Estimating Risk for Future Intracranial, Fully Implanted, Modular Neuroprosthetic Systems: A Systematic Review of Hardware Complications in Clinical Deep Brain Stimulation and Experimental Human Intracortical Arrays

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155940/1/ner13069.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155940/2/ner13069_am.pd

Feasibility of Using the Utah Array for Long-Term Fully Implantable Neuroprosthesis Systems

Damage to the spinal cord can disrupt the pathway of signals sent between the brain and the body and may result in partial or complete loss of both motor and sensory functions. The loss of these functions can have devastating implications on the quality of one’s life, interfering with activities of daily living related to walking, bladder and bowel control, trunk stability, and arm and hand function. Current approaches used to help improve and restore mobility require residual movement to control, which can be unintuitive and inoperative by individuals with higher level cervical injuries. In order to develop technology used by individuals of all levels of injury, it is necessary to generate control signals directly from the brain. This thesis is intended to address the clinical limitations of implantable neural recording systems, and thus lay the foundation for the development of a design and safety profile for a fully implantable intracortical system for motor restoration. We first present the design and testing of a 96-channel neural recording device used to mate with an existing functional electrical stimulation (FES) system in order to facilitate brain-controlled FES. By extracting signal power within a narrow frequency bandwidth and reducing overhead processor operations, a 25% power reduction is achieved. This establishes the feasibility for an implantable system and enables the integration of the neural recording device with implantable FES system. The specifications of this platform can be used as a guide to develop further application specific modules and dramatically accelerate the overall process to a clinically viable system. With a functional device, the next step is to move towards a clinical trial. Here we investigate the potential safety risks of future modular, implantable neuroprosthetic systems. A systematic review of 240 articles was used to identify and quantitatively summarize the hardware-related complications of the most established intracranial clinical system, deep brain stimulation, and the most widespread experimental human intracranial system, the NeuroPort, including the Utah microelectrode array. The safety and longevity data collected here will be used to better inform future device and clinical trial design and satisfy regulatory requirements. The stability and longevity of the Utah array are critical factors for determining whether the clinical benefit outweighs the risk for potential users. We investigate the biological adverse response to the insertion of the Utah array in a rhesus macaque. We examined the density of neurons around the shanks of the array in comparison to control brain. Non-human primate animal models allow us to further examine the effects of the implantation of the Utah array on neural tissue, which cannot be done with humans. Information gained through this will continue to increase the pool of safety data for the Utah array and emerging intracranial devices. Overall, we developed a neural recording device to be used for brain-controlled FES and examined the potential safety concerns reported in the human literature and experimentally using non-human primates. These results represent significant progress towards a clinically-viable system for motor restoration in people suffering from spinal cord injury.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149940/1/ajbull_1.pd

The potential of error-related potentials. Analysis and decoding for control, neuro-rehabilitation and motor substitution

