Active microelectronic neurosensor arrays for implantable brain communication interfaces

Nonresonant powering of injectable nanoelectrodes enables wireless deep brain stimulation in freely moving mice

Devices that electrically modulate the deep brain have enabled important breakthroughs in the management of neurological and psychiatric disorders. Such devices are typically centimeter-scale, requiring surgical implantation and wired-in powering, which increases the risk of hemorrhage, infection, and damage during daily activity. Using smaller, remotely powered materials could lead to less invasive neuromodulation. Here, we present injectable, magnetoelectric nanoelectrodes that wirelessly transmit electrical signals to the brain in response to an external magnetic field. This mechanism of modulation requires no genetic modification of neural tissue, allows animals to freely move during stimulation, and uses nonresonant carrier frequencies. Using these nanoelectrodes, we demonstrate neuronal modulation in vitro and in deep brain targets in vivo. We also show that local subthalamic modulation promotes modulation in other regions connected via basal ganglia circuitry, leading to behavioral changes in mice. Magnetoelectric materials present a versatile platform technology for less invasive, deep brain neuromodulation.

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.

Modeling and Characterization of Capacitive Elements With Tissue as Dielectric Material for Wireless Powering of Neural Implants

This paper reports on the modeling and characterization of capacitive elements with tissue as the dielectric material, representing the core building block of a capacitive link for wireless power transfer to neural implants. Each capacitive element consists of two parallel plates that are aligned around the tissue layer and incorporate a grounded, guarded, capacitive pad to mitigate the adverse effect of stray capacitances and shield the plates from external interfering electric fields. The plates are also coated with a biocompatible, insulating, coating layer on the inner side of each plate in contact with the tissue. A comprehensive circuit model is presented that accounts for the effect of the coating layers and is validated by measurements of the equivalent capacitance as well as impedance magnitude/phase of the parallel plates over a wide frequency range of 1 kHz-10 MHz. Using insulating coating layers of Parylene-C at a thickness of and Parylene-N at a thickness of deposited on two sets of parallel plates with different sizes and shapes of the guarded pad, our modeling and characterization results accurately capture the effect of the thickness and electrical properties of the coating layers on the behavior of the capacitive elements over frequency and with different tissues.

Robust Wireless Power Transmission to mm-Sized Free-Floating Distributed Implants

This paper presents an inductive link for wireless power transmission (WPT) to mm-sized free-floating implants (FFIs) distributed in a large three-dimensional space in the neural tissue that is insensitive to the exact location of the receiver (Rx). The proposed structure utilizes a high-Q resonator on the target wirelessly powered plane that encompasses randomly positioned multiple FFIs, all powered by a large external transmitter (Tx). Based on resonant WPT fundamentals, we have devised a detailed method for optimization of the FFIs and explored design strategies and safety concerns, such as coil segmentation and specific absorption rate limits using realistic finite element simulation models in HFSS including head tissue layers, respectively. We have built several FFI prototypes to conduct accurate measurements and to characterize the performance of the proposed WPT method. Measurement results on 1-mm receivers operating at 60 MHz show power transfer efficiency and power delivered to the load at 2.4% and 1.3 mW, respectively, within 14-18 mm of Tx-Rx separation and 7 cm² of brain surface.

A low-cost, scalable, current-sensing digital headstage for high channel count μECoG

OBJECTIVE

High channel count electrode arrays allow for the monitoring of large-scale neural activity at high spatial resolution. Implantable arrays featuring many recording sites require compact, high bandwidth front-end electronics. In the present study, we investigated the use of a small, light weight, and low cost digital current-sensing integrated circuit for acquiring cortical surface signals from a 61-channel micro-electrocorticographic (μECoG) array.

APPROACH

We recorded both acute and chronic μECoG signal from rat auditory cortex using our novel digital current-sensing headstage. For direct comparison, separate recordings were made in the same anesthetized preparations using an analog voltage headstage. A model of electrode impedance explained the transformation between current- and voltage-sensed signals, and was used to reconstruct cortical potential. We evaluated the digital headstage using several metrics of the baseline and response signals.

MAIN RESULTS

The digital current headstage recorded neural signal with similar spatiotemporal statistics and auditory frequency tuning compared to the voltage signal. The signal-to-noise ratio of auditory evoked responses (AERs) was significantly stronger in the current signal. Stimulus decoding based on true and reconstructed voltage signals were not significantly different. Recordings from an implanted system showed AERs that were detectable and decodable for 52 d. The reconstruction filter mitigated the thermal current noise of the electrode impedance and enhanced overall SNR.

