Adaptive Brain-Computer Interface Systems For Communication in People with Severe Neuromuscular Disabilities

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

Evaluating functional network inference using simulations of complex biological systems.

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.

Stimulus Integration and Parsing in the Primate Auditory Midbrain

Integrating information from multiple sources is a crucial function of the brain. Examples of such integration include multiple stimuli of different modalties, such as visual and auditory, multiple stimuli of the same modality, such as auditory and auditory, and integrating stimuli from the sensory organs (i.e. ears) with stimuli delivered from brain-machine interfaces.

The overall aim of this body of work is to empirically examine stimulus integration in these three domains to inform our broader understanding of how and when the brain combines information from multiple sources.

First, I examine visually-guided auditory, a problem with implications for the general problem in learning of how the brain determines what lesson to learn (and what lessons not to learn). For example, sound localization is a behavior that is partially learned with the aid of vision. This process requires correctly matching a visual location to that of a sound. This is an intrinsically circular problem when sound location is itself uncertain and the visual scene is rife with possible visual matches. Here, we develop a simple paradigm using visual guidance of sound localization to gain insight into how the brain confronts this type of circularity. We tested two competing hypotheses. 1: The brain guides sound location learning based on the synchrony or simultaneity of auditory-visual stimuli, potentially involving a Hebbian associative mechanism. 2: The brain uses a ‘guess and check’ heuristic in which visual feedback that is obtained after an eye movement to a sound alters future performance, perhaps by recruiting the brain’s reward-related circuitry. We assessed the effects of exposure to visual stimuli spatially mismatched from sounds on performance of an interleaved auditory-only saccade task. We found that when humans and monkeys were provided the visual stimulus asynchronously with the sound but as feedback to an auditory-guided saccade, they shifted their subsequent auditory-only performance toward the direction of the visual cue by 1.3-1.7 degrees, or 22-28% of the original 6 degree visual-auditory mismatch. In contrast when the visual stimulus was presented synchronously with the sound but extinguished too quickly to provide this feedback, there was little change in subsequent auditory-only performance. Our results suggest that the outcome of our own actions is vital to localizing sounds correctly. Contrary to previous expectations, visual calibration of auditory space does not appear to require visual-auditory associations based on synchrony/simultaneity.

My next line of research examines how electrical stimulation of the inferior colliculus influences perception of sounds in a nonhuman primate. The central nucleus of the inferior colliculus is the major ascending relay of auditory information before it reaches the forebrain, and thus an ideal target for understanding low-level information processing prior to the forebrain, as almost all auditory signals pass through the central nucleus of the inferior colliculus before reaching the forebrain. Thus, the inferior colliculus is the ideal structure to examine to understand the format of the inputs into the forebrain and, by extension, the processing of auditory scenes that occurs in the brainstem. Therefore, the inferior colliculus was an attractive target for understanding stimulus integration in the ascending auditory pathway.

Moreover, understanding the relationship between the auditory selectivity of neurons and their contribution to perception is critical to the design of effective auditory brain prosthetics. These prosthetics seek to mimic natural activity patterns to achieve desired perceptual outcomes. We measured the contribution of inferior colliculus (IC) sites to perception using combined recording and electrical stimulation. Monkeys performed a frequency-based discrimination task, reporting whether a probe sound was higher or lower in frequency than a reference sound. Stimulation pulses were paired with the probe sound on 50% of trials (0.5-80 µA, 100-300 Hz, n=172 IC locations in 3 rhesus monkeys). Electrical stimulation tended to bias the animals’ judgments in a fashion that was coarsely but significantly correlated with the best frequency of the stimulation site in comparison to the reference frequency employed in the task. Although there was considerable variability in the effects of stimulation (including impairments in performance and shifts in performance away from the direction predicted based on the site’s response properties), the results indicate that stimulation of the IC can evoke percepts correlated with the frequency tuning properties of the IC. Consistent with the implications of recent human studies, the main avenue for improvement for the auditory midbrain implant suggested by our findings is to increase the number and spatial extent of electrodes, to increase the size of the region that can be electrically activated and provide a greater range of evoked percepts.

