Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

Bidirectional neural interfaces are tools designed to “communicate” with the brain via recording and modulation of neuronal activity. The bidirectional interface systems have been adopted for many applications. Neuroscientists employ them to map neuronal circuits through precise stimulation and recording. Medical doctors deploy them as adaptable medical devices which control therapeutic stimulation parameters based on monitoring real-time neural activity. Brain-machine-interface (BMI) researchers use neural interfaces to bypass the nervous system and directly control neuroprosthetics or brain-computer-interface (BCI) spellers. In bidirectional interfaces, the implantable transducers as well as the corresponding electronic circuits and systems face several challenges. A high channel count, low power consumption, and reduced system size are desirable for potential chronic deployment and wider applicability. Moreover, a neural interface designed for robust closed-loop operation requires the mitigation of stimulation artifacts which corrupt the recorded signals. This dissertation introduces several techniques targeting low power consumption, small size, and reduction of stimulation artifacts. These techniques are implemented for extracellular electrophysiological recording and two stimulation modalities: direct current stimulation for closed-loop control of seizure detection/quench and optical stimulation for optogenetic studies. While the two modalities differ in their mechanisms, hardware implementation, and applications, they share many crucial system-level challenges. The first method aims at solving the critical issue of stimulation artifacts saturating the preamplifier in the recording front-end. To prevent saturation, a novel mixed-signal stimulation artifact cancellation circuit is devised to subtract the artifact before amplification and maintain the standard input range of a power-hungry preamplifier. Additional novel techniques have been also implemented to lower the noise and power consumption. A common average referencing (CAR) front-end circuit eliminates the cross-channel common mode noise by averaging and subtracting it in analog domain. A range-adapting SAR ADC saves additional power by eliminating unnecessary conversion cycles when the input signal is small. Measurements of an integrated circuit (IC) prototype demonstrate the attenuation of stimulation artifacts by up to 42 dB and cross-channel noise suppression by up to 39.8 dB. The power consumption per channel is maintained at 330 nW, while the area per channel is only 0.17 mm2. The second system implements a compact headstage for closed-loop optogenetic stimulation and electrophysiological recording. This design targets a miniaturized form factor, high channel count, and high-precision stimulation control suitable for rodent in-vivo optogenetic studies. Monolithically integrated optoelectrodes (which include 12 µLEDs for optical stimulation and 12 electrical recording sites) are combined with an off-the-shelf recording IC and a custom-designed high-precision LED driver. 32 recording and 12 stimulation channels can be individually accessed and controlled on a small headstage with dimensions of 2.16 x 2.38 x 0.35 cm and mass of 1.9 g. A third system prototype improves the optogenetic headstage prototype by furthering system integration and improving power efficiency facilitating wireless operation. The custom application-specific integrated circuit (ASIC) combines recording and stimulation channels with a power management unit, allowing the system to be powered by an ultra-light Li-ion battery. Additionally, the µLED drivers include a high-resolution arbitrary waveform generation mode for shaping of µLED current pulses to preemptively reduce artifacts. A prototype IC occupies 7.66 mm2, consumes 3.04 mW under typical operating conditions, and the optical pulse shaping scheme can attenuate stimulation artifacts by up to 3x with a Gaussian-rise pulse rise time under 1 ms.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147674/1/mendrela_1.pd

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

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine