Human Ipsilateral Motor Physiology and Neuroprosthetic Applications in Chronic Stroke

Improving the recovery of lost motor function in hemiplegic chronic stroke survivors is a critical need to improve the lives of these patients. Over the last several decades, neuroprosthetic systems have emerged as novel tools with the potential to restore function in a variety of patient populations. While traditional neuroprosthetics have focused on using neural activity contralateral to a moving limb for device control, an alternative control signal may be necessary to develop brain-computer interface (BCI) systems in stroke survivors that suffer damage to the cortical hemisphere contralateral to the affected limb. While movement-related neural activity also occurs in the hemisphere ipsilateral to a moving limb, it is uncertain if these signals can be used within BCI systems. This dissertation examines the motor activity ipsilateral to a moving limb and the potential use of these signals for neuroprosthetic applications in chronic stroke survivors. Patients performed three-dimensional (3D) reaching movements with the arm ipsilateral to an electrocorticography (ECoG) array in order to assess the extent of kinematic information that can be decoded from the cortex ipsilateral to a moving limb. Additionally, patients performed the same task with the arm contralateral to the same ECoG arrays, allowing us to compare the neural representations of contralateral and ipsilateral limb movements. While spectral power changes related to ipsilateral arm movements begin later and are lower in amplitude than power changes related to contralateral arm movements, 3D kinematics from both contralateral and ipsilateral arm trajectories can be decoded with similar accuracies. The ability to decode movement kinematics from the ipsilateral cortical hemisphere demonstrates the potential to use these signals within BCI applications for controlling multiple degrees of freedom. Next we examined the relationship between electrode invasiveness and signal quality. The ability to decode movement kinematics from neural activity was significantly decreased in simulated electroencephalography (EEG) signals relative to ECoG signals, indicating that invasive signals would be necessary to implement BCI systems with multiple degrees of freedom. For ECoG signals, the human dura also causes a significant decrease in signal quality when electrodes with small spatial sizes are used. This tradeoff between signal quality and electrode invasiveness should therefore be taken into account when designing ECoG BCI systems. Finally, chronic stroke survivors used activity associated with affected hand motor intentions, recorded from their unaffected hemisphere using EEG, to control simple BCI systems. This demonstrates that motor signals from the ipsilateral hemisphere are viable for BCI applications, not only in motor-intact patients, but also in chronic stroke survivors. Taken together, these experiments provide initial demonstrations that it is possible to develop BCI systems using the unaffected hemisphere in stroke survivors with multiple degrees of freedom. Further development of these BCI systems may eventually lead to improving function for a significant population of patients

Designing a Clinically Viable Brain Computer Interface for the Control of Neuroprosthetics

Currently no brain computer interfaces exist that can control the individual fingers of a hand prosthesis and is suitable for permanent implantation in and individual with a single limb amputation. Within this thesis a design for a novel minimally invasive brain computer interface system is proposed that would be relatively low risk, allow for control of a prosthesis using existing cortical structures and be suitable for patients with loss of a single limb. The early stage development and proof of concept work has been done taking into account relevant regulatory requirements, so that a finalised version of the design would be suitable for regulatory certification. This novel design is found to be worth pursuing and may in turn open up new research opportunities

Near-Infrared Spectroscopy for Brain Computer Interfacing

A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user and may even lead to abandonment of the device. A more suitable and usable interface is needed to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. This work implements a novel approach to BCI design, using noninvasive near-infrared spectroscopic (NIRS) techniques to develop a userfriendly optical BCI. NIRS is a practical non-invasive optical technique that can detect characteristic haemodynamic responses relating to neural activity. This thesis describes the use of NIRS to develop an accessible BCI system requiring very little user training. In harnessing the optical signal for BCI control an assessment of NIRS signal characteristics is carried out and detectable physiological effects are identified for BCI development. The investigations into various mental tasks for controlling the BCI show that motor imagery functions can be detected using NIRS. The optical BCI (OBCI) system operates in realtime characterising the occurrence of motor imagery functions, allowing users to control a switch - a “Mindswitch”. This work demonstrates the great potential of optical imaging methods for BCI development and brings to light an innovative approach to this field of research

