Communication channel analysis and real time compressed sensing for high density neural recording devices

Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

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

Wireless Simultaneous Stimulation-and-Recording Device (SRD) to Train Cortical Circuits in Rat Somatosensory Cortex

The primary goal of this project is to develop a wireless system for simultaneous recording-and-stimulation (SRD) to deliver low amplitude current pulses to the primary somatosensory cortex (SI) of rats to activate and enhance an interhemispheric cortical pathway. Despite the existence of an interhemispheric connection between similar forelimb representations of SI cortices, forelimb cortical neurons respond only to input from the contralateral (opposite side) forelimb and not to input from the ipsilateral (same side) forelimb. Given the existence of this interhemispheric pathway we have been able to strengthen/enhance the pathway through chronic intracortical microstimulation (ICMS) in previous acute experiments of anesthetized rats. In these acute experiments strengthening the interhemispheric pathway also brings about functional reorganization whereby cortical neurons in forelimb cortex respond to new input from the ipsilateral forelimb. Having the ability to modify cortical circuitry will have important applications in stroke patients and could serve to rescue and/or enhance responsiveness in surviving cells around the stroke region. Also, the ability to induce functional reorganization within the deafferented cortical map, which follows limb amputation, will also provide a vehicle for modulating maladaptive cortical reorganization often associated with phantom limb pain leading to reduced pain. In order to increase our understanding of the observed functional reorganization and enhanced pathway, we need to be able to test these observations in awake and behaving animals and eventually study how these changes persist over a prolonged period of time. To accomplish this a system was needed to allow simultaneous recording and stimulation in awake rats. However, no such commercial or research system exists that meets all requirements for such an experiment. In this project we describe the (1) system design, (2) system testing, (3) system evaluation, and (4) system implementation of a wireless simultaneous stimulation-and-recording device (SRD) to be used to modulate cortical circuits in an awake rodent animal model

Wireless Implantable ICs for Energy-Efficient Long-Term Ambulatory EEG Monitoring

This thesis presents the design, development, and experimental characterization of wireless subcutaneous implantable integrated circuits and systems for long-term ambulatory EEG monitoring. Application-, system- and circuit-level requirements for such a device are discussed and a critical review of the state-of-the-art academic and currently available commercial solutions are provided. Two prototypes are presented: The first prototype presented in Chapter 2 is an 8-channel wireless implantable device with a 2.5×1.5 mm2 custom-designed integrated circuit implemented using CMOS 180nm technology at its core. The microchip is fabricated and the measurement results showing its efficacy in EEG signal recording in terms of input-referred noise, voltage gain, signal-to-noise ratio, and power consumption are presented. The chip is implemented together with a BLE 5.0 module on the same platform. Our vision and discussions on biocompatible encapsulation of this system, as well as its integration with a microelectrode array as also provided. The second prototype, also implemented in CMOS 180nm technology and presented in Chapter 3, employs a novel EEG recording channel architecture that enables long-term implantation of EEG monitoring devices through significant improvement of their energy efficiency. The channel leverages the inherent sparsity of the EEG signals and conducts recording in an activity-dependent adaptive manner. Thanks to the proposed fully dynamic spectral-compressing architecture, the recording channels power consumption is drastically reduced. More importantly, the proposed architecture reduces the required wireless transmission throughput by more than an order of magnitude. Our test results on 10 different patients’ pre-recorded human EEG data shows an average of 12.6× improvement in the device’s energy efficiency

Advances in Microelectronics for Implantable Medical Devices

Implantable medical devices provide therapy to treat numerous health conditions as well as monitoring and diagnosis. Over the years, the development of these devices has seen remarkable progress thanks to tremendous advances in microelectronics, electrode technology, packaging and signal processing techniques. Many of today’s implantable devices use wireless technology to supply power and provide communication. There are many challenges when creating an implantable device. Issues such as reliable and fast bidirectional data communication, efficient power delivery to the implantable circuits, low noise and low power for the recording part of the system, and delivery of safe stimulation to avoid tissue and electrode damage are some of the challenges faced by the microelectronics circuit designer. This paper provides a review of advances in microelectronics over the last decade or so for implantable medical devices and systems. The focus is on neural recording and stimulation circuits suitable for fabrication in modern silicon process technologies and biotelemetry methods for power and data transfer, with particular emphasis on methods employing radio frequency inductive coupling. The paper concludes by highlighting some of the issues that will drive future research in the field

Physical principles for scalable neural recording

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices

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

Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems

Objective: Diagnosis of sleep disorders is an expensive procedure that requires performing a sleep study, known as polysomnography (PSG), in a controlled environment. This study monitors the neural, eye and muscle activity of a patient using electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals which are then scored in to different sleep stages. Home PSG is often cited as an alternative of clinical PSG to make it more accessible, however it still requires patients to use a cumbersome system with multiple recording channels that need to be precisely placed. This thesis proposes a wearable sleep staging system using a single channel of EEG. For realisation of such a system, this thesis presents novel features for REM sleep detection from EEG (normally detected using EMG/EOG), a low-complexity automatic sleep staging algorithm using a single EEG channel and its complete integrated circuit implementation. Methods: The difference between Spectral Edge Frequencies (SEF) at 95% and 50% in the 8-16 Hz frequency band is shown to have high discriminatory ability for detecting REM sleep stages. This feature, together with other spectral features from single-channel EEG are used with a set of decision trees controlled by a state machine for classification. The hardware for the complete algorithm is designed using low-power techniques and implemented on chip using 0.18μm process node technology. Results: The use of SEF features from one channel of EEG resulted in 83% of REM sleep epochs being correctly detected. The automatic sleep staging algorithm, based on contextually aware decision trees, resulted in an accuracy of up to 79% on a large dataset. Its hardware implementation, which is also the very first complete circuit level implementation of any sleep staging algorithm, resulted in an accuracy of 98.7% with great potential for use in fully wearable sleep systems.Open Acces

Innovative intelligent sensors to objectively understand exercise interventions for older adults

The population of most western countries is ageing and, therefore, the ageing issue now matters more than ever. According to the reports of the United Nations in 2017, there were a total of 15.8 million (26.9%) people over 60 years of age in the United Kindom, and the numbers are projected to reach 23.5 million (31.5%) by 2050. Spending on medical treatment and healthcare for older adults accounts for two-fifths of the UK National Health Service (NHS) budget. Keeping older people healthy is a challenge. In general, exercise is believed to benefit both mental and physical health. Specifically, resistance band exercises are proven by many studies that they have potentially positive effects on both mental and physical health. However, treatment using resistance band exercise is usually done in unmonitored environments, such as at home or in a rehabilitation centre; therefore, the exercise cannot be measured and/or quantified accurately. Despite many years of research, the true effectiveness of resistance band exercises remains unclear. [Continues.]</div

Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications

Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making. In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passive—powered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%. One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data