Collaborative Brain-Computer Interfaces in Rapid Image Presentation and Motion Pictures

The last few years have seen an increase in brain-computer interface (BCI) research for the able-bodied population. One of these new branches involves collaborative BCIs (cBCIs), in which information from several users is combined to improve the performance of a BCI system. This thesis is focused on cBCIs with the aim of increasing understanding of how they can be used to improve performance of single-user BCIs based on event-related potentials (ERPs). The objectives are: (1) to study and compare different methods of creating groups using exclusively electroencephalography (EEG) signals, (2) to develop a theoretical model to establish where the highest gains may be expected from creating groups, and (3) to analyse the information that can be extracted by merging signals from multiple users. For this, two scenarios involving real-world stimuli (images presented at high rates and movies) were studied. The first scenario consisted of a visual search task in which images were presented at high frequencies. Three modes of combining EEG recordings from different users were tested to improve the detection of different ERPs, namely the P300 (associated with the presence of events of interest) and the N2pc (associated with shifts of attention). We showed that the detection and localisation of targets can improve significantly when information from multiple viewers is combined. In the second scenario, feature movies were introduced to study variations in ERPs in response to cuts through cBCI techniques. A distinct, previously unreported, ERP appears in relation to such cuts, the amplitude of which is not modulated by visual effects such as the low-level properties of the frames surrounding the discontinuity. However, significant variations that depended on the movie were found. We hypothesise that these techniques can be used to build on the attentional theory of cinematic continuity by providing an extra source of information: the brain

INDIVIDUALIZED COGNITIVE DECLINE AND THE IMPACT OF GUT MICROBIOME COMPOSITION.

The U.S. population is aging at its greatest rate in history. An older average population will increase the number of age-related cognitive issues. Elucidation of factors that contribute to decline with age and methods to prevent or decrease the incidence of cognitive dysfunction in the aging population is vital to offset the impact of the age shift. Validation of tests to identify and predict decline is the first step, but must be paired with an increased understanding of the inter- and intra-individual differences that influence cognitive decline. One difference, gut microbiome diversity, changes within the person across their lifespan and varies among individuals. An individual’s gut microflora can significantly influence gut-brain communication, brain function, and behavior. The study was focused on identification and prediction of cognitive decline using CANTAB and visual ERP as well as exploring the relation between gut-microbiome diversity and cognitive performance. Participants underwent tests to evaluate cognitive decline over time: the MoCA, a CANTAB battery for behavioral cognitive assessment, and an electrophysiological evaluation via a passive oddball paradigm and an active detection task. The role of microbiome diversity in cognitive decline was investigated, ERP measures were validated against CANTAB measures, the predictive relation between MoCA and future cognitive outcomes were characterized, and the utility of ERP PCA factors and CANTAB outcomes to predict future ERP and CANTAB performance were shown. Three CANTAB measures (RTI, SWM, and RVP) were independently confirmed to significantly relate to selected ERP measures in both the active detection and the passive oddball tasks. Baseline MoCA score and change in MoCA score significantly predicted outcomes in the CANTAB battery and ERP tasks at follow-up. The study also included the design and implementation of novel methodology with two-step temporospatial PCA to successfully predict future performance on ERP with baseline performance on the same task, which, to this author’s knowledge, is the first known use of this method for this purpose. Finally, significant relations between gut-microbiome diversity and healthy cognitive function were revealed, where lower microbial diversity significantly relates to poorer cognitive performance on both behavioral (CANTAB) and electrophysiological (ERP) measures.Doctor of Philosoph

Learning to detect an oddball target with observations from an exponential family

The problem of detecting an odd arm from a set of K arms of a multi-armed bandit, with fixed confidence, is studied in a sequential decision-making scenario. Each arm's signal follows a distribution from a vector exponential family. All arms have the same parameters except the odd arm. The actual parameters of the odd and non-odd arms are unknown to the decision maker. Further, the decision maker incurs a cost for switching from one arm to another. This is a sequential decision making problem where the decision maker gets only a limited view of the true state of nature at each stage, but can control his view by choosing the arm to observe at each stage. Of interest are policies that satisfy a given constraint on the probability of false detection. An information-theoretic lower bound on the total cost (expected time for a reliable decision plus total switching cost) is first identified, and a variation on a sequential policy based on the generalised likelihood ratio statistic is then studied. Thanks to the vector exponential family assumption, the signal processing in this policy at each stage turns out to be very simple, in that the associated conjugate prior enables easy updates of the posterior distribution of the model parameters. The policy, with a suitable threshold, is shown to satisfy the given constraint on the probability of false detection. Further, the proposed policy is asymptotically optimal in terms of the total cost among all policies that satisfy the constraint on the probability of false detection

