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A growing body of research is targeted towards characterizing and explaining nicotine’s complex interactions with the ApoE E4 allele on brain responses underlying cognitive processes. However, when and how the ε4 allele modulates neuroelectric brain responses in the presence of nicotine versus nicotine abstinence in nicotine-dependent smokers is not well characterized. Being able to understand this modulation is potentially quite important given that recent research implies that, relative to non-ε4 carriers, young adult carriers of the ε4 allele exhibit greater cognitive benefits from the use of nicotine. In the present study, electroencephalography (EEG) and the oddball-related P3b event-related potential (ERP) were used to better characterize the potential moderating effects of ApoE on P3b ERP amplitude changes associated with overnight nicotine deprivation in dependent smokers. Results showed a significant interaction between ApoE genotype and nicotine use, as ε4 carriers, relative to noncarriers, demonstrated significantly greater decreases following overnight deprivation, relative to prequit baseline levels. Additionally, there was a main of effect of P3b ERP amplitude to target stimuli being greater in ε4 allele carriers than in noncarriers during nicotine use, but no main effect of APOE genotype during overnight nicotine deprivation. These results are consistent with findings that the ApoE genotype moderates the effects of nicotine and alters neuroelectric brain responses associated with selective attention to infrequent target stimuli.

Machine Learning for Analysis of Brain Signals / Event-Related Potential (ERP) measures derived from the electroencephalogram (EEG) have been widely used in outcome prediction of brain disorders. Recently, the ERPs that are transient (EEG) responses to auditory, visual, or tactile stimuli, have been introduced as useful predictors of a positive coma outcome (i.e. emergence from coma). In this study, machine learning techniques were applied for detecting the Mismatch Negativity (MMN) component, which is a transient EEG response to auditory stimuli, and its existence has a high correlation with coma awakening, through analyzing ERPs signals recorded from healthy control brain signals. To this end, two different dimensionality reduction methods, Localized Feature Selection (LFS) and minimum-redundancy maximum-relevance (mRMR) were employed, where a localized classifier and the support vector machine (SVM) with radial basis function (RBF) kernel are used as classifiers. We trained both LFS and mRMR algorithms using signals of healthy brains and evaluated their performance for MMN detection on both healthy subjects and coma patients. The evaluation on healthy subjects, using leave-one-subject-out cross-validation technique, shows the detection accuracy performance of 86.6% (using LFS) and 86.5% (using mRMR). In addition to analyzing brain signals for MMN detection, we also implemented a machine learning algorithm for discriminating healthy subjects from those who have experienced TBI. The EEG signals used in the TBI study were recorded using an ERP paradigm. However, we treated the recorded signals as resting state signals. To this end, we used the mRMR feature selection method and fed the selected features into the SVM classifier that outputs the estimated class labels. This method gives us a poor performance compared to the methods that directly used ERP components (without considering them as resting signals.). We conclude that our hypothesis of treating ERP data as resting data is not valid for TBI detection. / Thesis / Master of Applied Science (MASc)

<p>  </p> <p>EEG is a commonly used method in both research and medical practice that is reliant on electrode to scalp contact to record brain activity.  Anti-black racism is a problem that is prevalent within EEG because of both the differences in hair texture, density, and follicle shape as well as the cultural and historical significance of Black hair and touching Black hair for Black people. The potential impact of Black people being unable to successfully receive EEG substantial including: risk of misdiagnosis, lack of representation within neurophysiological research, and negative experiences to Black patients and participants. In the current study, we began to address the gap in the literature regarding Black hair and EEG by surveying current principal investigators (PIs) who are leading laboratories using EEG as a primary method. The primary objective was to gain an initial understanding of the way in which members of laboratories primarily using EEG in various parts of the country currently engage with Black participants, and to what extent they do so at all. We utilized quantitative and qualitative questions in order to assess a variety of components for each laboratory. We used a case study method approach to data analysis. Our findings suggest that there is value in examining concerns of underrepresentation of Black people in EEG. The laboratories in our study primarily did not have tailored outreach for Black participants. Many laboratories in our sample did not alter protocols for Black participants. Eight of our nine case studies reported additional challenges when working with Black participants in comparison to Non-Black participants; Each of those laboratories reported excluding the Black participant or not using the Black participant’s data after the fact. It is essential that we continue to examine the various components of conducting EEG with Black people to gain a better understanding, and therefor inform future best practices.</p>

