Epilepsy is a nervous system disorder which affects approximately 1% of the world's population. Nearly 25% of people who have epilepsy are resistant to traditional treatments such as medication and are not candidates for surgery [32]. A new form of treatment has emerged that attempts to disrupt epileptic activity in the brain by electrically stimulating neural tissue. However, the nature of this treatment requires that it is able to accurately predict the onset of a seizure in order to time the intervention correctly. Recent studies suggest that EEG recordings may be generated by a low dimensional nonlinear process [35] [36] [6]. This paper will investigate nonlinearity tests, as well as the use of methods from the theory of nonlinear dynamical systems in the prediction of seizures or seizure like events (SLEs) from complex time series. To do this data is generated from a nonlinear dynamical system with a stochastic time dependent parameter, which attempts to emulate the different states of an epileptic brain. Two kinds of nonlinearity tests were used in simulations, one which specifies a model in the alternative hypothesis (Keenans test) and one which simply states that the process is `not linear' (Surrogate data test). The tests were applied to the generated data, as well as a short EEG recording from a person with epilepsy and a simple nonstationary example. Both tests were able to correctly identify the model as nonlinear, neither test identified the EEG data as nonlinear and there were contradicting results when the tests were applied to nonstationary data. Estimates of the correlation dimension and Lyapunov exponent were then used to classify the preictal state of the model data. Correlation dimensions showed the best ability to classify states, so they were used in the prediction algorithm. The results of the simulation was that the correlation dimension was able to successfully predict half of the SLEs, however there was an alarmingly high false prediction rate. These results suggest that even though a complicated model may fit the data better, when dealing with prediction it is usually best to use a simple model. A simpler approach with better understood statistical properties may be able to improve on the prediction of SLEs as well as reduce the computational cost of performing them.

Epilepsy is a nervous system disorder which affects approximately 1% of the world's population. Nearly 25% of people who have epilepsy are resistant to traditional treatments such as medication and are not candidates for surgery [32]. A new form of treatment has emerged that attempts to disrupt epileptic activity in the brain by electrically stimulating neural tissue. However, the nature of this treatment requires that it is able to accurately predict the onset of a seizure in order to time the intervention correctly. Recent studies suggest that EEG recordings may be generated by a low dimensional nonlinear process [35] [36] [6]. This paper will investigate nonlinearity tests, as well as the use of methods from the theory of nonlinear dynamical systems in the prediction of seizures or seizure like events (SLEs) from complex time series. To do this data is generated from a nonlinear dynamical system with a stochastic time dependent parameter, which attempts to emulate the different states of an epileptic brain. Two kinds of nonlinearity tests were used in simulations, one which specifies a model in the alternative hypothesis (Keenans test) and one which simply states that the process is `not linear' (Surrogate data test). The tests were applied to the generated data, as well as a short EEG recording from a person with epilepsy and a simple nonstationary example. Both tests were able to correctly identify the model as nonlinear, neither test identified the EEG data as nonlinear and there were contradicting results when the tests were applied to nonstationary data. Estimates of the correlation dimension and Lyapunov exponent were then used to classify the preictal state of the model data. Correlation dimensions showed the best ability to classify states, so they were used in the prediction algorithm. The results of the simulation was that the correlation dimension was able to successfully predict half of the SLEs, however there was an alarmingly high false prediction rate. These results suggest that even though a complicated model may fit the data better, when dealing with prediction it is usually best to use a simple model. A simpler approach with better understood statistical properties may be able to improve on the prediction of SLEs as well as reduce the computational cost of performing them.

