During the first year of life, infants’ capacities for face processing are shaped by experience with faces in their environment; a process known as perceptual narrowing. Perceptual narrowing has been found to lead to a decline in infants’ abilities to identify and differentiate faces of other races. In the current study, it is hypothesized that this decline may also lead to differential processing of emotion information in own- versus other-race faces. In the current research, we recorded electrophysiological data (Event-related potential; ERP) from 5- and 9-month-old infants while they were presented with paired emotion non-verbal sounds and faces. ERPs in response to the sounds suggest that both 5- and 9-month old infants differentiate happy and sad sounds. The pattern of results, however, is different across ages. ERPs in response to the faces suggest that whereas 5-month-olds exhibit differential responses to happy and sad faces for both the N290 and P400 components, 9-month-olds did not differentiate happy and sad faces. Nine-month old infants did exhibit a great P400 in response to own- relative to other-race faces. These results suggest that although both 5- and 9-month olds differentiate happy and sad emotional sounds, their processing of emotion faces differs.

A brain-computer interface (BCI) is a method of communication that utilizes the scalp recorded electroencephalogram (EEG). A BCI requires no movement, making it a viable communication option for people who are severely disabled. Most BCI research has focused on improving BCI technology through advances in signal processing and paradigmatic manipulations. Research has recently begun to examine the influence of psychosocial factors on BCI performance. Examining psychosocial factors may be particularly important for disabled people who have several co-morbidities. The purpose of the current study is to examine the hypothesis that participants will be more motivated in a free spelling paradigm than in a copy spelling paradigm. Participants completed copy- and freespelling tasks, order was counterbalanced. Motivation was measured after each task. Preliminary data suggests an increase in motivation after the second task regardless of which task was performed second. No differences were observed in performance accuracy between the two tasks.

<p>Cognitive impairments in schizophrenia (SZ) include abnormalities in executive function, attention, and semantic processing. Event-related potentials (ERPs) are used as neurophysiological measures of cognitive impairment that have been shown to map onto symptom dimensions of psychotic disorders, such as schizophrenia. While much research exists on schizophrenia, less is understood about the longitudinal relationships between ERPs and symptom dimensions among individuals at risk for psychosis. Of published work in risk samples, most have been cross-sectional, leaving clinical inferences regarding longitudinal patterns non-specific. The current study aimed to bridge this gap by recording ERPs (P300, ERN, N400) across a battery of tasks within a single risk sample, and measured positive, negative, and disorganized symptom severity via the Multidimensional Schizotypy Scale (MSS). Participants exhibiting psychosis-risk were recruited from the community (N=60), and completed a baseline and 6-month follow-up assessment (n=29). The primary goal of the baseline assessment aimed to replicate ERP-symptom dimension relationships observed in the SZ literature. Effect sizes for P300-positive and ERN-negative relationships were observed to be in the same directionality as noted in the clinical SZ literature. While not statistically significant, the small effects suggest that P300 and ERN may be similarly effected by presence of positive and negative symptoms, respectively. By contrast, N400, however, was found to have an effect size directionality opposite to that reported in the literature. This finding is consistent with mixed presentation of disorganized symptoms in clinical SZ populations. The follow-up assessment aimed to examine the relationship of symptom dimensions over time in a single at-risk sample, and leveraged ERPs as potential prospective predictors of worsening of symptoms. As expected, baseline symptoms prospectively predicted corresponding symptoms at follow-up. However, only N400 amplitude at baseline correlated with disorganized symptoms at follow-up, and no ERP prospectively predicted corresponding symptom dimensions at follow-up. Overall, examining the relationship between multiple ERPs and symptom dimensions in a single sample and via a longitudinal design is a novel addition to the literature. Future research will be necessary to clarify the use of ERPs as neural biomarkers to identify and predict symptom severity over time, ultimately reducing subjectivity in clinical diagnosis and treatment. </p>

Monitoring the accuracy of memory is an automatic but essential process of memory encoding and retrieval. Retrospective memory confidence judgments are making effective and efficient decisions based on one's memories. The neural processes involved in retrospective confidence ratings were investigated with EEG and fMRI using a recognition memory task designed such that participants also rated their confidence in their memory response. Correct trials (hits and correct rejections) were examined for differences related to the participants' level of confidence in their response. There were significant differences in electrophysiological activity (in the FN400 and the late parietal component) associated with confidence rating, with mean deflection increasing as confidence decreased. fMRI analysis revealed activity that appeared to be specific to the process of confidence rating. Activity was found to increase in the medial frontal, lateral frontal, and lateral parietal cortices as confidence decreases, but only for hits. In the lateral frontal, lateral parietal, and medial parietal cortices, activity decreased as confidence increased. These data indicate that there are neural mechanisms specifically related to making retrospective memory confidence judgments.