SIGNIFICANCE

We developed and validated a novel approach to headstage acquisition that used current-input circuits to independently digitize 61 channels of μECoG measurements of the cortical field. These low-cost circuits, intended to measure photo-currents in digital imaging, not only provided a signal representing the local cortical field with virtually the same sensitivity and specificity as a traditional voltage headstage but also resulted in a small, light headstage that can easily be scaled to record from hundreds of channels.

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.

Active C4 Electrodes for Local Field Potential Recording Applications

Extracellular neural recording, with multi-electrode arrays (MEAs), is a powerful method used to study neural function at the network level. However, in a high density array, it can be costly and time consuming to integrate the active circuit with the expensive electrodes. In this paper, we present a 4 mm × 4 mm neural recording integrated circuit (IC) chip, utilizing IBM C4 bumps as recording electrodes, which enable a seamless active chip and electrode integration. The IC chip was designed and fabricated in a 0.13 μm BiCMOS process for both in vitro and in vivo applications. It has an input-referred noise of 4.6 μVrms for the bandwidth of 10 Hz to 10 kHz and a power dissipation of 11.25 mW at 2.5 V, or 43.9 μW per input channel. This prototype is scalable for implementing larger number and higher density electrode arrays. To validate the functionality of the chip, electrical testing results and acute in vivo recordings from a rat barrel cortex are presented.

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.

Implanted miniaturized antenna for brain computer interface applications: analysis and design

Implantable Brain Computer Interfaces (BCIs) are designed to provide real-time control signals for prosthetic devices, study brain function, and/or restore sensory information lost as a result of injury or disease. Using Radio Frequency (RF) to wirelessly power a BCI could widely extend the number of applications and increase chronic in-vivo viability. However, due to the limited size and the electromagnetic loss of human brain tissues, implanted miniaturized antennas suffer low radiation efficiency. This work presents simulations, analysis and designs of implanted antennas for a wireless implantable RF-powered brain computer interface application. The results show that thin (on the order of 100 micrometers thickness) biocompatible insulating layers can significantly impact the antenna performance. The proper selection of the dielectric properties of the biocompatible insulating layers and the implantation position inside human brain tissues can facilitate efficient RF power reception by the implanted antenna. While the results show that the effects of the human head shape on implanted antenna performance is somewhat negligible, the constitutive properties of the brain tissues surrounding the implanted antenna can significantly impact the electrical characteristics (input impedance, and operational frequency) of the implanted antenna. Three miniaturized antenna designs are simulated and demonstrate that maximum RF power of up to 1.8 milli-Watts can be received at 2 GHz when the antenna implanted around the dura, without violating the Specific Absorption Rate (SAR) limits.

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.

An implantable neural sensing microsystem with fiber-optic data transmission and power delivery

We have developed a prototype cortical neural sensing microsystem for brain implantable neuroengineering applications. Its key feature is that both the transmission of broadband, multichannel neural data and power required for the embedded microelectronics are provided by optical fiber access. The fiber-optic system is aimed at enabling neural recording from rodents and primates by converting cortical signals to a digital stream of infrared light pulses. In the full microsystem whose performance is summarized in this paper, an analog-to-digital converter and a low power digital controller IC have been integrated with a low threshold, semiconductor laser to extract the digitized neural signals optically from the implantable unit. The microsystem also acquires electrical power and synchronization clocks via optical fibers from an external laser by using a highly efficient photovoltaic cell on board. The implantable unit employs a flexible polymer substrate to integrate analog and digital microelectronics and on-chip optoelectronic components, while adapting to the anatomical and physiological constraints of the environment. A low power analog CMOS chip, which includes preamplifier and multiplexing circuitry, is directly flip-chip bonded to the microelectrode array to form the cortical neurosensor device.

Control strategies for underactuated neural ensembles driven by optogenetic stimulation

Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded) neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

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 Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine Interface Applications

Reliability, scalability and clinical viability are of utmost importance in the design of wireless Brain Machine Interface systems (BMIs). This paper reports on the design and implementation of a neuroprocessor for conditioning raw extracellular neural signals recorded through microelectrode arrays chronically implanted in the brain of awake behaving rats. The neuroprocessor design exploits a sparse representation of the neural signals to combat the limited wireless telemetry bandwidth. We demonstrate a multimodal processing capability (monitoring, compression, and spike sorting) inherent in the neuroprocessor to support a wide range of scenarios in real experimental conditions. A wireless transmission link with rate-dependent compression strategy is shown to preserve information fidelity in the neural data. At 32 channels, the neuroprocessor has been fully implemented on a 5mm×5mm nano-FPGA, and the prototyping resulted in 5.19 mW power consumption, bringing its performance within the power-size constraints for clinical use. The optimal design for compression and sorting performance was evaluated for multiple sampling frequencies, wavelet basis choice and power consumption.