Las interfaces cerebro-máquina (BMIs, por sus siglas en inglés) permiten la decodificación de patrones de activación neuronal del cerebro de los usuarios para proporcionar a personas con movilidad severamente limitada, ya sea debido a un accidente o a una enfermedad neurodegenerativa, una forma de establecer una conexión directa entre su cerebro y un dispositivo. En este sentido, las BMIs basadas en técnicas no invasivas, como el electroencefalograma (EEG) han ofrecido a estos usuarios nuevas oportunidades para recuperar el control sobre las actividades de su vida diaria que de otro modo no podrían realizar, especialmente en las áreas de comunicación y control de su entorno.En los últimos años, la tecnología está avanzando a grandes pasos y con ella la complejidad de dispositivos ha incrementado significativamente, ampliando el número de posibilidades para controlar sofisticados dispositivos robóticos, prótesis con numerosos grados de libertad o incluso para la aplicación de complejos patrones de estimulación eléctrica en las propias extremidades paralizadas de un usuario, que le permitan ejecutar movimientos precisos. Sin embargo, la cantidad de información que se puede transmitir entre el cerebro y estos dispositivos sigue siendo muy limitada, tanto por el número como por la velocidad a la que se pueden decodificar los comandos neuronales. Por lo tanto, depender únicamente de las señales neuronales no garantiza un control óptimo y preciso.Para poder sacar el máximo partido de estas tecnologías, el campo de las BMIs adoptó el conocido enfoque de “control-compartido". Esta estrategia de control pretende crear un sistema de cooperación entre el usuario y un dispositivo inteligente, liberando al usuario de las tareas más pesadas requeridas para ejecutar la tarea sin llegar a perder la sensación de estar en control. De esta manera, los usuarios solo necesitan centrar su atención en los comandos de alto nivel (por ejemplo, elegir un elemento específico que agarrar, o elegir el destino final donde moverse) mientras el agente inteligente resuelve problemas de bajo nivel (como planificación de trayectorias, esquivar obstáculos, etc.) que permitan realizar la tarea designada de la manera óptima.En particular, esta tesis gira en torno a una señal neuronal cognitiva de alto nivel originada como la falta de coincidencia entre las expectativas del usuario y las acciones reales ejecutadas por los dispositivos inteligentes. Estas señales, denominadas potenciales de error (ErrPs), se consideran una forma natural de intercomunicar nuestro cerebro con máquinas y, por lo tanto, los usuarios solo requieren monitorizar las acciones de un dispositivo y evaluar mentalmente si este último se comporta correctamente o no. Esto puede verse como una forma de supervisar el comportamiento del dispositivo, en el que la decodificación de estas evaluaciones mentales se utiliza para proporcionar a estos dispositivos retroalimentación directamente relacionada con la ejecución de una tarea determinada para que puedan aprender y adaptarse a las preferencias del usuario.Dado que la respuesta neuronal de ErrP está asociada a un evento exógeno (dispositivo que comete una acción errónea), la mayoría de los trabajos desarrollados han intentado distinguir si una acción es correcta o errónea mediante la explotación de eventos discretos en escenarios bien controlados. Esta tesis presenta el primer intento de cambiar hacia configuraciones asíncronas que se centran en tareas relacionadas con el aumento de las capacidades motoras, con el objetivo de desarrollar interfaces para usuarios con movilidad limitada. En este tipo de configuraciones, dos desafíos importantes son que los eventos correctos o erróneos no están claramente definidos y los usuarios tienen que evaluar continuamente la tarea ejecutada, mientras que la clasificación de las señales EEG debe realizarse de forma asíncrona. Como resultado, los decodificadores tienen que lidiar constantemente con la actividad EEG de fondo, que típicamente conduce a una gran cantidad de errores de detección de firmas de error. Para superar estos desafíos, esta tesis aborda dos líneas principales de trabajo.Primero, explora la neurofisiología de las señales neuronales evocadas asociadas con la percepción de errores durante el uso interactivo de un BMI en escenarios continuos y más realistas.Se realizaron dos estudios para encontrar características alternativas basadas en el dominio de la frecuencia como una forma de lidiar con la alta variabilidad de las señales del EEG. Resultados, revelaron que existe un patrón estable representado como oscilaciones "theta" que mejoran la generalización durante la clasificación. Además, se utilizaron técnicas de aprendizaje automático de última generación para aplicar el aprendizaje de transferencia para discriminar asincrónicamente los errores cuando se introdujeron de forma gradual y no se conoce presumiblemente el inicio que desencadena los ErrPs. Además, los análisis de neurofisiología arrojan algo de luz sobre los mecanismos cognitivos subyacentes que provocan ErrP durante las tareas continuas, lo que sugiere la existencia de modelos