Listening to Brain Microcircuits for Interfacing With External World-Progress in Wireless Implantable Microelectronic Neuroengineering Devices: Experimental systems are described for electrical recording in the brain using multiple microelectrodes and short range implantable or wearable broadcasting units

Highly stretchable and customizable microneedle electrode arrays for intramuscular electromyography

Stretchable three-dimensional (3D) penetrating microelectrode arrays have potential utility in various fields, including neuroscience, tissue engineering, and wearable bioelectronics. These 3D microelectrode arrays can penetrate and conform to dynamically deforming tissues, thereby facilitating targeted sensing and stimulation of interior regions in a minimally invasive manner. However, fabricating custom stretchable 3D microelectrode arrays presents material integration and patterning challenges. In this study, we present the design, fabrication, and applications of stretchable microneedle electrode arrays (SMNEAs) for sensing local intramuscular electromyography signals ex vivo. We use a unique hybrid fabrication scheme based on laser micromachining, microfabrication, and transfer printing to enable scalable fabrication of individually addressable SMNEA with high device stretchability (60 to 90%). The electrode geometries and recording regions, impedance, array layout, and length distribution are highly customizable. We demonstrate the use of SMNEAs as bioelectronic interfaces in recording intramuscular electromyography from various muscle groups in the buccal mass of Aplysia.

An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors

Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.

Recent advances in cellular optogenetics for photomedicine

Since the successful introduction of exogenous photosensitive proteins, channelrhodopsin, to neurons, optogenetics has enabled substantial understanding of profound brain function by selectively manipulating neural circuits. In an optogenetic system, optical stimulation can be precisely delivered to brain tissue to achieve regulation of cellular electrical activity with unprecedented spatio-temporal resolution in living organisms. In recent years, the development of various optical actuators and novel light-delivery techniques has greatly expanded the scope of optogenetics, enabling the control of other signal pathways in non-neuronal cells for different biomedical applications, such as phototherapy and immunotherapy. This review focuses on the recent advances in optogenetic regulation of cellular activities for photomedicine. We discuss emerging optogenetic tools and light-delivery platforms, along with a survey of optogenetic execution in mammalian and microbial cells.

Wireless Addressable Cortical Microstimulators Powered by Near-Infrared Harvesting

Ensembles of autonomous, spatially distributed wireless stimulators can offer a versatile approach to patterned microstimulation of biological circuits such as the cortex. Here, we demonstrate the concept of a distributed, untethered, and addressable microstimulator, integrating an ultraminiaturized ASIC with a custom-designed GaAs photovoltaic (PV) microscale energy harvester, dubbed as an "optical neurograin (ONG)". An on-board Manchester-encoded near-infrared downlink delivers incident IR power and provides a synchronous clock across an ensemble of microdevices, triggering stimulus events by remote command. Each ONG has a unique device address and, when an incoming downlink bit sequence matches with this device identification (ID), the implant delivers a charge-balanced current stimulus to the target cortex. Present devices use 7-bit metal fuses fabricated during the CMOS process for their device ID, laser-scribed in post-processing, allowing in principle for a stimulator network of up to 128 nodes. We have characterized small ensembles of ONGs and shown a proof of concept of the system both on benchtop and in vivo rat rodent model.

Developing Collaborative Platforms to Advance Neurotechnology and Its Translation

Neurotechnological devices are failing to deliver on their therapeutic promise because of the time it takes to translate them from bench to clinic. In this Perspective, we reflect on lessons learned from medical device successes and failures and consider how such lessons might shape a strategic vision for translating neurotechnologies in the future. We articulate how the intentional design and deployment of "scientific platforms," from the technology stack of hardware and software through the supporting ecosystem, could catalyze a new wave of innovation, discovery, and therapy. We also identify specific actions that could promote future neurotechnology roadmaps and industrial-academic-government collaborative activities. We believe that community-supported neurotechnology platforms will prove to be transformational in accelerating ideas from bench to bedside, maximizing scientific discovery and improving patient care.

