ECoG correlates of visuomotor transformation, neural plasticity, and application to a force-based brain computer interface

Electrocorticography: ECoG) has gained increased notoriety over the past decade as a possible recording modality for Brain-Computer Interface: BCI) applications that offers a balance of minimal invasiveness to the patient in addition to robust spectral information over time. More recently, the scale of ECoG devices has begun to shrink to the order of micrometer diameter contacts and millimeter spacings with the intent of extracting more independent signals for BCI control within less cortical real-estate. However, most control signals to date, whether within the field of ECoG or any of the more seasoned recording techniques, have translated their control signals to kinematic control parameters: i.e. position or velocity of an object) which may not be practical for certain BCI applications such as functional neuromuscular stimulation: FNS). Thus, the purpose of this dissertation was to present a novel application of ECoG signals to a force-based control algorithm and address its feasibility for such a BCI system. Micro-ECoG arrays constructed from thin-film polyimide were implanted epidurally over areas spanning premotor, primary motor, and parietal cortical areas of two monkeys: three hemispheres, three arrays). Monkeys first learned to perform a classic center-out task using a brain signal-to-velocity mapping for control of a computer cursor. The BCI algorithm utilized day-to-day adaptation of the decoding model to match the task intention of the monkeys with no need for pre-screeening of movement-related ECoG signals. Using this strategy, subjects showed notable 2-D task profiency and increased task-related modulation of ECoG features within five training sessions. After fixing the last model trained for velocity control of the cursor, the monkeys then utilized this decoding model to control the acceleration of the cursor in the same center-out task. Cursor movement profiles under this mapping paralleled those demonstrated using velocity control, and neural control signal profiles revealed the monkeys actively accelerated and decelerated the cursor within a limited time window: 1-1.5 seconds). The fixed BCI decoding model was recast once again to control the force on a virtual cursor in a novel mass-grab task. This task required targets not only to reach to peripheral targets but also account for an additional virtual mass as they grabbed each target and moved it to a second target location in the presence of the external force of gravity. Examination of the ensemble control signals showed neural adaptation to variations in the perceived mass of the target as well as the presence or absence of gravity. Finally, short rest periods were interleaved within blocks of each task type to elucidate differences between active BCI intention and rest. Using a post-hoc state-decoder model, periods of active BCI task control could be distinguished from periods of rest with a very high degree of accuracy: ~99%). Taken together, the results from these experiments present a first step toward the design of a dynamics-based BCI system suitable for FNS applications as well as a framework for implementation of an asyncrhonous ECoG BCI

Detection of cognitive process during differentiating task with EEG

Human beings notice differences in their environment and make explicit and implicit differentiations between events and activities. These are very intuitive activities that humans perform on regular basis, studying the cognitive processes behind them will help gain more knowledge on mental tasks, opening up possibilities for better brain-computer interaction (BCI) mechanisms in the future. In this study, these cognitive processes were measured using a non-invasive brain measuring method, Electroencephalography (EEG) as well as a behavioural measure (eye gaze path). Separate experiments were carried out to examine both scenarios of explicit (voluntary) and implicit (involuntary) differentiations. Using a combination of machine learning tools for signal analysis, a high correct rate of classification results in combination with significant statistical figures achieved has shown that one can effectively identify those mental tasks from studying the brainwave and eye gaze measurements. - provided by Candidate

Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair

A brain-computer interface (BCI) is a technology allowing patients with severe motor dysfunctions to use their electroencephalographic signals to create a communication channel to control devices. The objective of this paper is to study the feasibility of continuous and switch control modes for a brain-controlled wheelchair (BCW) using sensorimotor rhythms (SMR) modulated through a right-hand motor imagery task. Previous studies, which used a continuous navigation control with SMR, have reported the difficulty of maintaining the motor imagery task for a long time, especially for the forward command. The switch control has been presented as a proposal that may help to solve this issue since this task is only used temporary for either disabling or enabling the movement. Regarding the methodology, 10 of 15 able-bodied users, who had overcome the criterion of 30% error rate in the calibration phase, controlled the BCW using both paradigms. The navigation tasks consisted of a straight path divided in five sections: in three of them the users had to move forward, and in the other two the users had to maintain their position. To assess user performance in the device management, a usability approach was adopted, measuring the factors of effectiveness, efficiency, and satisfaction. Then, variables related to the time employed and commands selected by the user or parameters related to the confusion matrix were applied. In addition, the scores in NASA-TLX and two ad hoc questionnaires were considered to discuss the user experience controlling the wheelchair. Despite the results showed that the best system for a specific user relies on his/her abilities and preferences, the switch control mode obtained better accuracy (0.59 ± 0.17 for continuous and 0.72 ± 0.05 for switch). Furthermore, the switch paradigm can be recommended for the advance sections as with it users could complete the advance sections in less time (42.2 ± 28.7 s for continuous and 15.47 ± 3.43 s for switch), while the continuous mode seems to be better at keeping the wheelchair stopped (42.45 ± 16.01 s for continuous and 24.35 ± 10.94 s for switch)