neuronales en nuestro cerebro que acumulan evidencia y solo toman una decisión al alcanzar un cierto umbral. En segundo lugar, esta tesis evalúa la implementación de estos potenciales relacionados con errores en tres aplicaciones orientadas al usuario. Estos estudios no solo exploran cómo maximizar el rendimiento de decodificación de las firmas ErrP, sino que también investigan los mecanismos neuronales subyacentes y cómo los diferentes factores afectan las señales provocadas.La primera aplicación de esta tesis presenta una nueva forma de guiar a un robot móvil que se mueve en un entorno continuo utilizando solo potenciales de error como retroalimentación que podrían usarse para el control directo de dispositivos de asistencia. Con este propósito, proponemos un algoritmo basado en el emparejamiento de políticas para el aprendizaje de refuerzo inverso para inferir el objetivo del usuario a partir de señales cerebrales.La segunda aplicación presentada en esta tesis contempla los primeros pasos hacia un BCI híbrido para ejecutar distintos tipos de agarre de objetos, con el objetivo de ayudar a las personas que han perdido la funcionalidad motora de su extremidad superior. Este BMI combina la decodificación del tipo de agarre a partir de señales de EEG obtenidas del espectro de baja frecuencia con los potenciales de error provocados como resultado de la monitorización de movimientos de agarre erróneos. Los resultados muestran que, en efecto los ErrP aparecen en combinaciones de señales motoras originadas a partir de movimientos de agarre consistentes en una única repetición. Además, la evaluación de los diferentes factores involucrados en el diseño de la interfaz híbrida (como la velocidad de los estímulos, el tipo de agarre o la tarea mental) muestra cómo dichos factores afectan la morfología del subsiguiente potencial de error evocado.La tercera aplicación investiga los correlatos neuronales y los procesos cognitivos subyacentes asociados con desajustes somatosensoriales producidos por perturbaciones inesperadas durante la estimulación eléctrica neuromuscular en el brazo de un usuario. Este estudio simula los posibles errores que ocurren durante la terapia de neuro-rehabilitación, en la que la activación simultánea de la estimulación aferente mientras los sujetos se concentran en la realización de una tarea motora es crucial para una recuperación óptima. Los resultados muestran que los errores pueden aumentar la atención del sujeto en la tarea y desencadenar mecanismos de aprendizaje que al mismo tiempo podrían promover la neuroplasticidad motora.En resumen, a lo largo de esta tesis, se han diseñado varios paradigmas experimentales para mejorar la comprensión de cómo se generan los potenciales relacionados con errores durante el uso interactivo de BMI en aplicaciones orientadas al usuario. Se han propuesto diferentes métodos para pasar de la configuración bloqueada en el tiempo a la asíncrona, tanto en términos de decodificación como de percepción de los eventos erróneos; y ha explorado tres aplicaciones relacionadas con el aumento de las capacidades motoras, en las cuales los ErrPs se pueden usar para el control de dispositivos, la sustitución de motores y la neuro-rehabilitación.Brain-machine interfaces (BMIs) allow the decoding of cortical activation patterns from the users brain to provide people with severely limited mobility, due to an accident or disease, a way to establish a direct connection between their brain and a device. In this sense, BMIs based in noninvasive recordings, such as the electroencephalogram (EEG) have o↵ered these users new opportunities to regain control over activities of their daily life that they could not perform otherwise, especially in the areas of communication and control of their environment. Over the past years and with the latest technological advancements, devices have significantly grown on complexity expanding the number of possibilities to control complex robotic devices, prosthesis with numerous degrees of freedom or even to apply compound patterns of electrical stimulation on the subjects own paralyzed extremities to execute precise movements. However, the band-with of communication between brain and devices is still very limited, both in terms of the number and the speed at which neural commands can be decoded, and thus solely relying on neural signals do not guarantee accurate control them. In order to benefit of these technologies, the field of BMIs adopted the well-known approach of shared-control. This strategy intends to create a cooperation system between the user and an intelligent device, liberating the user from the burdensome parts of the task without losing the feeling of being in control. Here, users only need to focus their attention on high-level commands (e.g. choose the final destination to reach, or a specific item to grab) while the intelligent agent resolve low-level problems (e.g. trajectory planning, obstacle avoidance, etc) to perform the designated task in the optimal way. In particular, this thesis revolves around a high-level cognitive neural signal originated as the mismatch between the expectations