision-relevant information is represented in a dynamic and flexible manner.

This paper deal with the problem of automatic detection of epileptic events in EEGs from depth electrodes using multiresolution wavelet analysis. The basic problems in events detection are considered: the time localization and characterization of epileptiform events, and the computational efficiency. The algorithm presented is based on a polynomial spline wavelet transform. The multiresolution representation obtained from this wavelet transform and the digital filters derived allow us an automatic detection, efficient and fast, of epileptiform activity. The detector proposed is based on the multiresolution energy function. This paper shows that it is possible to use a multiresolution wavelet scheme for detecting events in a nonstationary signal. EEG records from depth electrodes were analysed and the results obtained are shown.