Functional interrogation of neural circuits with virally transmitted optogenetic tools

The vertebrate brain comprises a plethora of cell types connected by intertwined pathways. Optogenetics enriches the neuroscientific tool set for disentangling these neuronal circuits in a manner which exceeds the spatio-temporal precision of previously existing techniques. Technically, optogenetics can be divided in three types of optical and genetic combinations: (1) it is primarily understood as the manipulation of the activity of genetically modified cells (typically neurons) with light, i.e. optical actuators. (2) A second combination refers to visualizing the activity of genetically modified cells (again typically neurons), i.e. optical sensors. (3) A completely different interpretation of optogenetics refers to the light activated expression of a genetically induced construct. Here, we focus on the first two types of optogenetics, i.e. the optical actuators and sensors in an attempt to give an overview into the topic. We first cover methods to express opsins into neurons and introduce strategies of targeting specific neuronal populations in different animal species. We then summarize combinations of optogenetics with behavioral read out and neuronal imaging. Finally, we give an overview of the current state-of-the-art and an outlook on future perspectives.

Multi-Channel Neural Recording Implants: A Review

The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brain-machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multi-channel neural recording implants are surveyed. After presenting various neural-signal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.

Study of Long-Term Biocompatibility and Bio-Safety of Implantable Nanogenerators

Implantable nanogenerator (i-NG) has shown great promises for enabling self-powered implantable medical devices (IMDs). One essential requirement for practical i-NG applications is its long-term bio-compatibility and bio-safety. This paper presents a systematic study of polydimethylsiloxane (PDMS) and PDMS/Parylene-C packaged Polyvinylidene fluoride (PVDF) NGs implanted inside female ICR (Institute of Cancer Research) mice for up to six months. The PVDF NG had a stable in vitro output of 0.3 V when bended for 7200 cycles and an in vivo output of 0.1V under stretching. Multiple advanced imaging techniques, including computed tomography (CT), ultrasound, and photoacoustic were used to characterize the embedded i-NGs in vivo. The i-NGs kept excellent adhesion to the adjacent muscle surface, and exhibited stable electrical output during the entire examine period. No signs of toxicity or incompatibility were observed from the surrounding tissues, as well as from the whole body functions by pathological analyses and blood and serum test. The PDMS package was also able to effectively insulate the i-NG in biological environment with negligible stray currents at a pA scale. This series of in-vivo and in-vitro study confirmed the biological feasibility of using i-NG in vivo for biomechanical energy harvesting.

A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces

Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm² area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm² electrode array.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

OBJECTIVE

Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model.

APPROACH

This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity.

MAIN RESULTS

Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology.

SIGNIFICANCE

Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

Neural Probes for Chronic Applications

Developed over approximately half a century, neural probe technology is now a mature technology in terms of its fabrication technology and serves as a practical alternative to the traditional microwires for extracellular recording. Through extensive exploration of fabrication methods, structural shapes, materials, and stimulation functionalities, neural probes are now denser, more functional and reliable. Thus, applications of neural probes are not limited to extracellular recording, brain-machine interface, and deep brain stimulation, but also include a wide range of new applications such as brain mapping, restoration of neuronal functions, and investigation of brain disorders. However, the biggest limitation of the current neural probe technology is chronic reliability; neural probes that record with high fidelity in acute settings often fail to function reliably in chronic settings. While chronic viability is imperative for both clinical uses and animal experiments, achieving one is a major technological challenge due to the chronic foreign body response to the implant. Thus, this review aims to outline the factors that potentially affect chronic recording in chronological order of implantation, summarize the methods proposed to minimize each factor, and provide a performance comparison of the neural probes developed for chronic applications.

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.

Optimal wireless receiver structure for omnidirectional inductive power transmission to biomedical implants

In order to achieve omnidirectional inductive power transmission to biomedical implants, the use of several orthogonal coils in the receiver side (Rx) has been proposed in the past. In this paper, the optimal Rx structure for connecting three orthogonal Rx coils and the power management is found to achieve the maximum power delivered to the load (PDL) in the presence of any Rx coil tilting. Unlike previous works, in which a separate power management has been used for each coil to deliver power to the load, different resonant Rx structures for connecting three Rx coils to a single power management are studied. In simulations, connecting three Rx coils with the diameters of 3 mm, 3.3 mm, and 3.6 mm in series and resonating them with a single capacitor at the operation frequency of 100 MHz led to the maximum PDL for large loads when the implant was tilted for 45o. This optimal Rx structure achieves higher PDL in worst-case scenarios as well as reduces the number of power managements to only one.