of the user and the actual actions executed by the intelligent devices. These signals, denoted as error-related potentials (ErrPs), are thought as a natural way to intercommunicate our brain with machines and thus users only require to monitor the actions of a device and mentally assess whether the latter is behaving correctly or not. This can be seen as a way to supervise the device’s behavior, in which the decoding of these mental assessments is used to provide these devices with feedback directly related with the performance of a given task so they can learn and adapt to the user’s preferences. Since the ErrP’s neural response is associated to an exogenous event (device committing an erroneous action), most of the developed works have attempted to distinguish whether an action is correct or erroneous by exploiting discrete events under well-controlled scenarios. This thesis presents the first attempt to shift towards asynchronous settings that focus on tasks related with the augmentation of motor capabilities, with the objective of developing interfaces for users with limited mobility. In this type of setups, two important challenges are that correct or erroneous events are not clearly defined and users have to continuously evaluate the executed task, while classification of EEG signals has to be performed asynchronously. As a result, the decoders have to constantly deal with background EEG activity, which typically leads to a large number of missdetection of error signatures. To overcome these challenges, this thesis addresses two main lines of work. First, it explores the neurophysiology of the evoked neural signatures associated with the perception of errors during the interactive use of a BMI in continuous and more realistic scenarios. Two studies were performed to find alternative features based on the frequency domain as a way of dealing with the high variability of EEG signals. Results, revealed that there exists a stable pattern represented as theta oscillations that enhance generalization during classification. Also, state-of-the-art machine learning techniques were used to apply transfer learning to asynchronously discriminate errors when they were introduced in a gradual fashion and the onset that triggers the ErrPs is not presumably known. Furthermore, neurophsysiology analyses shed some light about the underlying cognitive mechanisms that elicit ErrP during continuous tasks, suggesting the existence of neural models in our brain that accumulate evidence and only take a decision upon reaching a certain threshold. Secondly, this thesis evaluates the implementation of these error-related potentials in three user-oriented applications. These studies not only explore how to maximize the decoding performance of ErrP signatures but also investigate the underlying neural mechanisms and how di↵erent factors a↵ect the elicited signals. The first application of this thesis presents a new way to guide a mobile robot moving in a continuous environment using only error potentials as feedback which could be used for the direct control of assistive devices. With this purpose, we propose an algorithm based on policy matching for inverse reinforcement learning to infer the user goal from brain signals. The second application presented in this thesis contemplates the first steps towards a hybrid BMI for grasping oriented to assist people who have lost motor functionality of their upper-limb. This BMI combines the decoding of the type of grasp from low-frequency EEG signals with error-related potentials elicited as the result of monitoring an erroneous grasping. The results show that ErrPs are elicited in combination of motor signatures from the low-frequency spectrum originated from single repetition grasping tasks and evaluates how di↵erent design factors (such as the speed of the stimuli, type of grasp or mental task) impact the morphology of the subsequent evoked ErrP. The third application investigates the neural correlates and the underlying cognitive processes associated with somatosensory mismatches produced by unexpected disturbances during neuromsucular electrical stimulation on a user’s arm. This study simulates possible errors that occur during neurorehabilitation therapy, in which the simultaneous activation of a↵erent stimulation while the subjects are concentrated in performing a motor task is crucial for optimal recovery. The results showed that errors may increase subject’s attention on the task and trigger learning mechanisms that at the same time could promote motor neuroplasticity. In summary, throughout this thesis, several experimental paradigms have been designed to improve the understanding of how error-related potentials are generated during the interactive use of BMIs in user-oriented applications. Di↵erent methods have been proposed to shift from time-locked to asynchronous settings, both in terms of decoding and perception of the erroneous events; and it has explored three applications related with the augmentation of motor capabilities, in which ErrPs can be used for control of devices, motor substitution and neurorehabilitation.<br /