A word’s predictability has been shown to influence its processing. Two methodologies have demonstrated this time and again: eye tracking while reading and Event Related Potentials (ERPs). In eye tracking while reading, words that are made predictable by their contexts (as operationalized by the cloze task; Taylor, 1953) receive shorter first fixation times (Staub, 2015, for a review) as well as shorter gaze duration and increased skipping rate. In ERPs, the N400 component’s amplitude has also been shown to inversely correlate with a word’s predictability (Kutas and Federmeier, 2011, for a review). Despite the similarities, there is much reason to suspect that these two measures are reflections of different underlying cognitive processes, both modulated by a word’s predictability. We utilized the simultaneous collection of EEG and eye tracking data to investigate the differential effects of lexical predictability and stimulus quality on these measures. We found that these two manipulations had additive effects in the eye movement record, but yet only the manipulation of predictability influenced the N400 Fixation Related Potential (FRP) amplitude, with stimulus quality influencing neither the amplitude nor the latency of the N400. These findings provide no evidence for there being a role for predictability in early visual processing, and thus call into question the relative ordering of lexical processing effects laid out in Staub and Goddard (2019). Our findings also suggest that the N400’s underlying process is strictly temporally fixed and indexes the lexical processing difficulty left after there has already been a convergence of evidence towards the identity of the observed stimulus.

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The human brain is unquestionably the most complex organ of the body as it controls and processes its movement and senses. A healthy brain is able to generate responses to the signals it receives, and transmit messages to the body. Some neural disorders can impair the communication between the brain and the body preventing the transmission of these messages. Brain Computer Interfaces (BCIs) are devices that hold immense potential to assist patients with such disorders by analyzing brain signals, translating and classifying various brain responses, and relaying them to external devices and potentially back to the body. Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major, yet very challenging, step in developing Brain Computer Interfaces (BCIs). Of primary importance is to use less data and computationally efficient algorithms to support real-time BCI. To this end, in this thesis we explore and develop algorithms that exploit the sparse characteristics of EEGs to classify these signals. Different feature vectors are extracted from EEG trials recorded by electrodes placed on the scalp. In this thesis, features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the EEG training signals for each class. This is used to classify the signals based on the pattern of their sparse representation using a minimum-residual decision rule. We first attempt to use all the available electrodes to verify the effectiveness of the proposed methods. To support real time BCI, the electrodes are reduced to those near the sensorimotor cortex which are believed to be crucial for motor preparation and imagination. In a second approach, we try to incorporate the effect of spatial correlation across the neighboring electrodes near the sensorimotor cortex. To this end, instead of considering one feature vector at a time, we use a collection of feature vectors simultaneously to find the joint sparse representation of these vectors. Although we were not able to see much improvement with respect to the first approach, we envision that such improvements could be achieved using more refined models that can be subject of future works. The performance of the proposed approaches is evaluated using different features, including wavelet coefficients, energy of the signals in different frequency sub-bands, and also entropy of the signals. The results obtained from real data demonstrate that the combination of energy and entropy features enable efficient classification of motor imagery EEG trials related to hand and foot movements. This underscores the relevance of the energies and their distribution in different frequency sub-bands for classifying movement-specific EEG patterns in agreement with the existence of different levels within the alpha band. The proposed approach is also shown to outperform the state-of-the-art algorithm that uses feature vectors obtained from energies of multiple spatial projections.

The present study explores the nature and structure of spiritual and aesthetic experiences through the interdisciplinary application of neurophenomenology (NP). This approach merges aspects of psychology, neurophysiology, and phenomenology into a unified methodology. The study is nested within a larger project, Space, Science, and Spirituality, and as such, it carries a common goal to use simulation to evoke spiritual and aesthetic responses similar to those expressed by astronauts and cosmonauts. Careful analysis of previous work in NP provided methodological “lessons learned”, which guided the experimental design, execution, and analysis of the present study. The data collected provides support for experience as a phenomenon that can be studied through empirical means. Further, the articulation of spiritual and aesthetic experiences akin to astronaut experiences corresponds to specific neurological and psychological indicators. Among those indicators are differences in EEG measures during simulation time relative to expressions of spiritual experience following the simulation and changes in visual processing across theta, alpha, and beta signals as correlated with self-identification. These findings support an embodied theory of experience that incorporates memory, executive function, perception, and consciousness. In addition to its academic contribution, this research holds implications for commercial space flight, long-term space missions, post-traumatic stress disorder therapies, and the entertainment industry

No description available.

In this thesis, the perturbation-based decomposition technique developed by Szlavik [1] was used in an attempt to solve the inverse problem in EEG source localization. A set of dipole locations were forward modeled using a 4-layer sphere model of the head at uniformly distributed lead locations to form the vector basis necessary for the method. Both a two-dimensional and a pseudo-three-dimensional versions of the model were assessed with the two-dimensional model yielding decompositions with minimal error and the pseudo-three-dimensional version having unacceptable levels of error. The utility of interpolation as a method to reduce the number of data points to become overdefined was assessed as well. The approach was effective as long as the number of component functions did not exceed the number of data points and stayed relatively small (less than 77 component functions). This application of the method to a spatially variate system indicates its potential for other systems and with some tweaking to the least squares algorithm used, could be applied to multivariate systems.