A Q-Modulation Technique for Efficient Inductive Power Transmission

A fully-integrated power management ASIC for efficient inductive power transmission has been presented capable of automatic load transformation using a method, called Q-modulation. Q-modulation is an adaptive scheme that offers load matching against a wide range of loading (RL ) and coupling distance (d23 ) variations in inductive links to maintain high power transfer efficiency (PTE). It is suitable for inductive powering implantable microelectronic devices (IMDs), recharging mobile electronics, and electric vehicles. In Q-modulation, the zero-crossings of the induced current in the receiver (Rx) LC-tank are detected and a low-loss switch chops the Rx LC-tank for part of the power carrier cycle to form a high-Q LC-tank and store the maximum energy, which is then transferred to RL by opening the switch. By adjusting the duty cycle (D), the loaded-Q of the Rx LC-tank can be dynamically modulated to compensate for variations in RL . A Q-modulation power management (QMPM) prototype chip was fabricated in a 0.35-μm standard CMOS process, occupying 4.8 mm2. In a 1.45 W wireless power transfer setup, using a class-E power amplifier (PA) operating at 2 MHz, the QMPM successfully increased the inductive link PTE and the overall power efficiency by 98.5% and 120.7% at d23 = 8 cm, respectively, by compensating for 150 Ω variation in RL at D = 45%.

A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces

Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45-54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 μV, and consumes 90 μW . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition.

A double-sided, single-chip integration scheme using through-silicon-via for neural sensing applications

We present a new double-sided, single-chip monolithic integration scheme to integrate the CMOS circuits and MEMS structures by using through-silicon-via (TSV). Neural sensing applications were chosen as the implementation example. The proposed heterogeneous device integrates standard 0.18 μm CMOS technology, TSV and neural probe array into a compact single chip device. The neural probe array on the back-side of the chip is connected to the CMOS circuits on the front-side of the chip by using low-parasitic TSVs through the chip. Successful fabrication results and detailed characterization demonstrate the feasibility and performance of the neural probe array, TSV and readout circuitry. The fabricated device is 5 × 5 mm(2) in area, with 16 channels of 150 μm-in-length neural probe array on the back-side, 200 μm-deep TSV through the chip and CMOS circuits on the front-side. Each channel consists of a 5 × 6 probe array, 3 × 14 TSV array and a differential-difference amplifier (DDA) based analog front-end circuitry with 1.8 V supply, 21.88 μW power consumption, 108 dB CMRR and 2.56 μVrms input referred noise. In-vivo long term implantation demonstrated the feasibility of presented integration scheme after 7 and 58 days of implantation. We expect the conceptual realization can be extended for higher density recording array by using the proposed method.

Electromagnetic and thermal effects of IR-UWB wireless implant systems on the human head

The usage of implanted wireless transmitting devices inside the human body has become widely popular in recent years. Applications such as multi-channel neural recording systems require high data rates in the wireless transmission link. Because of the inherent advantages provided by Impulse-Radio Ultra Wide Band (IR-UWB) such as high data rate capability, low power consumption and small form factor, there has been an increased research interest in using IR-UWB for bio-medical implant applications. Hence it has become imperative to analyze the electromagnetic effects caused by the use of IR-UWB when it is operated in or near the human body. This paper reports the electromagnetic effects of head implantable transmitting devices operating based on Impulse Radio Ultra Wide Band (IR-UWB) wireless technology. Simulations illustrate the performance of an implantable UWB antenna tuned to operate at 4 GHz with an -10 dB bandwidth of approximately 1 GHz when it is implanted in a human head model. Specific Absorption Rate (SAR), Specific Absorption (SA) and temperature increase are analyzed to compare the compliance of the transmitting device with international safety regulations.

