According to various studies, compared with novice athletes, experts exhibit superior integration of perceptual, cognitive, and motor skills. This superior ability has been associated with the focused and efficient organization of task-related neural networks. Specifically, skilled individuals demonstrate a spatially localized or relatively lower response in brain activity, characterized as ‘neural efficiency’, when performing within their domain of expertise. Previous works also suggested that elite basketball players can predict successful free throws more rapidly and accurately based on cues from body kinematics. These traits are the result of a prolonged training of specific motor skills and focused excitability of the motor cortex during the reaction, movement planning, and execution phases. However, due to motion artifacts occurring during movement initiation and execution, the knowledge about the underlying mechanism and the connection between brain neural networks and body musculoskeletal systems is still limited. Thus, the objective of this study is to utilize electroencephalography (EEG) and motion capture systems (MoCap) to advance and expand the current understanding of the relationships between neurophysiological activities and human biomechanics as well as their effects on the success rate of the motor skills. The project focuses on fulfilling three specific aims. The first aim focused on the integration of the EEG and the MoCap systems to analyze and compare the successful and unsuccessful outcomes of basketball throws. Then, the second aim utilized Convolution Neural Networks (CNNs) as an alternative approach to predict the shot’s outcome based on the recorded EEG signals and biomechanical parameters. Lastly, the third aim identified the impact of each EEG channel and MoCap parameter on the CNN models using ablation methods. The results obtained from this study can be a practical approach in analyzing the sources that lead to better elite athletes’ performance in various sport-related tasks. Moreover, the acquired data can contribute to a deeper understanding of the vital biomechanical and neurological factors that directly affect the performance of elite athletes during successful outcomes, thus, providing vital information for the overall improvement of athletic performance and guidance for sport-specific training needs.

Whilst studies on rhythm-keeping and error-related negativity have been conducted, previous studies have given participants auditory or visual cues to indicate the rhythm they are meant to be maintaining. In this electroencephalography study, a novel experiment called the Human Metronome Task was introduced, using healthy university students as participants. The Human Metronome Task tested the participants by having them tap in synchrony with a beat, and then having the beat be removed, with the participants still being supposed to maintain the same beat with their taps, now without any auditory or visual aids. The purpose was to see if deviations in unassisted rhythm keeping are sufficient to elicit error-related negativity. When comparing different deviations of the tap-timing of each participant to their average tap-timing, no significant differences in electroencephalography amplitude were found. It was concluded that the Human Metronome Task is unable to elicit error-related negativity in participants. It seems plausible that this is due to the ambiguity of whether responses are erroneous or accurate. Thus, it seems as if it is necessary for more indications of whether a response is erroneous or not for the elicitation of error-related negativity than was present in the Human Metronome Task.

No description available.

<p>Social fears gain in prominence among higher primates, including humans, where threats associated with other conspecifics become more common. Social fearfulness is expressed on a continuum, ranging from shyness to a diagnosable psychiatric disorder. Despite the wide prevalence and considerable distress associated with social anxiety, our understanding of its neural and cognitive correlates remains in its infancy and remains an imperative for future translational research. The current dissertation examined social anxiety by utilizing multiple experimental approaches and employing a broad range of measures involving neural, cognitive, behavioural and clinical assessments.</p> <p>Chapters 2 to 4 relied on nonclinical samples of adults selected for social anxiety from a large adult population. Chapters 2 and 3 employed event-related brain potentials to index distinct aspects of perceptual and cognitive processing in tasks that manipulated novelty under socio-emotional and affectively neutral contexts. The aim was to provide a fine-grained characterization of the information processing stages that are biased by social anxiety. Chapter 4 measured reaction times in a selective attention task that independently varied the temporal and energetic aspects of affective stimulus delivery to provide convergent evidence into how affective processing is perturbed by social anxiety. Chapter 5 employed a novel method of quantifying continuous EEG to examine large-scale brain activity during rest and symptom provocation in patients diagnosed with social anxiety disorder. The aim was to examine, for the first time, whether there are treatment-related changes in a measure that putatively indexes communication across different (cortical and subcortical) neuronal systems.</p> <p>Findings suggest that social anxiety is associated with a sensitization of (bottom-up) systems reacting to social threat and that these biases appear during the early, relatively automatic stages of information processing. Some of these systems may be susceptible to evidence-based psychological treatments that are correlated with changes in brain activity detectable in EEG patterns. <br /><br /></p> / Doctor of Philosophy (PhD)