Epilepsy is a neurological disorder affecting 1-2 % of the population in the world. People diagnosed with epilepsy are put at high risk of getting injured due to the unpredictable seizures caused by the disorder. Electroencephalography (EEG) in combination with machine learning can be used for prediction of an epileptic seizure. Therefore, a portable prediction device is of great interest with high emphasis for it to be user-friendly. One way to achieve this is by minimizing the number of electrodes placed on the scalp. This study examines the number of electrodes that provide sufficient information for prediction of a seizure. The highest prediction accuracy of 91 %, 97 % sensitivity and 85 % specificity was achieved with as few as 16 electrodes. Due to the limitation of the intracranial EEG recordings further testing must be performed on scalp EEG recordings to provide valid results.

Internalizing behaviors, or behaviors related to behavioral inhibition and the tendency to withdraw from novelty or uncertainty, are stable over time. There is substantial evidence indicating the association between greater resting right lateralized frontal EEG alpha asymmetry and negative affect as well as internalizing behaviors (Coan & Allen, 2003; Henderson, Fox, & Rubin, 2001; Fox, 1991). Further, right frontal asymmetry has been shown to be a stable marker of the presence of psychosocial risk (e.g. child maltreatment; see Peltola, Bakermans-Kranenburg, Alink, Huffmeijer, Biro, & van IJzendoorn, 2014 for meta-analyses). However, little is known about the influences of the home and family environment on the link between EEG asymmetry and behavior. The current study examines the associations between resting frontal EEG asymmetry, temperament, and internalizing behaviors in the context of household chaos, as well as additional models. Participants included 247 6-year-old children recruited as part of a larger study on emotion regulation. Results suggest that while household chaos is marginally associated with concurrent internalizing behaviors, the association does not differ depending on patterns of hemispheric asymmetry. Methodological considerations and future directions are discussed. By understanding the physiological mechanisms underlying risk for internalizing problems as well as potential moderators of this link we can better inform the development and timing of effective prevention strategies.

This study aims to examine psychophysiological signatures of music preference in relation to personality, using electroencephalograms (EEG). To this end, EEG readings of ten participants were performed and analysed. As stimulus material, six music pieces were used based on previous categorisation that defined three distinct dimensions of psychological attributes in music: arousal, valence and depth. For each dimension, a song was chosen to represent the positive and negative end of the dimension respectively. The two personality factors of extraversion and neuroticism were assessed in participants before EEG measurements. Both correlations and comparisons of means were calculated. To enable the comparison of means, participants were divided into different subgroups regarding the characteristic of either high or low scores in each personality factor. Participants high in extroversion as well as those low in neuroticism showed lower arousal, signs of higher engagement by means of alpha-wave suppression, and rated songs higher than other participants. Positive and negative ends of the music dimensions did not show differences in EEG measurements. Frontal asymmetry did not differ regarding participants ratings for all the six music pieces. Results based on differences in EEG signatures between high and low scores within the personality factors extroversion and neuroticism, indicate that there is a connection between personality and music preference. Further research is needed to better understand the interaction between psychophysiological signatures and music preference.