Functional asymmetries, most commonly associated in humans with population-level hand preference and lateralization in language processing, are complex, heterogeneous traits with poorly understood biological and genetic bases. Notably, functional asymmetries are also associated with familial non-right handedness suggesting that common genetic factors influence both handedness and functional lateralization. This dissertation has two aims. The first is the development of a specific biological hypothesis that may partially account for the consistent co-lateralization of hand preference and prefrontal language function. I argue that asymmetries in local neural properties that affect the excitability and signal-to-noise ratio of neural assemblies can produce a bias in the direction and, to some extent, the degree of functional lateralization for complex functions. At a high level of representation, this hypothesis is similar to long-standing theories of hemispheric differences, but differs from these by providing a single biological difference between hemispheres that influences both motor and prefrontal asymmetries. Specifically, I propose that a hemispheric asymmetry in the ratio of activity at D1 and D2 dopamine receptors can account for both forms of asymmetry. The second aim is to identify novel electrophysiological and behavioral correlates of genetic effects linked to handedness. By applying a standard genetic model to familial handedness data, I obtain an estimate of these genetic effects for individual research participants that may improve sensitivity over previous studies that have primarily used categorical classifications to study familial handedness effects. Two EEG studies of executive function provide evidence for computational changes associated with familial handedness. The first, an auditory oddball paradigm, suggests that cortical noise is increased in conjunction with estimated genetic effects associated with left handedness. In the second study, a go-nogo task, a dissociation between response inhibition and response conflict processing was found with respect to estimated genetic effects associated with left handedness. In addition to bearing on current theories of conflict processing, these results may provide indirect evidence for dopaminergic contributions to neurological and behavioral differences associated with familial sinistrality. Additional studies of resting EEG and behavioral responses to Necker cube viewing provide additional evidence for broad effects of familial sinistrality.

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The acquisition of a behavioural response (learning) and the later retrieval of this response (memory) are separated by an endogenous biological process which consolidates the temporary neural changes initiated by training. Intracerebral infusions of stimulants to the hippocampus potentiate this process and infusions of protein synthesis inhibitors (PSIs) impair it. A tacit assumption regarding the application of PSIs is that they have no effect upon spontaneous brain electrical activity; however, given their documented non-specific side effects, this idea was re-evaluated under controlled conditions. Hippocampal recordings were made in urethane anaesthetized rats before and after unilateral hippocampal infusions of the PSIs anisomycin and cycloheximide. Infusions suppressed local field potentials, eliminated sink/source alternations and silenced multiunit activity without affecting the contralateral hippocampus. This suppression was correlated with the degree of protein synthesis inhibition. These results present a serious confound for all results obtained using anisomycin and cycloheximide to test memory consolidation.

The ongoing electrical activity of the brain is known as the electroencephalograph (EEG). Event related potentials (ERPs) are voltage deviations in the EEG elicited in association with stimuli. Their elicitation require cognitive processes such as response to a recognised stimulus. ERPs therefore provide clinical information by allowing an insight into neurological processes. The amplitude of an event-related potential is typically several times less than the background EEG. The background EEG has the effect of obscuring the ERP and therefore appropriate signal processing is required for its recovery. Traditionally ERPs are estimated using the synchronised averaging of several single trials or sweeps. This inhibits investigation of any trial-to-trial variation, which can prove valuable in understanding cognitive processes. An aim of this study was to develop wavelet-based techniques for the recovery of single trial ERPs from background EEG. A novel wavelet-based adaptive digital filtering method for ERPs has been developed. The method provides the ability to effectively estimate or recover single ERPs. The effectiveness of the method has been quantitatively evaluated and compared with other methods of ERP estimation. The ability to recover single sweep ERPs allowed the investigation of characteristics that are not possible using the conventional averaged estimation. The development of features of a cognitive ERP known as the contingent negative variation over a number of trials was investigated. The trend in variation enabled the identification of schizophrenic subjects using artificial intelligence methods. A new technique to investigate the phase dynamics of ERPs was developed. This was successfully applied, along with other techniques, to the investigation of independent component analysis (ICA) component activations in a visual spatial attention task. Two components with scalp projections that suggested that they may be sources within the visual cortex were investigated. The study showed that the two components were visual field selective and that their activation was both amplitude and phase modulated.

The main objective of this study was to investigate whether event related potentials (ERPs) can be used as a biomarker of disease severity staging in Alzheimer’s disease (AD) within a heterogeneous group of patients presenting to a memory disorders clinic for initial evaluation. Based on the known progression of AD pathology, we hypothesized ERP components would be abnormal, commensurate with disease severity in mild cognitive impairment (MCI) due to AD, mild, and moderate to severe dementia due to AD. ERP components were predicted based on the known sites of their neural generators. ERP peaks measured during an auditory oddball paradigm from twenty-two AD (n=9) and non-AD (n=13) patients were compared to their clinical outcomes using multivariate ANCOVA controlling for age with Bonferroni corrections. The predictive abilities of significant ERP components were examined using a binary logistic regression model. Significant between-group effects were found in N100 distractor amplitude, F(2, 12) = 6.062, p = .015, ηp2 = .503. The results supported our hypothesis that N100 amplitude would be increased in AD, suggesting that sensory gating may be more impaired in mild AD than in non-AD related cognitive impairment.