Brain-computer interfaces (BCIs) create a new form of communication and control for humans by translating brain activity directly into actions performed by a computer. This new field of research, best known for its breakthroughs in enabling fully paralyzed or locked-in patients to communicate and control simple devices, has resulted in a variety of remarkable technological developments. However, the field is still in its infancy, and facilitating control of a computer application via thought in a broader context involves a number of a challenges that have not yet been met. Advancing BCIs beyond the experimental phase continues to be a struggle. End-users have rarely been reached, except for in the case of a few highly specialized applications which require continual involvement of BCI experts. While these applications are profoundly beneficial for the patients they serve, the potential for BCIs is much broader in scope and powerful in effect. Unfortunately, the current approaches to brain-computer interfacing research have not been able to address the primary limitations in the field: the poor reliability of most BCIs and the highly variable performance across individuals. In addition to this, the modes of control available to users tend to be restrictive and unintuitive (\emph{e.g.}, imagining complex motor activities to answer ``Yes" or ``No" questions). This thesis presents a novel approach that addresses both of these limitations simultaneously. Brain-computer interfacing is currently viewed primarily as a machine learning problem, wherein the computer must learn the patterns of brain activity associated with a user's mental commands. In order to simplify this problem, researchers often restrict mental commands to those which are well characterized and easily distinguishable based on \emph{a priori} knowledge about their corresponding neural correlates. However, this approach does not fully recognize two properties of a BCI which makes it unique to other human-computer interfaces. First, individuals can vary widely with respect to the patterns of activation associated with how their brains generate similar mental activity and with respect to which kinds of mental activity have been most trained due to life experience. Thus, it is not surprising that BCIs based on predefined neural correlates perform inconsistently for different users. Second, for a BCI to perform well, the human and the computer must become a cohesive unit such that the computer can adapt as the user's brain naturally changes over time and while the user learns to make their mental commands more consistent and distinguishable given feedback from the computer. This not only implies that BCI use is a skill that must be developed, honed, and maintained in relation to the computer's algorithms, but that the human is the fundamental component of the system in a way that makes human learning just as important as machine learning. In this thesis it is proposed that, in the long term, a generalized BCI that can discover the appropriate neural correlates of individualized mental commands is preferable to the traditional approach. Generalization across mental strategies allows each individual to make better use of their own experience and cognitive abilities in order to interact with BCIs in a more free and intuitive way. It is further argued that in addition to generalization, it is necessary to develop improved training protocols respecting the potential of the user to learn to effectively modulate their own brain activity for BCI use. It is shown through a series of studies exploring generalized BCI methods, the influence of prior non-BCI training on BCI performance, and novel methods for training individuals to control their own brain activity, that this new approach based on balancing the roles of the user and the computer according to their respective capabilities is a promising avenue for advancing brain-computer interfacing towards a broader array of applications usable by the general population. / Thesis / Doctor of Philosophy (PhD)

Study of the mammalian auditory system suggests that processes once thought exclusive to cortical structures also operate subcortically. Recently, this observation has extended to the detection of acoustic change. This thesis uses methods designed for the concurrent capture of auditory-evoked potential (AEP) components attributed to different subcortical and cortical sources. Using such an approach, Chapter 2 shows that 2-month-old infants respond to infrequent changes in sound source location with neural activity implicating both subcortically- and cortically-driven mechanisms of change-detection. Chapter 3 describes the development of a new stimulation protocol and presents normative data from adult listeners showing that the morphologies of several well-known subcortical and cortical AEP components are related. Finally, Chapter 4 uses the new methods developed in Chapter 3 to demonstrate that stimulus regularity not only affects neural activity at both subcortical and cortical structures, but that the activity localized to these structures is linked. Together, the studies presented in this thesis emphasize the potential for existing technologies to study the interaction of subcortical and cortical processing in human listeners. Moreover, the results of Chapters 2 through 4 lend support to models wherein change-detection is considered a distributed, and perhaps fundamental, attribute of the auditory hierarchy. / Thesis / Doctor of Philosophy (PhD)

Previous research has established that observing another in pain activates both affective and sensorimotor cortical activity that is also present during the first-hand experience of pain. Some researchers have taken this “mirroring” response as indicative of empathic processing. However, very little work has explored the downstream behavioral effects of empathic pain observation. The aim of this dissertation is to begin to fill this gap in the literature by exploring the relationship between empathic pain observation, overt motor behaviours, and sensorimotor activity. In chapters 2-4, I provide robust evidence that observing pain inflicted on another person leads to faster reaction time responses. This effect is shown to be temporally extended (by at least 500ms after pain observation), effector-general (affecting both finger and foot responses), influenced by top-down (i.e., instructions to explicitly empathize) but not bottom-up (i.e., the perceived level of pain) factors, and is not influenced by adaptive (approach/withdraw) behaviours. In chapter 5, I show that sensorimotor activity, measured via TMS-induced Motor Evoked Potentials, increases while observing another in pain regardless whether the observer is preparing to make an action vs. passively observing the stimuli. These results run counter to the literature, and I provide several explanations for why these results were found. Lastly, in chapter 6, I show that sensorimotor activity, measured via Mu and Beta suppression, also increases while observing another in pain regardless whether the observer is preparing to make an action vs. passively observing the stimuli. Interestingly, I do not find significant correlations between sensorimotor activity during pain observation and faster reaction times after pain observation. I embed these findings in relation to the wider social neuroscience of empathy literature and discuss several limitations and challenges in empirically measuring “empathy” as a psychological construct. Overall, this dissertation furthers our understanding of empathy for pain by highlighting the behavioural consequences of pain observation and its connection (or rather, lack thereof) to sensorimotor activity during pain observation. / Thesis / Doctor of Philosophy (PhD) / Past research suggests that overlapping brain activity during the first-hand experience of pain and pain observation may be indicative of empathy. However, very little work has been done to explore how pain observation influences overt behaviours. This thesis investigates this issue by having participants complete a reaction time task while watching videos of needles stabbing a person’s hand. The findings reported in this thesis suggests that observing another in pain facilitates motor behaviours (i.e., faster reaction times); this facilitation extends 500ms after pain observation, affects both the hand and feet, is accentuated by instructing participants to explicitly empathize, and is not influenced by approach vs. withdraw movements. Brain activity in the motor system was also found to increase during pain observation. Overall, this thesis begins the discussion of how empathic pain observation influences explicit motor behaviours, and how such behaviours may be related to brain activity.