<p> The focus of this thesis is on the utilization of the sparsity concept in solving some challenging problems, e.g., finding a unique solution to the under-determined linear system of equations in which the number of equations is less than the number of unknowns. This concept is extended to the problem of solving the under-determined blind source separation (BSS) problem in which the number of source signals is greater than the number of sensors and both the mixing matrix and the source signals are unknowns. In this respect we study three problems: </p> <p> 1. Developing some algorithms for solving the under-determined linear system of equations for the case of a sparse solution vector. In this thesis we develop a new methodology for minimizing a class of non-convex (concave on the non-negative orthant) functions for solving the aforementioned problem. The proposed technique is based on locally replacing the original objective function by a quadratic convex function which is easily minimized. For a certain selection of the convex objective function, the existing class of algorithms called Iterative Re-weighted Least Squares (IRLS) can be derived from the proposed methodology. Thus the proposed algorithms are a generalization and unification of the previous methods. In this thesis we also propose a convex objective function that produces an algorithm that can converge to a sparse solution vector in significantly fewer iterations than the IRLS algorithms.</p> <p> 2. Solving the under-determined BSS problem by developing new clustering algorithms for estimating the mixing matrix. The under-determined BSS problem is usually solved by following a two-step approach, in which the mixing matrix is estimated in the first step, then the sources are estimated in the second step. For the case of sparse sources, the mixing matrix is usually estimated by clustering the columns of the observation matrix. In this thesis we develop three novel clustering algorithms that can efficiently estimate the mixing matrix, as well as the number of sources, which is usually unknown. Numerical simulations verify the efficiency of the proposed algorithms compared to some well known algorithms that are usually used for solving the same problem.</p> <p> 3. Extraction of a desired source signal from a linear mixture of hidden sources when prior information is available about the desired source signal. There are many situations in which one is interested in extracting a specific source signal. The a priori available information about the desired source signal could be temporal, spatial, or both. In this thesis we develop new algorithms for extracting a desired sparse source signal from a linear mixture of hidden sources. The information available about the desired source signal, as well as its sparsity, are incorporated in an optimization problem for extracting this source signal. Four different algorithms have been developed for solving this problem. Numerical simulations show that the proposed algorithms can be used successfully for removing different kind of artifacts from real electroencephalographic (EEG) data and for estimating the event related potential (ERP) signal from synthesized EEG data.</p> / Thesis / Doctor of Philosophy (PhD)

<p> The study of the different sleep stages of a patient using his/her recorded EEG signals falls in the area of signal classification. In general, this involves extracting from the EEG signals, a signal feature on which the classification is performed. In this thesis, we apply the techniques of signal classification to the analysis of the sleep of a patient. The feature we use is the power spectral density (PSD) matrices of a multi-channel EEG signal. This not only allows us to examine the power spectrum contents of each signal which complies with what clinical experts use in their visual judgement of EEG signals, but also allows the correlation between the multi-channel signals to be studied. To establish a metric facilitating the classification, we analyze the structure as well as exploit the specific geometric properties of the space of PSD matrices. Specifically, we study this space from the viewpoint of Riemannian manifolds. We apply a Riemannian metric and, with the aid of fibre bundle theory, develop intrinsic (geodesic) distance measures for the PSD matrix manifold. To utilize such new distance measures effectively for EEG signal classification, we need to find a suitable weighting matrix for the PSD matrices so that the distances between similar features are minimized while those between dissimilar features are maximized. A closed form expression for this weighting matrix is obtained by solving an equivalent convex optimization problem. The effectiveness of using these novel weighted distance measures is verified by applying them to the sleep pattern classification of a collection of recorded EEG signals using the k-nearest neighbor decision algorithm with excellent results. </p> / Thesis / Doctor of Philosophy (PhD)

Event Related Potential (ERP) measures derived from the electroencephalogram (EEG) have been widely used in research on language, cognition, and pathology. The high dimensionality (time x channel x condition) of a typical EEG/ERP dataset makes it a time-consuming prospect to properly analyze, explore, and validate knowledge without a particular restricted hypothesis. This study proposes an automated empirical greedy approach to the analysis process to datamine an EEG dataset for the location, robustness, and latency of ERPs, if any, present in a given dataset. We utilize Support Vector Machines (SVM), a well established machine learning model, on top of a preprocessing pipeline that focuses on detecting differences across experimental conditions. A hybrid of monte-carlo bootstrapping, cross-validation, and permutation tests is used to ensure the reproducibility of results. This framework serves to reduce researcher bias, time spent during analysis, and provide statistically sound results that are agnostic to dataset specifications including the ERPs in question. This method has been tested and validated on three different datasets with different ERPs (N100, Mismatch Negativity (MMN), N2b, Phonological Mapping Negativity (PMN), and P300). Results show statistically significant, above-chance level identification of all ERPs in their respective experimental conditions, latency, and location. / Thesis / Master of Science (MSc)