Optrodes for combined optogenetics and electrophysiology in live animals

Optical tissue properties limit visible light depth penetration in tissue. Because of this, the recent development of optogenetic tools was quickly followed by the development of light delivery devices for in vivo optogenetics applications. We summarize the efforts made in the last decade to design neural probes that combine conventional electrophysiological recordings and optical channel(s) for optogenetic activation, often referred to as optodes or optrodes. Several aspects including challenges for light delivery in living brain tissue, the combination of light delivery with electrophysiological recordings, probe designs, multimodality, wireless implantable system, and practical considerations guiding the choice of configuration depending on the questions one seeks to address are presented.

An externally head-mounted wireless neural recording device for laboratory animal research and possible human clinical use

In this paper we present a new type of head-mounted wireless neural recording device in a highly compact package, dedicated for untethered laboratory animal research and designed for future mobile human clinical use. The device, which takes its input from an array of intracortical microelectrode arrays (MEA) has ninety-seven broadband parallel neural recording channels and was integrated on to two custom designed printed circuit boards. These house several low power, custom integrated circuits, including a preamplifier ASIC, a controller ASIC, plus two SAR ADCs, a 3-axis accelerometer, a 48MHz clock source, and a Manchester encoder. Another ultralow power RF chip supports an OOK transmitter with the center frequency tunable from 3GHz to 4GHz, mounted on a separate low loss dielectric board together with a 3V LDO, with output fed to a UWB chip antenna. The IC boards were interconnected and packaged in a polyether ether ketone (PEEK) enclosure which is compatible with both animal and human use (e.g. sterilizable). The entire system consumes 17mA from a 1.2Ahr 3.6V Li-SOCl2 1/2AA battery, which operates the device for more than 2 days. The overall system includes a custom RF receiver electronics which are designed to directly interface with any number of commercial (or custom) neural signal processors for multi-channel broadband neural recording. Bench-top measurements and in vivo testing of the device in rhesus macaques are presented to demonstrate the performance of the wireless neural interface.

A 13.56-mbps pulse delay modulation based transceiver for simultaneous near-field data and power transmission

A fully-integrated near-field wireless transceiver has been presented for simultaneous data and power transmission across inductive links, which operates based on pulse delay modulation (PDM) technique. PDM is a low-power carrier-less modulation scheme that offers wide bandwidth along with robustness against strong power carrier interference, which makes it suitable for implantable neuroprosthetic devices, such as retinal implants. To transmit each bit, a pattern of narrow pulses are generated at the same frequency of the power carrier across the transmitter (Tx) data coil with specific time delays to initiate decaying ringing across the tuned receiver (Rx) data coil. This ringing shifts the zero-crossing times of the undesired power carrier interference on the Rx data coil, resulting in a phase shift between the signals across Rx power and data coils, from which the data bit stream can be recovered. A PDM transceiver prototype was fabricated in a 0.35- μm standard CMOS process, occupying 1.6 mm(2). The transceiver achieved a measured 13.56 Mbps data rate with a raw bit error rate (BER) of 4.3×10(-7) at 10 mm distance between figure-8 data coils, despite a signal-to-interference ratio (SIR) of -18.5 dB across the Rx data coil. At the same time, a class-D power amplifier, operating at 13.56 MHz, delivered 42 mW of regulated power across a separate pair of high-Q power coils, aligned with the data coils. The PDM data Tx and Rx power consumptions were 960 pJ/bit and 162 pJ/bit, respectively, at 1.8 V supply voltage.

2.5D heterogeneously integrated microsystem for high-density neural sensing applications

Heterogeneously integrated and miniaturized neural sensing microsystems are crucial for brain function investigation. In this paper, a 2.5D heterogeneously integrated bio-sensing microsystem with μ-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of μ-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 × 24 μ-probe array with embedded TSVs is fabricated on a 5×5 mm(2) chip and bonded on the back side of the interposer. Thus, each channel contains 6 × 6 μ -probes with embedded TSVs. Additionally, the 4 dies are bonded on the front side of the interposer and designed for biopotential acquisition, feature extraction and classification via low-power analog front-end (AFE) circuits, area-power-efficient analog-to-digital converters (ADCs), configurable discrete wavelet transforms (DWTs), filters, and a MCU. An on-interposer bus ( μ-SPI) is designed for transferring data on the interposer. Finally, the successful in-vivo test demonstrated the proposed 2.5D heterogeneously integrated bio-sensing microsystem. The overall power of this microsystem is only 676.3 μW for 16-channel neural sensing.

