Wireless Neural Recording System Research Articles

Modeling and Characterization of Electrically Small Ultrawideband Antenna for Headstage-Based Wireless Neural Signal Recording System

Modeling and Characterization of Electrically Small Ultrawideband Antenna for Headstage-Based Wireless Neural Signal Recording System

Neural Dielet 2.0: A 128-Channel 2mm×2mm Battery-Free Neural Dielet Merging Simultaneous Multi-Channel Transmission through Multi-Carrier Orthogonal Backscatter.

Miniaturization of wireless neural-recording systems enables minimally-invasive surgery and alleviates the rejection reactions for implanted brain-computer interface (BCI) applications. Simultaneous massive-channel recording capability is essential to investigate the behaviors and inter-connections in billions of neurons. In recent years, battery-free techniques based on wireless power transfer (WPT) and backscatter communication have reduced the sizes of neural-recording implants by battery eliminating and antenna sharing. However, the existing battery-free chips realize the multi-channel merging in the signal-acquisition circuits, which leads to large chip area, signal attenuation, insufficient channel number or low bandwidth, etc. In this work, we demonstrate a 2mm×2mm battery-free neural dielet, which merges 128 channels in the wireless part. The neural dielet is fabricated with 65nm CMOS process, and measured results show that: 1) The proposed multi-carrier orthogonal backscatter technique achieves a high data rate of 20.16Mb/s and an energy efficiency of 0.8pJ/bit. 2) A self-calibrated direct digital converter (SC-DDC) is proposed to fit the 128 channels in the 2mm×2mm die, and then the all-digital implementation achieves 0.02mm2 area and 9.87μW power per channel.

Designing a Low-Power Single Stage Neural Amplifier for Efficient Wireless Neural Recording Systems

Designing a Low-Power Single Stage Neural Amplifier for Efficient Wireless Neural Recording Systems

Ab Initio Physics Considerations in the Design of Wireless and Non-Invasive Neural Recording Systems Using Magnetoelectric Nanoparticles

Ab Initio Physics Considerations in the Design of Wireless and Non-Invasive Neural Recording Systems Using Magnetoelectric Nanoparticles

A wearable group-synchronized EEG system for multi-subject brain-computer interfaces.

The multi-subject brain-computer interface (mBCI) is becoming a key tool for the analysis of group behaviors. It is necessary to adopt a neural recording system for collaborative brain signal acquisition, which is usually in the form of a fixed wire. In this study, we designed a wireless group-synchronized neural recording system that supports real-time mBCI and event-related potential (ERP) analysis. This system uses a wireless synchronizer to broadcast events to multiple wearable EEG amplifiers. The simultaneously received broadcast signals are marked in data packets to achieve real-time event correlation analysis of multiple targets in a group. To evaluate the performance of the proposed real-time group-synchronized neural recording system, we conducted collaborative signal sampling on 10 wireless mBCI devices. The average signal correlation reached 99.8%, the amplitude of average noise was 0.87 μV, and the average common mode rejection ratio (CMRR) reached 109.02 dB. The minimum synchronization error is 237 μs. We also tested the system in real-time processing of the steady-state visual-evoked potential (SSVEP) ranging from 8 to 15.8 Hz. Under 40 target stimulators, with 2 s data length, the average information transfer rate (ITR) reached 150 ± 20 bits/min, and the highest reached 260 bits/min, which was comparable to the marketing leading EEG system (the average: 150 ± 15 bits/min; the highest: 280 bits/min). The accuracy of target recognition in 2 s was 98%, similar to that of the Synamps2 (99%), but a higher signal-to-noise ratio (SNR) of 5.08 dB was achieved. We designed a group EEG cognitive experiment; to verify, this system can be used in noisy settings. The evaluation results revealed that the proposed real-time group-synchronized neural recording system is a high-performance tool for real-time mBCI research. It is an enabler for a wide range of future applications in collaborative intelligence, cognitive neurology, and rehabilitation.

Non-human primate models and systems for gait and neurophysiological analysis.

Brain-computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.

Miniaturized Fully Passive Wireless Neural Recording With Heterogeneous Integration in Thin Packages

Wireless fully-passive neural recording systems are demonstrated with heterogeneous integration of sensing, mixing, and communication components in flexible polymer dielectric films. The recording neurosensor has an antenna, antiparallel diode, and a by-pass capacitor to modulate an incoming carrier signal with the neuropotentials and backscatter the mixed signal to an external reader circuit. Planar antennas are realized on Liquid Crystal Polymer (LCP) flexible substrates with low dielectric constant. The designed antenna topology is realized with a single metal layer and eliminates the dependency on substrate thickness, and leads to thinner sensors. This is a major advance compared to prior passive neural recording with rigid and thicker substrates and components. Approximately 80% thickness reduction and 20% volume reduction are achieved with a similar performance when compared to earlier studies. Our approach thus leads to low-cost and disposable wireless neurosensors on a flexible platform with heterogeneous integration. This paper thus advances innovative wireless neural recording in three aspects: a)advanced packaging for miniaturized neural recording with passive telemetry, through which the overall size of the sensor is reduced without degrading the performance. b) new antenna topologies for neural recording applications, and c) detailed power link analysis to estimate the minimum signal sensitivity.