A 4 μW/Ch analog front-end module with moderate inversion and power-scalable sampling operation for 3-D neural microsystems

We report an analog front-end prototype designed in 0.25 μm CMOS process for hybrid integration into 3-D neural recording microsystems. For scaling towards massive parallel neural recording, the prototype has investigated some critical circuit challenges in power, area, interface, and modularity. We achieved extremely low power consumption of 4 μW/channel, optimized energy efficiency using moderate inversion in low-noise amplifiers (K of 5.98 × 10⁸ or NEF of 2.9), and minimized asynchronous interface (only 2 per 16 channels) for command and data capturing. We also implemented adaptable operations including programmable-gain amplification, power-scalable sampling (up to 50 kS/s/channel), wide configuration range (9-bit) for programmable gain and bandwidth, and 5-bit site selection capability (selecting 16 out of 128 sites). The implemented front-end module has achieved a reduction in noise-energy-area product by a factor of 5-25 times as compared to the state-of-the-art analog front-end approaches reported to date.

Computationally efficient neural feature extraction for spike sorting in implantable high-density recording systems

Modern microelectrode arrays acquire neural signals from hundreds of neurons in parallel that are subsequently processed for spike sorting. It is important to identify, extract, and transmit appropriate features that allow accurate spike sorting while using minimum computational resources. This paper describes a new set of spike sorting features, explicitly framed to be computationally efficient and shown to outperform principal component analysis (PCA)-based spike sorting. A hardware friendly architecture, feasible for implantation, is also presented for detecting neural spikes and extracting features to be transmitted for off chip spike classification. The proposed feature set does not require any off-chip training, and requires about 5% of computations as compared to the PCA-based features for the same classification accuracy, tested for spike trains with a broad range of signal-to-noise ratio. Our simulations show a reduction of required bandwidth to about 2% of original data rate, with an average classification accuracy of greater than 94% at a typical signal to noise ratio of 5 dB.

A high-efficiency low-voltage CMOS rectifier for harvesting energy in implantable devices

We present, in this paper, a new full-wave CMOS rectifier dedicated for wirelessly-powered low-voltage biomedical implants. It uses bootstrapped capacitors to reduce the effective threshold voltage of selected MOS switches. It achieves a significant increase in its overall power efficiency and low voltage-drop. Therefore, the rectifier is good for applications with low-voltage power supplies and large load current. The rectifier topology does not require complex circuit design. The highest voltages available in the circuit are used to drive the gates of selected transistors in order to reduce leakage current and to lower their channel on-resistance, while having high transconductance. The proposed rectifier was fabricated using the standard TSMC 0.18 μm CMOS process. When connected to a sinusoidal source of 3.3 V peak amplitude, it allows improving the overall power efficiency by 11% compared to the best recently published results given by a gate cross-coupled-based structure.

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.

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)).

A Fully-Passive Wireless Microsystem for Recording of Neuropotentials using RF Backscattering Methods

The ability to safely monitor neuropotentials is essential in establishing methods to study the brain. Current research focuses on the wireless telemetry aspect of implantable sensors in order to make these devices ubiquitous and safe. Chronic implants necessitate superior reliability and durability of the integrated electronics. The power consumption of implanted electronics must also be limited to within several milliwatts to microwatts to minimize heat trauma in the human body. In order to address these severe requirements, we developed an entirely passive and wireless microsystem for recording neuropotentials. An external interrogator supplies a fundamental microwave carrier to the microsystem. The microsystem comprises varactors that perform nonlinear mixing of neuropotential and fundamental carrier signals. The varactors generate third-order mixing products that are wirelessly backscattered to the external interrogator where the original neuropotential signals are recovered. Performance of the neuro-recording microsystem was demonstrated by wireless recording of emulated and in vivo neuropotentials. The obtained results were wireless recovery of neuropotentials as low as approximately 500 microvolts peak-to-peak (μV(pp)) with a bandwidth of 10 Hz to 3 kHz (for emulated signals) and with 128 epoch signal averaging of repetitive signals (for in vivo signals).

Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia

Functional electrical stimulation (FES), the coordinated electrical activation of multiple muscles, has been used to restore arm and hand function in people with paralysis. User interfaces for such systems typically derive commands from mechanically unrelated parts of the body with retained volitional control, and are unnatural and unable to simultaneously command the various joints of the arm. Neural interface systems, based on spiking intracortical signals recorded from the arm area of motor cortex, have shown the ability to control computer cursors, robotic arms and individual muscles in intact non-human primates. Such neural interface systems may thus offer a more natural source of commands for restoring dexterous movements via FES. However, the ability to use decoded neural signals to control the complex mechanical dynamics of a reanimated human limb, rather than the kinematics of a computer mouse, has not been demonstrated. This study demonstrates the ability of an individual with long-standing tetraplegia to use cortical neuron recordings to command the real-time movements of a simulated dynamic arm. This virtual arm replicates the dynamics associated with arm mass and muscle contractile properties, as well as those of an FES feedback controller that converts user commands into the required muscle activation patterns. An individual with long-standing tetraplegia was thus able to control a virtual, two-joint, dynamic arm in real time using commands derived from an existing human intracortical interface technology. These results show the feasibility of combining such an intracortical interface with existing FES systems to provide a high-performance, natural system for restoring arm and hand function in individuals with extensive paralysis.

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.

An implantable integrated low-power amplifier-microelectrode array for Brain-Machine Interfaces

One of the important challenges in designing Brain-Machine Interfaces (BMI) is to build implantable systems that have the ability to reliably process the activity of large ensembles of cortical neurons. In this paper, we report the design, fabrication, and testing of a polyimide-based microelectrode array integrated with a low-power amplifier as part of the Florida Wireless Integrated Recording Electrode (FWIRE) project at the University of Florida developing a fully implantable neural recording system for BMI applications. The electrode array was fabricated using planar micromachining MEMS processes and hybrid packaged with the amplifier die using a flip-chip bonding technique. The system was tested both on bench and in-vivo. Acute and chronic neural recordings were obtained from a rodent for a period of 42 days. The electrode-amplifier performance was analyzed over the chronic recording period with the observation of a noise floor of 4.5 microVrms, and an average signal-to-noise ratio of 3.8.

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

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.

Decoding 3-D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex

Intracortical microelectrode array recordings generate a variety of neural signals with potential application as control signals in neural interface systems. Previous studies have focused on single and multiunit activity (MUA), as well as low-frequency local field potentials (LFPs), but have not explored higher frequency (>200 Hz) LFPs. In addition, the potential to decode 3-D reach and grasp kinematics based on LFPs has not been demonstrated. Here, we use mutual information and decoding analyses to probe the information content about 3-D reaching and grasping of seven different LFP frequency bands in the range of 0.3-400 Hz. LFPs were recorded via 96-microelectrode arrays in primary motor cortex (M1) of two monkeys performing free reaching to grasp moving objects. Mutual information analyses revealed that higher frequency bands (e.g., 100-200 and 200-400 Hz) carried the most information about the examined kinematics. Furthermore, Kalman filter decoding revealed that broad-band high frequency LFPs, likely reflecting MUA, provided the best decoding performance as well as substantial accuracy in reconstructing reach kinematics, grasp aperture, and aperture velocity. These results indicate that LFPs, especially high frequency bands, could be useful signals for neural interfaces controlling 3-D reach and grasp kinematics.

Wireless, high-bandwidth recordings from non-human primate motor cortex using a scalable 16-Ch implantable microsystem

A multitude of neuroengineering challenges exist today in creating practical, chronic multichannel neural recording systems for primate research and human clinical application. Specifically, a) the persistent wired connections limit patient mobility from the recording system, b) the transfer of high bandwidth signals to external (even distant) electronics normally forces premature data reduction, and c) the chronic susceptibility to infection due to the percutaneous nature of the implants all severely hinder the success of neural prosthetic systems. Here we detail one approach to overcome these limitations: an entirely implantable, wirelessly communicating, integrated neural recording microsystem, dubbed the Brain Implantable Chip (BIC).

A bio-friendly and economical technique for chronic implantation of multiple microelectrode arrays

Many neurophysiological experiments on rodents and non-human primates involve the implantation of more than one multi-electrode array to record from many regions of the brain. So called 'floating' microelectrode arrays are implanted in cortical regions of interest and are coupled via a flexible cable to their connectors which are fixed to the skull by a cement cap or a titanium pedestal, such as the Cereport system, which has been approved for human use. The use of bone cement has several disadvantages including the creation of infection prone areas at the interface with the skull and surrounding skin. Alternatively, the more biocompatible Cereport has a limited carrying capacity and is far more expensive. In this paper, we describe a new implantation technique, which combines the biocompatibility of titanium, a high carrying capacity with a minimal skull footprint, and a decreased chance of infection, all in a relatively inexpensive package. This technique utilizes an in-house fabricated 'Nesting Platform' (NP), mounted on a titanium headpost to hold multiple connectors above the skin, making the headpost the only transcutaneous object. The use of delrin, a durable, lightweight and easily machinable material, allows easy customization of the NP for a wide variety of floating electrodes and their connectors. The ultimate result is a longer survival time with superior neural recordings that can potentially last longer than with traditional implantation techniques.

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

Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature's amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic "brain-interfaces" within the body, a point of special emphasis of this paper.