Intention estimation in brain-machine interfaces

OBJECTIVE

The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF).

APPROACH

This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm.

MAIN RESULTS

Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied.

SIGNIFICANCE

These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.

Modifying cognition and behavior with electrical microstimulation: implications for cognitive prostheses

A fundamental goal of cognitive neuroscience is to understand how brain activity generates complex mental states and behaviors. While neuronal activity may predict or correlate with behavioral responses in a cognitive task, the use of electrical microstimulation presents the possibility to augment such correlational findings with direct evidence for causal relationships. Although microstimulation has been used for many years as a tool for mapping sensory and motor function, its role in learning, memory and decision-making has emerged only recently. Focal microstimulation of higher cortical areas can produce complex mental states and sequences of action. However, the relationship between the locus of stimulation and the percepts or actions evoked is often stereotyped and inflexible. The challenge is to develop stimulation systems that do not have fixed output but can flexibly contribute to complex cognitive and behavioral tasks. We discuss how microstimulation has been instrumental in manipulating a wide spectrum of cognitive functions including working memory, perceptual decisions and executive control by enhancing attention, re-ordering temporal sequence of saccades, improving associative learning or cognitive performance. For example, stimulation in prefrontal, parietal and sensory cortices may establish causal effects on decision-making, while microstimulation of inferotemporal cortex or caudate nucleus enhances associative learning. Building cognitive prosthetics based on the insights gleaned from such studies may depend on the development of multiple-input, multiple-output (MIMO) devices that allow subjects to control stimulation with their own thoughts in a closed-loop system.

Nonlinear Signal-Specific ADC for Efficient Neural Recording in Brain-Machine Interfaces

A nonlinear ADC dedicated to the digitization of neural signals in implantable brain-machine interfaces is presented. Benefitting from an exponential quantization function, effective resolution of the proposed ADC in the digitization of action potentials is almost 2 bits more than its physical number of bits. Hence, it is shown in this paper that the choice of a proper nonlinear quantization function helps reduce the outgoing bit rate carrying the recorded neural data. Another major benefit of digitizing neural signals using the proposed signal-specific ADC is the considerable reduction in the background noise of the neural signal. The 8-b exponential ADC reported in this paper digitizes large action potentials with maximum resolution of 10.5 bits , while quantizing the small background noise is performed with a resolution of as low as 3 bits. Fully-integrated version of the circuit was designed and fabricated in a 0.18-μm CMOS process, occupying 0.036 mm(2) silicon area. Designed based on a two-step successive-approximation register ADC architecture, the proposed ADC employs a piecewise-linear approximation of the target exponential function for quantization. Operating at a sampling frequency of 25 kS/s (typical for intra-cortical neural recording) and with a supply voltage of 1.8 V, the entire chip, including the ADC and reference circuits, dissipates 87.2 μW. According to the experiments, Noise-Content-Reduction Ratio (NCRR) of the ADC is 41.1 dB.

A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants

This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces.

Data compression in brain-machine/computer interfaces based on the Walsh-Hadamard transform

This paper reports on the application of the Walsh-Hadamard transform (WHT) for data compression in brain-machine/brain-computer interfaces. Using the proposed technique, the amount of the neural data transmitted off the implant is compressed by a factor of at least 63 at the expense of as low as 4.66% RMS error between the signal reconstructed on the external host and the original neural signal on the implant side. Based on the proposed idea, a 128-channel WHT processor was designed in a 0.18- μm CMOS process occupying 1.64 mm(2) of silicon area. The circuit consumes 81 μW (0.63 μW per channel) from a 1.8-V power supply at 250 kHz. A prototype of the proposed processor was implemented and successfully tested using prerecorded neural signals.

Current challenges to the clinical translation of brain machine interface technology

Development of neural prostheses over the past few decades has produced a number of clinically relevant brain-machine interfaces (BMIs), such as the cochlear prostheses and deep brain stimulators. Current research pursues the restoration of communication or motor function to individuals with neurological disorders. Efforts in the field, such as the BrainGate trials, have already demonstrated that such interfaces can enable humans to effectively control external devices with neural signals. However, a number of significant issues regarding BMI performance, device capabilities, and surgery must be resolved before clinical use of BMI technology can become widespread. This chapter reviews challenges to clinical translation and discusses potential solutions that have been reported in recent literature, with focuses on hardware reliability, state-of-the-art decoding algorithms, and surgical considerations during implantation.