VLSI Circuits for Bidirectional Neural Interfaces

Medical devices that deliver electrical stimulation to neural tissue are important clinical tools that can augment or replace pharmacological therapies. The success of such devices has led to an explosion of interest in the field, termed neuromodulation, with a diverse set of disorders being targeted for device-based treatment. Nevertheless, a large degree of uncertainty surrounds how and why these devices are effective. This uncertainty limits the ability to optimize therapy and gives rise to deleterious side effects. An emerging approach to improve neuromodulation efficacy and to better understand its mechanisms is to record bioelectric activity during stimulation. Understanding how stimulation affects electrophysiology can provide insights into disease, and also provides a feedback signal to autonomously tune stimulation parameters to improve efficacy or decrease side-effects. The aims of this work were taken up to advance the state-of-the-art in neuro-interface technology to enable closed-loop neuromodulation therapies. Long term monitoring of neuronal activity in awake and behaving subjects can provide critical insights into brain dynamics that can inform system-level design of closed-loop neuromodulation systems. Thus, first we designed a system that wirelessly telemetered electrocorticography signals from awake-behaving rats. We hypothesized that such a system could be useful for detecting sporadic but clinically relevant electrophysiological events. In an 18-hour, overnight recording, seizure activity was detected in a pre-clinical rodent model of global ischemic brain injury. We subsequently turned to the design of neurostimulation circuits. Three critical features of neurostimulation devices are safety, programmability, and specificity. We conceived and implemented a neurostimulator architecture that utilizes a compact on-chip circuit for charge balancing (safety), digital-to-analog converter calibration (programmability) and current steering (specificity). Charge balancing accuracy was measured at better than 0.3%, the digital-to-analog converters achieved 8-bit resolution, and physiological effects of current steering stimulation were demonstrated in an anesthetized rat. Lastly, to implement a bidirectional neural interface, both the recording and stimulation circuits were fabricated on a single chip. In doing so, we implemented a low noise, ultra-low power recording front end with a high dynamic range. The recording circuits achieved a signal-to-noise ratio of 58 dB and a spurious-free dynamic range of better than 70 dB, while consuming 5.5 μW per channel. We demonstrated bidirectional operation of the chip by recording cardiac modulation induced through vagus nerve stimulation, and demonstrated closed-loop control of cardiac rhythm

Moving through language: a behavioural and linguistic analysis of spatial mental model construction

Over the past few decades, our understanding of the cognitive processes underpinning our navigational abilities has expanded considerably. Models have been constructed that attempt to explain various key aspects of our wayfinding abilities, from the selection of salient features in environments to the processes involved in updating our position with respect to those features during movement. However, there remain several key open questions. Much of the research in spatial cognition has investigated visuospatial performance on the basis of sensory input (predominantly vision, but also sound, hapsis, and kinaesthesia), and while language production has been the subject of extensive research in psycholinguistics and cognitive linguistics, many aspects of language encoding remain unexplored. The research presented in this thesis aimed to explore outstanding issues in spatial language processing, tying together conceptual ends from different fields that have the potential to greatly inform each other, but focused specifically on how landmark information and spatial reference frames are encoded in mental representations characterised by different spatial reference frames. The first five experiments introduce a paradigm in which subjects encode skeletal route descriptions containing egocentric (“left/right”) or allocentric (cardinal) relational terms, while they also intentionally maintain an imagined egocentric or allocentric viewpoint. By testing participants’ spatial knowledge either in an allocentric (Experiments 1-3) or in an egocentric task (Experiments 4 and 5) this research exploits the facilitation produced by encoding-test congruence to clarify the contribution of mental imagery during spatial language processing and spatial tasks. Additionally, Experiments 1-3 adopted an eye-tracking methodology to study the allocation of attention to landmarks in descriptions and sketch maps as a function of linguistic reference frame and imagined perspective, while also recording subjective self-reports of participants’ phenomenal experiences. Key findings include evidence that egocentric and allocentric relational terms may not map directly onto egocentric and allocentric imagined perspectives, calling into question a common assumptions of psycholinguistic studies of spatial language. A novel way to establish experimental control over mental representations is presented, together with evidence that specific eye gaze patterns on landmark words or landmark regions of maps can be diagnostic of different imagined spatial perspectives. Experiments 4 and 5 adopted the same key manipulations to the study of spatial updating and bearing estimation following encoding of short, aurally-presented route descriptions. By employing two different response modes in this triangle completion task, Experiments 4 and 5 attempted to address key issues of experimental control that may have caused the conflicting results found in the literature on spatial updating during mental navigation and visuospatial imagery. The impact of encoding manipulations and of differences in response modality on embodiment and task performance were explored. Experiments 6-8 subsequently attempted to determine the developmental trajectory for the ability to discriminate between navigationally salient and non-salient landmarks, and to translate spatial relations between different reference frames. In these developmental studies, children and young adolescents were presented with videos portraying journeys through virtual environments from an egocentric perspective, and tested their ability to translate the resulting representations in order to perform allocentric spatial tasks. No clear facilitation effect of decision-point landmarks was observed or any strong indication that salient navigational features are more strongly represented in memory within the age range we tested (four to 11 years of age). Possible reasons for this are discussed in light of the relevant literature and methodological differences. Globally, the results presented indicate a functional role of imagery during language processing, pointing to the importance of introspection and accurate task analyses when interpreting behavioural results. Additionally, the study of implicit measures of attention such as eye tracking measures has the potential to improve our understanding mental representations, and of how they mediate between perception, action, and language. Lastly, these results also suggest that synergy between seemingly distinct research areas may be key in better characterising the nature of mental imagery in its different forms, and that the phenomenology of imagery content will be an essential part of this and future research