Decreases in overall well-being and daily functioning result from unpleasant and uncomfortable symptoms associated with physical health and mental health disorders. Neurofeedback training, rooted in the theory of operant conditioning, presents the possibility of increasing brain wave regulation, decreasing symptoms experienced from abnormal brain wave activity, and increasing overall well-being and daily functioning. The efficacy of neurofeedback for physical and mental health outcomes is unclear, contributing to confusion about the treatment and any potential benefits. In order to assess the efficacy of neurofeedback in the alleviation of physical health and mental health symptoms, a systematic review and meta-analysis of neurofeedback using a random effects model to generate the effect sizes was conducted on 21 studies with 22 comparisons that used neurofeedback to treat patients. The results showed that neurofeedback can be effective for physical and mental health outcomes, including for autism with an effect size of 0.29, tinnitus with an effect size of 0.77, schizophrenia with an effect size of 0.76, depression with an effect size of 0.28, insomnia with an effect size of 0.52, obesity with an effect size of 0.40, intellectual disability with an effect size of 0.73, and pain with an effect size of 0.30. Well-being and daily functioning for those with physical and mental health disorders can be improved. These findings have implications for clinical practice to help patients in treatment for physical and mental health problems, and also for social change by providing evidence for alternative health care options.

Several studies have made use of EEG features to detect specific mental health illnesses such as epilepsy or schizophrenia, as supplementary diagnosis to the usual symptom-based diagnoses. At the same time general mental health diagnostic tools (biomarker or symptom-based) to identify individuals who are manifesting early signs of mental health disorders are not commonly available. This thesis seeks to explore the potential use of EEG features as a biomarker-based tool for general mental health diagnosis. Specifically, the predictive ability using machine learning of a general biomarker derived from EEG readings elicited from an oddball auditory experiment to predict someone’s mental health status (mentally ill or healthy) is investigated in this study. Given that mindfulness exercises are regularly provided as treatment for a wide range of mental illnesses, the features of interest seek to quantify it as a measure of mental health. The 2 feature sets developed and tested in this study were collected from a traumatic brain injury (TBI) and healthy controls dataset. Further testing of these feature sets was done on the Bipolar and Schizophrenia Network on Intermediate Phenotypes (BSNIP) dataset containing multiple mental illnesses and healthy controls to test the features for generalizability. Feature Set 1 consisted of the average and variance of P300 and N200 ERP component peak amplitudes and latencies across the centroparietal and fronto-central EEG channels respectively. Feature Set 2 contains the average and variance of P300 and N200 ERP component mean amplitudes across the centro-parietal andfronto-central EEG channels respectively. The predictive ability of these 2 feature sets was tested. Logistic regression, support vector machines, decision trees, random forests, KNN classification algorithms were used, and random forest and KNN were used in combination with oversampling to predict the mental health status of the subjects (whether they were cases or healthy controls). The model performance was tested using accuracy, precision, sensitivity, specificity, f1 score, confusion matrices, and AUC of the ROC. The results of this thesis show promise on the use of EEG features as biomarkers to diagnose mental illnesses or to get a better understanding of mental wellness. The use of this technology opens doors for more accurate, biomarker-based diagnosis of mental health conditions, lowering the cost of mental health care, and making mental health care accessible for more people.

The job-related fatality rate in the construction industry is high as a result of multiple factors associated with the safety of workers. However, mental fatigue, a prominent factor affecting one’s hazard perception, from engagement in construction tasks and its effects on fall hazard has not been adequately studied. This thesis proposes a two-trajectory framework to assess mental fatigue using Electroencephalography (EEG). Primarily, Wavelet Packet Decomposition (WPD) was used to obtain energy in each brain wave, and seven mental fatigue indices including θ, α, β, α/β, θ/α, θ/β, and (θ + α)/β were calculated. Secondarily, sample entropy (SampleEn) values were calculated for groups under comparison to examine the results from the WPD. Results from the adopted method suggest that typical construction activities and height exposure can cause mental fatigue and reduce vigilance level in workers. It is essential to have a quantitative approach for continuous cognitive monitoring to enhance construction safety.