Neuroprosthetic technology for individuals with spinal cord injury

CONTEXT

Spinal cord injury (SCI) results in a loss of function and sensation below the level of the lesion. Neuroprosthetic technology has been developed to help restore motor and autonomic functions as well as to provide sensory feedback.

FINDINGS

This paper provides an overview of neuroprosthetic technology that aims to address the priorities for functional restoration as defined by individuals with SCI. We describe neuroprostheses that are in various stages of preclinical development, clinical testing, and commercialization including functional electrical stimulators, epidural and intraspinal microstimulation, bladder neuroprosthesis, and cortical stimulation for restoring sensation. We also discuss neural recording technologies that may provide command or feedback signals for neuroprosthetic devices.

CONCLUSION/CLINICAL RELEVANCE

Neuroprostheses have begun to address the priorities of individuals with SCI, although there remains room for improvement. In addition to continued technological improvements, closing the loop between the technology and the user may help provide intuitive device control with high levels of performance.

Continuous Time Level Crossing Sampling ADC for Bio-Potential Recording Systems

In this paper we present a fixed window level crossing sampling analog to digital convertor for bio-potential recording sensors. This is the first proposed and fully implemented fixed window level crossing ADC without local DACs and clocks. The circuit is designed to reduce data size, power, and silicon area in future wireless neurophysiological sensor systems. We built a testing system to measure bio-potential signals and used it to evaluate the performance of the circuit. The bio-potential amplifier offers a gain of 53 dB within a bandwidth of 200 Hz-20 kHz. The input-referred rms noise is 2.8 µV. In the asynchronous level crossing ADC, the minimum delta resolution is 4 mV. The input signal frequency of the ADC is up to 5 kHz. The system was fabricated using the AMI 0.5 µm CMOS process. The chip size is 1.5 mm by 1.5 mm. The power consumption of the 4-channel system from a 3.3 V supply is 118.8 µW in the static state and 501.6 µW with a 240 kS/s sampling rate. The conversion efficiency is 1.6 nJ/conversion.

A high frequency active voltage doubler in standard CMOS using offset-controlled comparators for inductive power transmission

In this paper, we present a fully integrated active voltage doubler in CMOS technology using offset-controlled high speed comparators for extending the range of inductive power transmission to implantable microelectronic devices (IMD) and radio-frequency identification (RFID) tags. This active voltage doubler provides considerably higher power conversion efficiency (PCE) and lower dropout voltage compared to its passive counterpart and requires lower input voltage than active rectifiers, leading to reliable and efficient operation with weakly coupled inductive links. The offset-controlled functions in the comparators compensate for turn-on and turn-off delays to not only maximize the forward charging current to the load but also minimize the back current, optimizing PCE in the high frequency (HF) band. We fabricated the active voltage doubler in a 0.5-μm 3M2P std . CMOS process, occupying 0.144 mm(2) of chip area. With 1.46 V peak AC input at 13.56 MHz, the active voltage doubler provides 2.4 V DC output across a 1 kΩ load, achieving the highest PCE = 79% ever reported at this frequency. In addition, the built-in start-up circuit ensures a reliable operation at lower voltages.

A 100-channel hermetically sealed implantable device for chronic wireless neurosensing applications

A 100-channel fully implantable wireless broadband neural recording system was developed. It features 100 parallel broadband (0.1 Hz-7.8 kHz) neural recording channels, a medical grade 200 mAh Li-ion battery recharged inductively at 150 kHz , and data telemetry using 3.2 GHz to 3.8 GHz FSK modulated wireless link for 48 Mbps Manchester encoded data. All active electronics are hermetically sealed in a titanium enclosure with a sapphire window for electromagnetic transparency. A custom, high-density configuration of 100 individual hermetic feedthrough pins enable connection to an intracortical neural recording microelectrode array. A 100 MHz bandwidth custom receiver was built to remotely receive the FSK signal and achieved -77.7 dBm sensitivity with 10(-8) BER at 48 Mbps data rate. ESD testing on all the electronic inputs and outputs has proven that the implantable device satisfies the HBM Class-1B ESD Standard. In addition, the evaluation of the worst-case charge density delivered to the tissue from each I/O pin verifies the patient safety of the device in the event of failure. Finally, the functionality and reliability of the complete device has been tested on-bench and further validated chronically in ongoing freely moving swine and monkey animal trials for more than one year to date.