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

Dispersal and compensatory population dynamics in a harvested mammal

Populations of wild birds and mammals are often harvested for sport, subsistence or commerce. Sustainable exploitation is an important aspect of environmental management and is critical to human wellbeing. However, our inability to sustainably harvest even well studied populations is often due to poor demographic data and a lack of understanding of critical biological processes. A key component of sustainable harvesting is the density-dependant feedback between vital rates (births, deaths, immigration and emigration) and population density, as this mechanism provides populations with the capacity to compensate for harvest mortality. The aims of this thesis are to investigate density-dependence, compensation and sustainable harvesting in a traditional Scottish quarry species, the mountain hare, Lepus timidus scoticus, using a combination of replicated field experiments, cross-sectional studies and modelling. The mountain hare has been traditionally harvested for centuries and continues to be an important source of revenue for some land owners. Over the past decade the motive for killing hares has switched from predominantly sport shooting, to culling to reduce ticks and the tick-borne disease Louping-ill virus (LIV). Louping-ill virus causes high mortality, and can supress harvest, in economically important red grouse, Lagopus lagopus scoticus. Sustainable management of the mountain hare is further complicated by cyclic, or unstable, population dynamics shown throughout their circumpolar distribution. The thesis begins with a review of the evidence that culling mountain hares can lead to a reduction in ticks and LIV prevalence, resulting in increased red grouse harvest. The empirical studies follow with the aim of providing evidence for compensatory survival and dispersal, and density-dependent reproduction. Two field study approaches were adopted; the first was a live-capture and radio-telemetry study which compared birth dates, survival probabilities and dispersal distances and rates of different age and sex classes between two populations under different harvesting regimes (harvested and non-harvested). The second empirical study used a cross-sectional design whereby population density of ten independent populations were estimated using Distance sampling, which preceded hare harvesting. Tissue samples of killed hares were collected, enabling the effects of population density on female fecundity and juvenile recruitment to be assessed. Finally, an age-structure, female-only matrix population model was parameterised using data from the empirical studies. Sustainable harvest rates were determined as the maximum harvest that produced a positive population growth rate and an extinction probability of less than 5%. By manipulating age-specific harvest and initial population size, the effects of age-biased harvest and changing population size on population viability could be investigated. Results of the cross-sectional study revealed evidence for negative density-dependent juvenile recruitment. However, this finding was not translated into compensatory survival in either juveniles or adults, although birth date of leverets was significantly later in the harvested population. Overall, dispersal distances and rates were low. Dispersal distance was greater in the harvested population, although when distance was scaled to account for differences in observation time, to give a dispersal rate, no difference was found. Projections from the matrix model suggest that 40% annual harvest rate is sustainable, although extinction probability increases with decreasing population size and as harvesting becomes increasingly yearling biased. Overall, no evidence for compensatory survival or dispersal was found, although density-dependent juvenile recruitment was identified and may be important in population persistence under exploitation. Survival and timing of breeding may be influenced by behavioural and physiological effects of harvesting, or environmental variation, which may have implications for unstable dynamics of mountain hare. I identify and discuss key areas for future research aimed at increasing our understanding of the effects of harvesting on mountain hare population dynamics and demography. In conclusion, we found little evidence that culling mountain hares can increase red grouse harvest, and therefore, cannot justify culling mountain hares for tick and LIV control. The low dispersal rates and distances, combined with limited evidence for compensatory mechanisms, imply that local blanket culls may succeed in reducing hare numbers with unknown implications for mountain hare population persistence and the wider biodiversity of the Scottish uplands

Program: Graduate Research Achievement Day 2017

Full program for 2017 Graduate Research Achievement Day.https://digitalcommons.odu.edu/graduateschool_achievementday2017-18_programs/1001/thumbnail.jp

Endogenicity and awareness in voluntary action

The idea that we can trigger and control our actions at will is central to our experience as agents. Here, we investigated different cognitive mechanisms involved in voluntary action control. In the first part of the thesis, we investigated the relationship between motor preparation and awareness of intention. To do so, we used spontaneous action paradigms and combined them with novel random and real-time EEG probing techniques. We investigated two main questions. First, do people know that they are about to do something before they do it? Second, to what extent are delayed intention judgements informed by prospective motor preparation rather than retrospective reconstruction? Our findings suggest that people have some feeling of motor intention before acting and can use it to voluntarily control action initiation in real-time. However, their recall-based intention judgements are strongly influenced by overt events happening after the time of probing. Because most daily-life voluntary actions occur in interaction with the environment, in the second part of the thesis we embedded self-paced actions in a decision-making context. We investigated two ways in which endogenous factors can contribute to action selection. First, as a symmetry-breaking mechanism in contexts of external ambiguity. Second, by top-down modulating decision-making processes. We identified the neural correlates of an internal decision-variable that tracks perceptual decisions and also indexes dynamic changes in endogenous goals. Further, we show that the readiness potential can be found not only preceding spontaneous actions, but also in contexts where actions are informed by evidence but preserve a self-paced nature. In sum, this thesis provides new insights into the cognitive mechanisms underlying conscious experience of intention and provides new tools to investigate voluntary control over action initiation and selection processes