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Given the ecological importance of face recognition, it is not surprising that the visual system is capable of processing faces with remarkable efficiency. When presented with a face, information is rapidly extracted to detect and categorize it as a face, followed by face-specific information such as age, gender, and identity. According to cognitive and neural models, the processes underlying face recognition encompass a sequence of steps that begin with a perceptual or visual analysis followed by more image-invariant and identity-selective representations. Importantly, it is only familiar faces for which we have acquired long-term face memories that reach the final stages of identity processing to permit robust, image-invariant behavioural recognition. A key aspect of face processing is that it is fast and automatic. This can be said for both high-level categorization (i.e., detecting that a stimulus is a face) and for encoding at the identity-level. The purpose of these experiments was to use novel electrophysiological and psychophysical techniques to characterize these fast and automatic categorization processes. Experiment 1 and 2 used an implicit visual discrimination paradigm (fast periodic visual stimulation; FPVS) combined with electroencephalography (EEG) to isolate identity-specific neural responses to a personally familiar face, the own-face, and an unfamiliar stranger face. Experiment 1 showed that identity-specific responses recorded over the occipito-temporal region were stronger for a personally familiar face compared to the unfamiliar control identity, while the response to the own-face was even greater than to a personally familiar friend. In Experiment 2, identity-specific responses for a given identity were measured in participants both before and after real-world familiarization. As expected, the results showed a significant increase in the identity-specific response once participants became personally familiar with the test identities. In Experiment 3, we used saccadic eye movements to estimate the lower bounds of the speed of face categorization, and in particular to investigate the question of whether this categorization occurs during early feedforward processing. The results support the view that information needed to detect and selectively respond to face stimuli happens during the earliest visual processing. Collectively, these studies provide additional insight on the mechanisms underlying rapid and automatic face detection and face identity recognition. / Graduate

<p>It has been observed by researchers that periodic auditory stimuli can cause the activities in different brain areas to be periodically synchronized. Fast auditory stimuli have been shown to cause the brain sources to synchronize at the rate of stimuli. Brain sources respond to them not only by increase in local synchronization, but also in the global synchronization of cortical regions often regarded as functional connectivity. Spectral power and coherence are often used to characterize such neural synchronization. Beta band oscillations have been reported to underlie the neural mechanism during repetitive auditory stimuli. Cortical generators of these underlying beta oscillations were investigated in several studies based on MEG measurements. This research is intended to investigate (1) EEG can be used to detect and localize neural sources changing in power and coherence and (2) beta oscillations underlie such neural synchronization during fast repetitive auditory stimuli based on EEG measurements. The procedure of this study consists of several steps. First, the minimum variance (MV) scalar beamformer, an adaptive spatial filter, is used to estimate the temporal signals in the brain source space, given EEG recordings. The analysis of the estimated source temporal signals then consists of two stages firstly the power analysis and secondly the coherence analysis. The dynamics of power and coherence is investigated instantaneously over time and in the lower beta frequency band [14,20Hz]. This is done by detecting the most prominent changes in the two spectral parameters through singular value decomposition (SVD). Two coherence measures imaginary component (IC) and magnitude-squared coherence (MSC) are employed and compared in terms of their performance both mathematically and experimentally. In the simulations, we show the capability of using EEG to detect and localize power co-variations and dynamic functional connectivity in the cortical regions. We also perform the procedure on the recorded real data from subjects passively listening to rhythmic auditory stimuli. Beta oscillations are found to underlie the neural activity to percept auditory stimuli. This is shown by localization of auditory cortices and detection of power co-variation in this frequency band. We demonstrate the feasibility of using EEG to identify coupled and co-activated brain sources similar to those obtained from MEG signals in the previous studies. These include auditory and motor regions which were found to be functionally coherent and have a functional role in the auditory perception. The superiority of IC over MSC measure is proven mathematically and validated in both simulations and real data experiments.</p> / Master of Applied Science (MASc)

There has been a recent growth of interest in exploring the complexities of multisensory processes underlying human body representation. One cross-modal aspect of body representations involves the visuotactile body mapping between tactile stimulation of a body limb and the observation of a body limb. Previous findings demonstrate that visual information influences the spatial and temporal patterning of brain responses to tactile stimulation. By manipulating the congruency of the visuotactile stimuli, the integration of visual and tactile information of the body can be investigated further. In the current studies, electroencephalography (EEG) was used to record the neural responses to touch during congruent and incongruent visuotactile stimuli in adults and infants. Two studies investigated different characteristics of visuotactile congruency on the neural response to touch during observations of others’ bodies. In Study 1, spatial congruency of visuotactile events in adults was examined by recording electrophysiological responses to tactile stimulation of the hand in different postural positions while viewing pictures of hands. In Study 2, visuotactile body mappings were explored within typically developing, preverbal infants. In the second study, infants received tactile stimulation to their hand or foot while viewing the hand or foot of another person. The findings of both studies indicate neural modulations were driven by the viewed stimuli regardless of the visuotactile congruency suggestive of attentional factors at work during late stages of somatosensory processing. / Psychology