An implantable wireless neural interface for recording cortical circuit dynamics in moving primates

OBJECTIVE

Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective.

APPROACH

We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz.

MAIN RESULTS

Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity.

SIGNIFICANCE

We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of neurological conditions and will advance brain research.

A wideband dual-antenna receiver for wireless recording from animals behaving in large arenas

A low-noise wideband receiver (Rx) is presented for a multichannel wireless implantable neural recording (WINeR) system that utilizes time-division multiplexing of pulse width modulated (PWM) samples. The WINeR-6 Rx consists of four parts: 1) RF front end; 2) signal conditioning; 3) analog output (AO); and 4) field-programmable gate array (FPGA) back end. The RF front end receives RF-modulated neural signals in the 403-490 MHz band with a wide bandwidth of 18 MHz. The frequency-shift keying (FSK) PWM demodulator in the FPGA is a time-to-digital converter with 304 ps resolution, which converts the analog pulse width information to 16-bit digital samples. Automated frequency tracking has been implemented in the Rx to lock onto the free-running voltage-controlled oscillator in the transmitter (Tx). Two antennas and two parallel RF paths are used to increase the wireless coverage area. BCI-2000 graphical user interface has been adopted and modified to acquire, visualize, and record the recovered neural signals in real time. The AO module picks three demultiplexed channels and converts them into analog signals for direct observation on an oscilloscope. One of these signals is further amplified to generate an audio output, offering users the ability to listen to ongoing neural activity. Bench-top testing of the Rx performance with a 32-channel WINeR-6 Tx showed that the input referred noise of the entire system at a Tx-Rx distance of 1.5 m was 4.58 μV rms with 8-bit resolution at 640 kSps. In an in vivo experiment, location-specific receptive fields of hippocampal place cells were mapped during a behavioral experiment in which a rat completed 40 laps in a large circular track. Results were compared against those acquired from the same animal and the same set of electrodes by a commercial hardwired recording system to validate the wirelessly recorded signals.

Design constraints for mobile, high-speed fluorescence brain imaging in awake animals

In this paper we present a fully self-contained imaging instrument (30 mm overall length) that is capable of recording high speed and detect relatively small fluorescent signals (0.1% ΔF/F) from brain tissues potentially containing genetically-encoded sensors or dyes. This device potentially enables the study of neuronal activity in awake and mobile animals during natural behaviors without the stress and suppression of anesthesia and restraint. The device is a fully self-contained illumination system, wide field fluorescence microscope (~ 4.8 mm² FOV-25 um lateral resolution-1.8 × magnification-0.39 NA) and CMOS image sensor (32 × 32). The total weight of the system is 10 g and is capable of imaging up to 900 fps. We present voltage dye RH1692 experiments using the system to study the somatosensory cortex of mice during whisker movements using an air puff.

A power and data link for a wireless-implanted neural recording system

A wireless cortical neural recording system with a miniature-implanted package is needed in a variety of neuroscience and biomedical applications. Toward that end, we have developed a transcutaneous two-way communication and power system for wireless neural recording. Wireless powering and forward data transmission (into the body) at 1.25 Mbps is achieved using a frequency-shift keying modulated class E converter. The reverse telemetry (out of the body) carrier frequency is generated using an integer-N phase-locked loop, providing the necessary wideband data link to support simultaneous reverse telemetry from multiple implanted devices on separate channels. Each channel is designed to support reverse telemetry with a data rate in excess of 3 Mbps, which is sufficient for our goal of streaming 16 channels of raw neural data. We plan to incorporate this implantable power and telemetry system in a 1-cm diameter single-site cortical neural recording implant.