Moving through language: a behavioural and linguistic analysis of spatial mental model construction

Over the past few decades, our understanding of the cognitive processes underpinning our navigational abilities has expanded considerably. Models have been constructed that attempt to explain various key aspects of our wayfinding abilities, from the selection of salient features in environments to the processes involved in updating our position with respect to those features during movement. However, there remain several key open questions. Much of the research in spatial cognition has investigated visuospatial performance on the basis of sensory input (predominantly vision, but also sound, hapsis, and kinaesthesia), and while language production has been the subject of extensive research in psycholinguistics and cognitive linguistics, many aspects of language encoding remain unexplored. The research presented in this thesis aimed to explore outstanding issues in spatial language processing, tying together conceptual ends from different fields that have the potential to greatly inform each other, but focused specifically on how landmark information and spatial reference frames are encoded in mental representations characterised by different spatial reference frames. The first five experiments introduce a paradigm in which subjects encode skeletal route descriptions containing egocentric (“left/right”) or allocentric (cardinal) relational terms, while they also intentionally maintain an imagined egocentric or allocentric viewpoint. By testing participants’ spatial knowledge either in an allocentric (Experiments 1-3) or in an egocentric task (Experiments 4 and 5) this research exploits the facilitation produced by encoding-test congruence to clarify the contribution of mental imagery during spatial language processing and spatial tasks. Additionally, Experiments 1-3 adopted an eye-tracking methodology to study the allocation of attention to landmarks in descriptions and sketch maps as a function of linguistic reference frame and imagined perspective, while also recording subjective self-reports of participants’ phenomenal experiences. Key findings include evidence that egocentric and allocentric relational terms may not map directly onto egocentric and allocentric imagined perspectives, calling into question a common assumptions of psycholinguistic studies of spatial language. A novel way to establish experimental control over mental representations is presented, together with evidence that specific eye gaze patterns on landmark words or landmark regions of maps can be diagnostic of different imagined spatial perspectives. Experiments 4 and 5 adopted the same key manipulations to the study of spatial updating and bearing estimation following encoding of short, aurally-presented route descriptions. By employing two different response modes in this triangle completion task, Experiments 4 and 5 attempted to address key issues of experimental control that may have caused the conflicting results found in the literature on spatial updating during mental navigation and visuospatial imagery. The impact of encoding manipulations and of differences in response modality on embodiment and task performance were explored. Experiments 6-8 subsequently attempted to determine the developmental trajectory for the ability to discriminate between navigationally salient and non-salient landmarks, and to translate spatial relations between different reference frames. In these developmental studies, children and young adolescents were presented with videos portraying journeys through virtual environments from an egocentric perspective, and tested their ability to translate the resulting representations in order to perform allocentric spatial tasks. No clear facilitation effect of decision-point landmarks was observed or any strong indication that salient navigational features are more strongly represented in memory within the age range we tested (four to 11 years of age). Possible reasons for this are discussed in light of the relevant literature and methodological differences. Globally, the results presented indicate a functional role of imagery during language processing, pointing to the importance of introspection and accurate task analyses when interpreting behavioural results. Additionally, the study of implicit measures of attention such as eye tracking measures has the potential to improve our understanding mental representations, and of how they mediate between perception, action, and language. Lastly, these results also suggest that synergy between seemingly distinct research areas may be key in better characterising the nature of mental imagery in its different forms, and that the phenomenology of imagery content will be an essential part of this and future research