This dissertation addresses user responses to the introduction of Brain-Computer Interface technologies (BCI) for gaming and consumer applications in the early part of the 21st century. BCI technology has emerged from the contexts of interrelated medical, academic, and military research networks including an established computer and gaming industry. First, I show that the emergence and development of BCI technology are based on specific economic, socio-cultural, and material factors, and secondly, by utilizing user surveys and interviews, I argue that the success of BCI are not determined by these contextual factors but are dependent on user acceptance and interpretation. Therefore, this project contributes to user-technology studies by developing a model which illustrates the interrelations between producers, users, values, and technology and how they contribute to the acceptance, resistance, and modification in the technological development of emerging BCI technologies. This project focuses on human computer interaction researchers, independent developers, the companies producing BCI headsets, and neuro-gadget companies who are developing BCI's for users as an alternative interface for the enhancement of human performance and gaming and computer simulated experience. Moreover, BCI production and use as modes of enhancement align significantly with social practices of play which allows an expanded definition of technology to include cultural dimensions of play. / Ph. D.

Over 30 years of functional imaging studies have demonstrated that the human brain operates as a complex and interconnected system, with distinct functional networks and long-range coordination of neural activity. Yet, how our brains coordinate our behavior from moment to moment, permitting us to think, talk, and move at the same time, has been almost impossible to decode (Chapter 1). The invasive, long-term, and often multi-regional iEEG monitoring utilized for epilepsy surgery evaluation presents a valuable opportunity for studying brain-wide dynamic neural activity in behaving human subjects. In this study, we analyzed over 93 hours of iEEG recordings along with simultaneously acquired video recordings from 10 patients with drug-resistant focal epilepsy syndromes, who underwent invasive iEEG with broadly distributed bilateral depth electrodes for clinical evaluation. Initially, we explored the dynamic connectivity patterns quantified from band-limited neural activities using metrics from previous literature in a subset of subjects. These metrics can characterize long-range connectivity across brain regions and reveal variations over time. They have shown success in identifying state differences using controlled task presentations and trial-averaged data. However, we found that replicating this success with naturalistic, complex behaviors in our subjects is challenging. Although they demonstrate differences across wake and sleep states, they are less sensitive in differentiating more complicated and subtle state transitions during wakefulness. In addition, patterns identified from individual frequency bands exhibit patient-to-patient differences, making it difficult to generalize results across frequency bands and subjects. (Chapter 2). Inspired by clinical electrocortical stimulation mapping studies, which seek to identify critical brain sites for language and motor function, and the frequency gradient observed from human scalp and intracranial EEG recordings, we developed a new approach to meet the requirements for real-time analysis and frequency band selection. It is worth mentioning that detecting state transitions in naturalistic behavior requires analyzing raw EEG during individual transitions. We refer to this as "real-time analysis," to distinguish it from formal task performance and trial-averaging techniques. Rather than representing data as time-varying signals within specific frequency bands, we incorporated all frequencies (2-55 Hz) into our analysis by calculating the power spectral density (PSD) at each electrode. This analysis confirmed that the human brain’s neural activity PSD is heterogenous, exhibiting a distinct topography with bilateral symmetry, consistent with prior resting-state MEG and iEEG studies. However, investigating the variability of each region’s PSD over time (within a 2-second moving window), we discovered the tendency of individual electrode channel to switch back and forth between 2 distinct power spectral densities (PSDs, 2-55Hz) (Chapter 3). We further recognized that this ‘spectral switching’ occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that could be obscured in the analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation, hand movements, and eyes open versus closed, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. These network-behavior relationships were stable over multiple days but were altered during sleep, suggesting state-dependent plasticity of brain-wide network organization (Chapter 4). Our results provide robust evidence for the presence of multiple synchronous neuronal networks across the human brain. The real-time PSD switching dynamics of these networks provide physiologically interpretable read-outs, demonstrating the parallel engagement of multiple brain regions in a range of concurrent naturalistic behaviors (Chapter 5).

Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal processing of brain signals and how brain activity can be quantifiable through data analysis. An experiment is analyzed in this thesis to provide evidence that theta frequency band activity is associated with stress and stress is negatively correlated with concentration and problem solving, therefore hindering decision making skill. From the results of the experiment, it is seen that theta is negatively correlated to delta and beta frequency band activity, thus establishing the fact that stress affects internal focus while carrying out a task.