Design of ultra-low power biopotential amplifiers for biosignal acquisition applications

Rapid development in miniature implantable electronics are expediting advances in neuroscience by allowing observation and control of neural activities. The first stage of an implantable biosignal recording system, a low-noise biopotential amplifier (BPA), is critical to the overall power and noise performance of the system. In order to integrate a large number of front-end amplifiers in multichannel implantable systems, the power consumption of each amplifier must be minimized. This paper introduces a closed-loop complementary-input amplifier, which has a bandwidth of 0.05 Hz to 10.5 kHz, an input-referred noise of 2.2 μ Vrms, and a power dissipation of 12 μW. As a point of comparison, a standard telescopic-cascode closed-loop amplifier with a 0.4 Hz to 8.5 kHz bandwidth, input-referred noise of 3.2 μ Vrms, and power dissipation of 12.5 μW is presented. Also for comparison, we show results from an open-loop complementary-input amplifier that exhibits an input-referred noise of 3.6 μ Vrms while consuming 800 nW of power. The two closed-loop amplifiers are fabricated in a 0.13 μ m CMOS process. The open-loop amplifier is fabricated in a 0.5 μm SOI-BiCMOS process. All three amplifiers operate with a 1 V supply.

An overview of the recent wideband transcutaneous wireless communication techniques

Neuroprosthetic devices such as cochlear and retinal implants need to deliver a large volume of data from external sensors into the body, while invasive brain-computer interfaces need to deliver sizeable amounts of data from the central nervous system to target devices outside of the body. Nonetheless, the skin should remain intact. This paper reviews some of the latest techniques to establish wideband wireless communication links across the skin.

A recurrent neural network for closed-loop intracortical brain-machine interface decoders

Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.

Polymeric packaging for fully implantable wireless neural microsensors

We present polymeric packaging methods used for subcutaneous, fully implantable, broadband, and wireless neurosensors. A new tool for accelerated testing and characterization of biocompatible polymeric packaging materials and processes is described along with specialized test units to simulate our fully implantable neurosensor components, materials and fabrication processes. A brief description of the implantable systems is presented along with their current encapsulation methods based on polydimethylsiloxane (PDMS). Results from in-vivo testing of multiple implanted neurosensors in swine and non-human primates are presented. Finally, a novel augmenting polymer thin film material to complement the currently employed PDMS is introduced. This thin layer coating material is based on the Plasma Enhanced Chemical Vapor Deposition (PECVD) process of Hexamethyldisiloxane (HMDSO) and Oxygen (O(2)).

Recent advances in neural recording microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

Challenges and opportunities for next-generation intracortically based neural prostheses

Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.

Developing implantable neuroprosthetics: a new model in pig

A new model has been established in the domestic pig for neural prosthetic device development and testing. To this end, we report on a complete neural prosthetic developmental system using a wireless sensor as the implant, a pig as the animal model, and a novel data acquisition paradigm for actuator control. A new type of stereotactic frame with clinically-inspired fixations pins that place the pig brain in standard surgical plane was developed and tested with success during the implantation of the microsystem. The microsystem implanted was an ultra-low power (12.5 mW) 16-channel intracortical/epicranial device transmitting broadband (40 kS/s) data over a wireless infrared telemetric link. Pigs were implanted and neural data was collected over a period of 5 weeks, clearly showing single unit spiking activity.

A dynamical systems view of motor preparation: implications for neural prosthetic system design

Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached.

Efficient decoding with steady-state Kalman filter in neural interface systems

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

Autonomous head-mounted electrophysiology systems for freely behaving primates

Recent technological advances have led to new light-weight battery-operated systems for electrophysiology. Such systems are head mounted, run for days without experimenter intervention, and can record and stimulate from single or multiple electrodes implanted in a freely behaving primate. Here we discuss existing systems, studies that use them, and how they can augment traditional, physically restrained, 'in-rig' electrophysiology. With existing technical capabilities, these systems can acquire multiple signal classes, such as spikes, local field potential, and electromyography signals, and can stimulate based on real-time processing of recorded signals. Moving forward, this class of technologies, along with advances in neural signal processing and behavioral monitoring, have the potential to dramatically expand the scope and scale of electrophysiological studies.