Visual processing in the human brain: Investigating deviance detection from a predictive coding perspective

According to predictive coding, the brain gives extra processing to unpredicted events that disrupt anticipated patterns. To adapt to these events, the brain continually extracts statistical regularities about sensory input from past input. When something unpredicted occurs, it produces an error. In vision, this can be shown by the visual mismatch negativity (vMMN) in event-related potentials (ERPs). The vMMN reaches its maximum amplitude between 150 and 300 ms after the onset of an irregular, deviant event in a sequence of otherwise regular, standard events and it is usually measured from areas on the scalp closest to the visual cortices (e.g., parieto-occipital areas). Attention toward a deviant is not necessary to generate the vMMN, suggesting that regularities and irregularities are pre-attentively encoded and detected, respectively. Although vMMN research continues to grow, there are still unanswered questions about it. This thesis focuses on clarifying some of these issues, asking whether the type or size of the difference between predicted and unpredicted visual input (i.e., the magnitude of deviance) or visual field in which deviance occurs can affect the vMMN. To remedy this, I manipulated these facets across four studies. My thesis was that local aspects of change detection, such as the magnitude of deviance, affect the brain’s error response to unpredicted input, evidenced by the vMMN. A conclusion regarding the effect of magnitude of deviance, the type of change, or visual field on the vMMN was not possible given that (1) ERPs to rule-based deviants and standards did not differ where participants found it difficult to detect irregularities in visual input, and (2) changes in basic properties of well-controlled visual stimuli do not evoke the vMMN. Subsequently, my thesis became that isolated changes in basic properties of visual input do not evoke the vMMN, perhaps because these changes are detected and resolved prior to the vMMN. Instead, this thesis provides evidence for an earlier deviant-related positivity for changes in low-level features of visual input. This is the first report of a possible pre-vMMN positive prediction error and represents a significant and original contribution to the wider field

Auditory distraction during visuomotor steering

Auditory distraction, the involuntary processing of unexpected sounds, allows us to become aware of changes in our environment that otherwise might go unnoticed. For example, while being focused on the road ahead, the sound of a car horn might warn us from an approaching car that we would have neglected, without auditory distraction. It is assumed that distraction occurs when an event violates our expectations about our auditory environment. For example, in auditory oddball tasks, sounds with a lower probability of occurrence are less expected and, thus, are reliably shown to be processed preferentially, reflected in increased measured brain potentials (i.e. eventrelated potentials (ERPs)), relative to expected sounds. However, besides the probability of occurrence, it was recently suggested that also the local short-term context in which an event occurs, as well as expectations that are based on our long-term memory content, influence our expectations and thus define auditory distraction. In the first part of the current dissertation, I provide evidence to support this assumption. Both, the physical difference of an unexpected event from its short-term context as well as its difference from long-term memory expectation were shown to result in increased processing of the eliciting event, as reflected in enhanced brain potentials. The increased processing of an unexpected auditory event also increases its demand for attentional resources and, thus, can decrease the performance in simultaneously performed tasks. It is, however, still under debate whether auditory distraction places a demand on general resources that are shared between sensory modalities or whether this demand is specific to the auditory modality. In the current dissertation, I argue that both is possible. Events that are distracting, due to their difference from their short-term context, increased the demand for general attentional resources that are shared between the auditory modality and a visually presented visuomotor control task. Events that are distracting because they differ from our long-term memory expectations increase the demand for modalityspecific attentional resources. But attentional resources are not only involuntarily attracted by unexpected auditory events. It is also possible to voluntarily attend to relevant events or tasks. While most research is devoted to study either voluntary or involuntary attentional processing, recent evidence suggested that both processes might interact. Indeed, in the second part of my dissertation, I show that increased demands, in a voluntarily performed visuomotor control task, can decrease the involuntary auditory distraction. More specifically, this is only the case for such demands which are known to increase the demand for ”perceptualcentral” resources. Furthermore, I show that a decrease of auditory distraction can not only result from high task demands, but also occurs in cases in which the auditory modality is perceived as being irrelevant