The brain as image processor and generator:towards function-restoring brain-computer-interfaces

As neuroscientists are slowly unraveling the mysteries of the brain, neurotechnology like brain-computer-interfaces (BCIs) might become a new standard for medical applications in those with brain injuries. BCIs allow for direct communication between the brain and a device, and could potentially restore links that are broken due to brain damage. In addition, a better understanding of the human mind and its mechanisms could greatly boost the success of these devices. This dissertation features (high-field) functional magnetic resonance imaging (fMRI) to study human cognitive functioning, as fMRI allows for studying the brain of living humans in great spatial detail. Firstly, the dissertation describes how well brain regions that are important for visual perception can be located between individuals. Some of these regions are in part responsible for recognizing objects like faces, bodies, places and motion. Secondly, differences in functional organization of the brain were explored between individuals by simulating the placement of a visual cortical prosthesis. Such a prosthesis can bypass the (broken) connections between the eye and brain in blind people, and potentially restore a rudimentary form of vision. Finally, new techniques were presented that show that visual perception and mental imagery are closely related, and allow for reading letter shapes directly from the mind. Together, this dissertation adds new foundations for the development of neurotechnological applications

Development and Investigation of Sparse Co-Adaptive Algorithms in ECoG based Closed-Loop Brain Computer Interface

Electrocorticography (ECoG) has gained a lot of momentum and has become a serious contender as a recording modality for the implementation of Brain-Computer Interface (BCI) systems in the last few years. ECoG signals provide the right balance between minimal invasiveness and robust spectral information to accomplish a BCI task. However, all the BCI studies until now have used signals recorded from a large number of implanted electrodes and a larger number of spectral features. The recording and processing of these signals uses a lot of electrical power and thus hinders its use outside the research setting. To translate this research to the clinic as a chronic recording modality for neural prosthesis, minimizing the number of features and thus, the power consumption to record and process them, is of prime importance. This thesis develops and investigates two different techniques to minimize the feature space required to obtain a robust BCI control in a virtual environment setting. ECoG electrodes embedded in thin-film polyimide or Silastic were implanted in the epidural space over pre-motor, primary motor and parietal cortical areas in non-human primates. Subjects tested this thesis had had their electrode arrays implanted at least 1-2 years before the beginning of these experiments. Monkeys were trained to perform a classic 2D center out task using the recorded signals and one of two new BCI decoding algorithms developed in this thesis. Both the algorithms used for BCI control updated the decoding model using data from the previous trials. The parameters of the decoding algorithms were varied every 1-2 weeks to gradually reduce the number of features being used for control. A robust BCI control was obtained using only 30-40% of the available feature set. Post hoc analysis of the reduced feature set revealed a significant presence of mid-gamma (75-115Hz) band followed by the beta band (15-30 Hz). A novel, 1D Up-Down BCI task was used to study the modulation frequency of these two bands and the differences between them. It was observed that though subjects gradually increased the frequency of modulation in both the bands over a few weeks, they were able to modulate the mid-gamma band at a faster rate. Finally, as a proof concept, two previously trained subjects were used to perform the 2D center-out task with features recorded from only 4 ECoG electrodes. The laboratory recording system and a low power recording system were used in different sessions of experiments, and a robust control was obtained in both the cases. The overall observations and results of these studies provide with a strong basis for ECoG as a low power recording modality that can be chronically used for neural prosthesis

Reading Your Own Mind: Dynamic Visualization of Real-Time Neural Signals

Brain Computer Interfaces: BCI) systems which allow humans to control external devices directly from brain activity, are becoming increasingly popular due to dramatic advances in the ability to both capture and interpret brain signals. Further advancing BCI systems is a compelling goal both because of the neurophysiology insights gained from deriving a control signal from brain activity and because of the potential for direct brain control of external devices in applications such as brain injury recovery, human prosthetics, and robotics. The dynamic and adaptive nature of the brain makes it difficult to create classifiers or control systems that will remain effective over time. However it is precisely these qualities that offer the potential to use feedback to build on simple features and create complex control features that are robust over time. This dissertation presents work that addresses these opportunities for the specific case of Electrocorticography: ECoG) recordings from clinical epilepsy patients. First, queued patient tasks were used to explore the predictive nature of both local and global features of the ECoG signal. Second, an algorithm was developed and tested for estimating the most informative features from naive observations of ECoG signal. Third, a software system was built and tested that facilitates real-time visualizations of ECoG signal patients and allows ECoG epilepsy patients to engage in an interactive BCI control feature screening process