Shyness is a personality trait that is stable across time and situations in some individuals. While childhood shyness is a risk factor for later mental health and emotional problems, not all shy children grow up to have these problems. This thesis examined subtypes of shyness identified based on the temporal stability of shyness and based on levels of sociability and their corresponding outcomes, as well as the roles of social and biological contextual factors. Chapters 2-4 comprise the empirical studies. In Chapter 2, I report three shyness trajectories from middle childhood to adulthood (ages 8 to 30-35). Relative to a low-stable non-shy trajectory, children with an increasing, but not a decreasing, shy trajectory were at higher risk for clinically significant social anxiety, depression, and substance use, and were hypervigilant to angry faces in adulthood. Chapters 3 and 4 then report electrocortical correlates and mechanisms during the processing of non-social auditory novelty and social exclusion across children, adolescents, and adults with varying levels of shyness and sociability. Chapter 3 established that shyness, but not sociability, was related to the P300 ERP in processing non-social auditory stimuli in both 10-year-old children and adults, in support of the notion that shyness and sociability are independent personality dimensions. Findings on subtypes of shyness also showed that children characterized by conflicted shyness (with high levels of both shyness and sociability) reported higher neuroticism, but this relation was mediated by increased P300 amplitudes to processing background stimuli. Finally, Chapter 4 reports that individuals characterized by conflicted shyness who exhibited high theta EEG spectral power to social exclusion were most fearful of negative evaluation, irrespective of age. Also, conflicted shy adolescents who showed high theta spectral power to social exclusion were most likely to engage in substance-use. These findings highlight that there is much heterogeneity in shyness, and that shyness is not directly related to adverse mental health outcomes. / Thesis / Doctor of Philosophy (PhD) / Shyness is a personality trait that is stable across time and situations in some individuals. Past research suggests that shy children exhibit more internalizing problems, including anxiety and depression, compared to their non-shy counterparts. However, the development of shyness has not been studied beyond adolescence, and the biological and social factors that contribute to adverse developmental pathways and outcomes related to shyness are not well understood. The goal of this thesis was to understand the mental health outcomes of shy individuals by examining different subtypes of shy individuals. To this end, this thesis first demonstrated how shyness unfolds across the first four decades of life to shape adult mental health outcomes in a cohort of individuals. Second, this thesis examined how neural responses to threatening social and non-social contexts related to the socioemotional outcomes across children, adolescents and adults with varying levels of shyness.

In recent years, emotion classification using electroencephalography (EEG) has attracted much attention with the rapid development of machine learning techniques and various applications of brain-computer interfacing. In this study, a general model for emotion recognition was created using a large dataset of 116 participants' EEG responses to happy and fearful videos. We compared discrete and dimensional emotion models, assessed various popular feature extraction methods, evaluated the efficacy of feature selection algorithms, and examined the performance of 2 classification algorithms. An average test accuracy of 76% was obtained using higher-order spectral features with a support vector machine for discrete emotion classification. An accuracy of up 79% was achieved on the subset of classifiable participants. Finally, the stability of EEG patterns in emotion recognition was examined over time by evaluating consistency across sessions. / Thesis / Master of Science (MSc)

Concussion, defined as a functional injury with complex symptomatology, affects millions annually and has been classified as a serious public health concern. Clinical tools currently available for concussion assessment fail to objectively measure cognitive function and thus, are inadequate for proper evaluation of the cognitive dysfunctions associated with the injury. As a result, investigation into the neurological consequences associated with concussion has become a prominent focus in neuroscience research. Traditionally, neuroimaging methods have been used primarily on concussion detection, while behavioural and neuropsychological assessments have been used for both concussion detection and cognitive-performance tracking. However, to date, minimal work has explored the use of neuroimaging to track the consequences of concussion at the neurophysiological level. Accordingly, the present thesis sought to investigate the clinical applicability of electroencephalography (EEG) as an effective neuroimaging tool capable of concussion detection, as well as its ability to objectively track neurophysiological changes over time. Event-related potentials (ERPs) were used to assess specific functions, or more accurately, dysfunctions of select cognitive processes as reflected by electrophysiological changes in the brain. Specifically, the Mismatch Negativity (MMN), N2b, and P300 were investigated to evaluate memory, attention, and executive control in concussed populations. The results of this thesis demonstrated alterations in each of the aforementioned ERPs, signifying cognitive dysfunctions linked to neurophysiological abnormalities in concussed populations. Of particular importance, Chapter 2 revealed the first instance of MMN abnormalities in a concussed population, Chapter 3 was the first to assess concussed adolescents at the acute stage of their injury, and Chapter 4 demonstrates the potential of ERPs to track neurophysiological changes from the acute to post-acute stages of the injury. Ultimately, the findings presented in this dissertation support the clinical viability of using ERPs to not only detect cognitive dysfunctions associated with concussion, but also to objectively track neurophysiological changes on the path to recovery. / Dissertation / Doctor of Philosophy (PhD)

While dreaming, one lacks the understanding that what is experienced is self-generated hallucinatory contents of consciousness. However, during dreaming there is a rare state called lucid dreaming. The minimal requirement for a dream to be considered lucid is that one is self-aware that one is currently sleeping. If self-awareness is the minimal criterion for lucid dreaming, that would entail the activation of those brain areas and networks typically related to self-referential processing. Further, lucid dreaming often entails the ability to exert volition over dream content. This thesis is a systematic review of the neural correlates of lucid dreaming and investigates the potential overlap of the neural correlates of lucid dreaming and volition. Only peer-reviewed original empirical articles that used healthy adults as participants were included. Thus, five studies were found. Two of the studies used functional magnetic resonance imaging (fMRI), two used electroencephalography (EEG), and one used both EEG and fMRI. This thesis found that the precuneus and left parietal lobe, which are brain areas related to self-referential processing, have increased activity during lucid dreaming compared to non-lucid dreaming. Also, the dorsolateral prefrontal cortex (DLPFC) has increased functional connectivity in people who are more likely to experience lucid dreaming. DLPFC has been associated with metacognitive functions, which includes volition.There also seems to be an overlap in brain regions activated in volition compared to lucid dreaming; these areas include the parietal cortex, supplementary motor area, and anteriorprefrontal cortex.