My next line of research employs a frequency-tagging approach to examine the extent to which multiple sound sources are combined (or segregated) in the nonhuman primate inferior colliculus. In the single-sound case, most inferior colliculus neurons respond and entrain to sounds in a very broad region of space, and many are entirely spatially insensitive, so it is unknown how the neurons will respond to a situation with more than one sound. I use multiple AM stimuli of different frequencies, which the inferior colliculus represents using a spike timing code. This allows me to measure spike timing in the inferior colliculus to determine which sound source is responsible for neural activity in an auditory scene containing multiple sounds. Using this approach, I find that the same neurons that are tuned to broad regions of space in the single sound condition become dramatically more selective in the dual sound condition, preferentially entraining spikes to stimuli from a smaller region of space. I will examine the possibility that there may be a conceptual linkage between this finding and the finding of receptive field shifts in the visual system.

In chapter 5, I will comment on these findings more generally, compare them to existing theoretical models, and discuss what these results tell us about processing in the central nervous system in a multi-stimulus situation. My results suggest that the brain is flexible in its processing and can adapt its integration schema to fit the available cues and the demands of the task.

A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data.

Interest in structural brain connectivity has grown with the understanding that abnormal neural connections may play a role in neurologic and psychiatric diseases. Small animal connectivity mapping techniques are particularly important for identifying aberrant connectivity in disease models. Diffusion magnetic resonance imaging tractography can provide nondestructive, 3D, brain-wide connectivity maps, but has historically been limited by low spatial resolution, low signal-to-noise ratio, and the difficulty in estimating multiple fiber orientations within a single image voxel. Small animal diffusion tractography can be substantially improved through the combination of ex vivo MRI with exogenous contrast agents, advanced diffusion acquisition and reconstruction techniques, and probabilistic fiber tracking. Here, we present a comprehensive, probabilistic tractography connectome of the mouse brain at microscopic resolution, and a comparison of these data with a neuronal tracer-based connectivity data from the Allen Brain Atlas. This work serves as a reference database for future tractography studies in the mouse brain, and demonstrates the fundamental differences between tractography and neuronal tracer data.

Decision-Making in the Primate Brain

Making decisions is fundamental to everything we do, yet it can be impaired in various disorders and conditions. While research into the neural basis of decision-making has flourished in recent years, many questions remain about how decisions are instantiated in the brain. Here we explored how primates make abstract decisions and decisions in social contexts, as well as one way to non-invasively modulate the brain circuits underlying decision-making. We used rhesus macaques as our model organism. First we probed numerical decision-making, a form of abstract decision-making. We demonstrated that monkeys are able to compare discrete ratios, choosing an array with a greater ratio of positive to negative stimuli, even when this array does not have a greater absolute number of positive stimuli. Monkeys’ performance in this task adhered to Weber’s law, indicating that monkeys—like humans—treat proportions as analog magnitudes. Next we showed that monkeys’ ordinal decisions are influenced by spatial associations; when trained to select the fourth stimulus from the bottom in a vertical array, they subsequently selected the fourth stimulus from the left—and not from the right—in a horizontal array. In other words, they begin enumerating from one side of space and not the other, mirroring the human tendency to associate numbers with space. These and other studies confirmed that monkeys’ numerical decision-making follows similar patterns to that of humans, making them a good model for investigations of the neurobiological basis of numerical decision-making.

We sought to develop a system for exploring the neuronal basis of the cognitive and behavioral effects observed following transcranial magnetic stimulation, a relatively new, non-invasive method of brain stimulation that may be used to treat clinical disorders. We completed a set of pilot studies applying offline low-frequency repetitive transcranial magnetic stimulation to the macaque posterior parietal cortex, which has been implicated in numerical processing, while subjects performed a numerical comparison and control color comparison task, and while electrophysiological activity was recorded from the stimulated region of cortex. We found tentative evidence in one paradigm that stimulation did selectively impair performance in the number task, causally implicating the posterior parietal cortex in numerical decisions. In another paradigm, however, we manipulated the subject’s reaching behavior but not her number or color comparison performance. We also found that stimulation produced variable changes in neuronal firing and local field potentials. Together these findings lay the groundwork for detailed investigations into how different parameters of transcranial magnetic stimulation can interact with cortical architecture to produce various cognitive and behavioral changes.

Finally, we explored how monkeys decide how to behave in competitive social interactions. In a zero-sum computer game in which two monkeys played as a shooter or a goalie during a hockey-like “penalty shot” scenario, we found that shooters developed complex movement trajectories so as to conceal their intentions from the goalies. Additionally, we found that neurons in the dorsolateral and dorsomedial prefrontal cortex played a role in generating this “deceptive” behavior. We conclude that these regions of prefrontal cortex form part of a circuit that guides decisions to make an individual less predictable to an opponent.