Evaluation and Advancement of Electrocorticographic Brain-Machine Interfaces for Individuals with Upper-Limb Paralysis

Brain-machine interface (BMI) technology aims to provide individuals with movement paralysis a natural and intuitive means for the restoration of function. Electrocorticography (ECoG), in which disc electrodes are placed on either the surface of the dura or the cortex to record field potential activity, has been proposed as a viable neural recording modality for BMI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previous demonstrations of BMI control using ECoG have consisted of short-term periods of control by able-bodied subjects utilizing basic processing and decoding techniques. This dissertation presents work seeking to advance the current state of ECoG BMIs through an assessment of the ability of individuals with movement paralysis to control an ECoG BMI, an investigation into adaptation during BMI skill acquisition, an evaluation of chronic implantation of an ECoG electrode grid, and improved extraction of BMI command signals from ECoG recordings. Two individuals with upper-limb paralysis were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for up to 30 days, with both subjects found to be capable of voluntarily modulating their cortical activity to control movement of a computer cursor with up to three degrees of freedom. Analysis of control signal angular error and the tuning characteristics of ECoG spectral features during the acquisition of brain control revealed that both decoder calibration and fixed-decoder training could facilitate performance improvements. In addition, to better understand the capability of ECoG to provide robust, long-term recordings, work was conducted assessing the effects of chronic implantation of an ECoG electrode grid in a non-human primate, demonstrating that movement-related modulation could be recorded from electrode nearly two years post-implantation despite the presence of substantial fibrotic encapsulation. Finally, it was found that the extraction of command signals from ECoG recordings could be improved through the use of a decoding method incorporating weight-space priors accounting for the expected correlation structure of electrical field potentials. Combined, this work both demonstrates the feasibility of ECoG-based BMI systems as well as addresses some of key challenges that must be overcome before such systems are translated to the clinical realm

Investigating the effects of neuromodulatory training on autistic traits: a multi-methods psychophysiological study.

Autism spectrum disorder (ASD) is characterized by noticeable difficulties with social interaction and communication. Building on past research in this area and with the aim of improving methodological perspectives, a multi method approach to the study of ASD, mirror neurons and neurofeedback was taken. This thesis is made up of three main experiments: 1) A descriptive study of the resting state electroencephalography (EEG) across the spectrum of autistic traits in neurotypical individuals, 2) A comparison of 3 EEG protocols on MNs activation (mu suppression) and its difference according to self-reported traits of autism in neurotypical individuals, and 3) Neurofeedback training (NFT) on individuals with high autistic traits. In chapters 3 and 4 we employed simultaneous monitoring of physiological data. For chapter 3 EEG and eye-tracking was used, In the case of chapter 4, EEG and eye-tracking as well functional near infrared spectroscopy (fNIRS). Overall the findings revealed differences in mu rhythm reactivity associated to AQ traits. In chapter 2, the rEEG showed that individuals with high AQ scores showed less activation of frontal and fronto-central regions combined with higher levels of complexity in fronto-temporal, temporal, parietal and parieto-occipital areas. In chapter 3, EEG protocols that elicited Mu reactivity in individuals with different AQ traits suggested that as the AQ traits become more pronounced in neurotypical population, the event-related desynchronization (ERD) in low alpha declines. Chapter 3 was also the basis for the choice of pre/post assessment for chapter 4. In chapter 4 the multi-method physiological approach provided parallel physiological evidence for the effects of NFT in sensorimotor reactivity, namely, an increase in ERD in high alpha, higher levels of oxygenated haemoglobin and changes to the amplitude and frequency in the microstructure of mu for participants who underwent active training as opposed to a sham group

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician