A Bidirectional ASIC for Active Microchannel Neural Interfaces

Closed-loop neural prostheses have been widely used as a therapeutic strategy for a range of neurological, inflammatory, and cardiac disorders. Vagus nerve stimulation has shown promising results for the monitoring and treatment of post-operation symptoms of heart transplant recipients. A prime candidate for selective control of vagal fibres is the microchannel neural interface (MNI), which provides a suitable environment for neural growth and enables effective control of the neural activity in a bidirectional system. This paper presents the design and simulation of an ASIC in 180-nm high-voltage CMOS technology, capable of concurrent stimulation and neural recording with artifact reduction in a seven-channel MNI. The analog front-end amplifies action potentials with a gain of 40 dB, presenting a common-mode rejection ratio of 81 dB at 1 kHz and a noise efficiency factor of 5.13 over the 300 Hz to 5 kHz recording bandwidth. A 42-V-compliant stimulation module operates concurrently and independently across the seven channels

A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals

A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level. Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep. This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals

Electrochemical Safety Studies of Cochlear Implant Electrodes Using the Finite Element Method

Cochlear implants, amongst other neural prostheses, utilise platinum electrodes as an interface between the synthetic implant and the biological tissue environment. If excessive electrical charge is injected via these electrodes, injury to the tissue may result. Empirically derived stimulation limits have been defined to prevent tissue damage, however the injurious mechanisms are still unclear. Evidence suggests that the non-uniform distribution of charge on electrodes influences the electrochemical generation of toxic by-products. However, in vivo and in vitro techniques are limited in their ability to systematically explore the factors and mechanisms that contribute to stimulation-induced tissue injury. To this end, an in silico approach was used to develop a time-domain model of cochlear implant stimulation electrodes. A constant phase angle impedance was used to model the reversible processes on the electrode surface, and Butler-Volmer reaction kinetics were used to define the behaviour of the water window irreversible electrochemical reactions. The resulting model provided time-domain responses of the current density distributions, and net charge consumed by the hydrolysis reactions. This model was then used to perform systematic evaluations of various electrode geometries and stimulation parameters. The modelling results showed the current associated with irreversible reactions was non-uniform and tended towards the periphery of the electrode. A comparison of electrode geometries revealed interactions between electrode size, shape and recess depth. Stimulation mode, electrode position, and electrolyte conductivity were found to impact the shape of the electric field and the extent of irreversible reactions. This emphasised the influence of the physiological environment on the stimulation safety. In vitro experiments were conducted to validate the model. The implications of the results described in this thesis can be used to inform the design of safer electrodes

Aaltomuodon hallinta transkraniaalisessa magneettistimulaatiossa

Transcranial magnetic stimulation (TMS) is a non-invasive tool for stimulating the brain via induced electric fields generated by driving a strong current pulse through a stimulation coil. The TMS group at Aalto University has developed a multi-locus TMS (mTMS) device which exploits linear superposition of electric fields. This allows for the rotation or movement of the stimulated area to be controlled electrically by having a set of overlapping coils instead of physically moving a single coil. The device utilizes an H-bridge topology which can be used to carefully control the current through a coil by changing the path of current through the stimulator circuitry with insulated gate bipolar transistors (IGBTs), and the aim of this Thesis was to develop a method for controlling these currents in such a way that a given reference current pulse (i.e., waveform) could be approximated. For better IGBT control, new controller cards had recently been designed, and one objective of this Thesis was to test them. Additionally, in preparation for more coils to be added to the system, a coil identification system utilizing digital temperature sensors and a microcontroller was prototyped. The bulk of this Thesis, however, consists of the algorithm that was developed for IGBT control. The idea is to calculate a timing sequence for the IGBTs in such a way that a waveform generated by a lower initial voltage reference pulse is effectively simulated by periodically driving current to the coil from a high-voltage source. The non-idealities present in the circuit, however, pose a problem for approximation accuracy, and this was compensated for by further developing the model by adding a back-prediction module that tries to predict a better input sequence for the system based on previous measurements. The controller cards for the IGBTs were found to be satisfactory, and the prototyped coil identification system seems like a feasible solution even in the presence of strong magnetic fields. The waveform approximation was found to give rising-phase predictions with 0.3—7.7% relative difference in maximum amplitude compared to actual output for the sequences tested, depending on the chosen correction parameters. The falling-phase predictions varied significantly due to lack of parameter data. The tools developed in this Thesis give a good starting point for further development of waveform control in TMS.Transkraniaalinen magneettistimulaatio (engl. transcranial magnetic stimulation, TMS) on ei-invasiivinen menetelmä aivojen stimulaatioon. Menetelmä perustuu indusoituihin sähkökenttiin, jotka luodaan ajamalla suuri virtapulssi stimulaatiokäämin läpi. Aalto-yliopiston TMS-ryhmä on kehittänyt uuden sukupolven mTMS-laitetta (engl. multi-locus TMS), jossa sähkökenttien superpositioon perustuen stimulaatioaluetta voidaan siirtää ja kääntää sähköisesti hyödyntäen useita keloja. Tyypillisesti stimulaatioalueen siirto tai kääntö toteutetaan fyysisesti kelaa liikuttamalla. Yliopistolla kehitetty laite perustuu sähköiseen H-siltakytkentään, jossa sähkövirran kulkureittiä voidaan hallita kytkemällä IGBT-transistoreita (engl. insulated gate bipolar transistor) päälle tai pois päältä. Tämän diplomityön tavoite oli kehittää menetelmä piirin virrankulun hallintaan siten, että haluttu referenssipulssi (eli aaltomuoto) voidaan mallintaa. IGBT-transistoreiden parempaa hallittavuutta varten ryhmässä oli aiemmin kehitetty uudet ohjainkortit, joiden testaaminen oli yksi tämän diplomityön osa-alueista, ja lisäksi uusien kelojen lisäämistä silmälläpitäen järjestelmälle valmistettiin prototyyppi kelojen tunnistusta varten. Pääpaino työssä oli kuitenkin kehittää IGBT-transistoreiden hallintaan algoritmi, jolla kelan läpi kulkevaa virtaa voidaan tarkasti hallita. Perusidea tässä algoritmissa on, että suurella alkujännitteellä ajetaan stimulaatiokelaan virtaa vain hetkittäin, jolloin voidaan efektiivisesti simuloida tilannetta, jossa alemmalla alkujännitteellä ajetaan virtaa kelan läpi jatkuvasti. Laitteen epäideaalisuudet johtivat hyvin epätarkkaan approksimaatioon, mitä varten kehitettiin ennustusmoduuli, joka pyrkii aiempiin mittauksiin perustuen antamaan paremman ennusteen aaltomuodon käyttäytymisestä. IGBT-ohjainkortit toimivat mittausten perusteella hyvin, ja kelojen tunnistusjärjestelmä vaikuttaa ainakin ensiarvioiden perusteella hyvältä, vahvoista magneettikentistä huolimatta. Approksimointialgoritmi testatuille aaltomuodoille antoi 0.3—7.7% suhteellisen eron maksimiamplitudien välille riippuen korjausparametreista. Työssä esitetyt työkalut antavat hyvän lähtökohdan aaltomuotojen hallinnan jatkokehitykseen TMS:ssä

Quantitative estimation of nerve fiber engagement by vagus nerve stimulation using physiological markers

Background Cervical vagus nerve stimulation (VNS) is a rapidly emerging bioelectronic treatment for brain, metabolic, cardiovascular and immune disorders. Its desired and off-target effects are mediated by different nerve fiber populations and knowledge of their engagement could guide calibration and monitoring of VNS therapies. Objective /Hypothesis: Stimulus-evoked compound action potentials (eCAPs) directly provide fiber engagement information but are currently not feasible in humans. A method to estimate fiber engagement through common, noninvasive physiological readouts could be used instead of eCAP measurements. Methods In anesthetized rats, we recorded eCAPs while registering acute physiological response markers to VNS: cervical electromyography (EMG), changes in heart rate (ΔHR) and breathing interval (ΔBI). Quantitative models were established to capture the relationship between A-, B- and C-fiber type activation and those markers, and to quantitatively estimate fiber activation from physiological markers and stimulation parameters. Results In bivariate analyses, we found that EMG correlates with A-fiber, ΔHR with B-fiber and ΔBI with C-fiber activation, in agreement with known physiological functions of the vagus. We compiled multivariate models for quantitative estimation of fiber engagement from these markers and stimulation parameters. Finally, we compiled frequency gain models that allow estimation of fiber engagement at a wide range of VNS frequencies. Our models, after calibration in humans, could provide noninvasive estimation of fiber engagement in current and future therapeutic applications of VNS