Neural Dielet: A 0.4mm 3 Battery-Less Crystal-Less Neural-Recording System on Die Achieving 1.6cm Backscatter Range with 2mm×2mm On-Chip Antenna.

Miniaturization is essential in the design of wireless neural-recording systems. In recent years, the battery in neural-recording systems can be eliminated by wireless power transfer (WPT), while antenna and crystal become two main bottlenecks to minimize a battery-less neural implant. In conventional battery-less designs, the miniaturization of antenna led to a short communication range, and a crystal-less clock suffered from noise issue or power-hungry circuits. In this work, we demonstrate a 0.4mm 3 neural dielet, which is a battery-less crystal-less neural-recording system on die (SoD) within a 2mm×2mm on-chip coil antenna. The communication range through the ultra-small antenna is extended by a proposed dither-based 3 rd-order intermodulation (IM3) technique, which prevents the backscatter communication from WPT blocker. Meanwhile, a dither-based 2 nd-order intermodulation (IM2) wireless-lock technique is proposed to remove the crystal. Measured results show that the SoD consumes 53.2 μ W power and achieves a wireless communication range of 1.6cm at a bit-error rate (BER) of 8 ×10-6, accompanied by simultaneous WPT for battery-less operation. In the animal experiment, the neural signal wirelessly recorded by our SoD in a battery-less way matches favorably with the wire-test results obtained by a commercial chip.

(D-Ser2) oxyntomodulin recovers hippocampal synaptic structure and theta rhythm in Alzheimer's disease transgenic mice.

In our previous studies, we have shown that (D-Ser2) oxyntomodulin (Oxm), a glucagon-like peptide 1 (GLP-1) receptor (GLP1R)/glucagon receptor (GCGR) dual agonist peptide, protects hippocampal neurons against Aβ1-42-induced cytotoxicity, and stabilizes the calcium homeostasis and mitochondrial membrane potential of hippocampal neurons. Additionally, we have demonstrated that (D-Ser2) Oxm improves cognitive decline and reduces the deposition of amyloid-beta in Alzheimer's disease model mice. However, the protective mechanism remains unclear. In this study, we showed that 2 weeks of intraperitoneal administration of (D-Ser2) Oxm ameliorated the working memory and fear memory impairments of 9-month-old 3×Tg Alzheimer's disease model mice. In addition, electrophysiological data recorded by a wireless multichannel neural recording system implanted in the hippocampal CA1 region showed that (D-Ser2) Oxm increased the power of the theta rhythm. In addition, (D-Ser2) Oxm treatment greatly increased the expression level of synaptic-associated proteins SYP and PSD-95 and increased the number of dendritic spines in 3×Tg Alzheimer's disease model mice. These findings suggest that (D-Ser2) Oxm improves the cognitive function of Alzheimer's disease transgenic mice by recovering hippocampal synaptic function and theta rhythm.

Representation of edges, head direction, and swimming kinematics in the brain of freely-navigating fish

Like most animals, the survival of fish depends on navigation in space. This capacity has been documented in behavioral studies that have revealed navigation strategies. However, little is known about how freely swimming fish represent space and locomotion in the brain to enable successful navigation. Using a wireless neural recording system, we measured the activity of single neurons in the goldfish lateral pallium, a brain region known to be involved in spatial memory and navigation, while the fish swam freely in a two-dimensional water tank. We found that cells in the lateral pallium of the goldfish encode the edges of the environment, the fish head direction, the fish swimming speed, and the fish swimming velocity-vector. This study sheds light on how information related to navigation is represented in the brain of fish and addresses the fundamental question of the neural basis of navigation in this group of vertebrates.

An Inductively-Powered Wireless Neural Recording and Stimulation System for Freely-Behaving Animals.

An inductively-powered wireless integrated neural recording and stimulation (WINeRS-8) system-on-a-chip (SoC) that is compatible with the EnerCage-HC2 for wireless/battery-less operation has been presented for neuroscience experiments on freely behaving animals. WINeRS-8 includes a 32-ch recording analog front end, a 4-ch current-controlled stimulator, and a 434 MHz on - off keying data link to an external software- defined radio wideband receiver (Rx). The headstage also has a bluetooth low energy link for controlling the SoC. WINeRS-8/EnerCage-HC2 systems form a bidirectional wireless and battery-less neural interface within a standard homecage, which can support longitudinal experiments in an enriched environment. Both systems were verified in vivo on rat animal model, and the recorded signals were compared with hardwired and battery-powered recording results. Realtime stimulation and recording verified the system's potential for bidirectional neural interfacing within the homecage, while continuously delivering 35mW to the hybrid WINeRS-8 headstage over an unlimited period.

An Implantable Peripheral Nerve Recording and Stimulation System for Experiments on Freely Moving Animal Subjects

A new study with rat sciatic nerve model for peripheral nerve interfacing is presented using a fully-implanted inductively-powered recording and stimulation system in a wirelessly-powered standard homecage that allows animal subjects move freely within the homecage. The Wireless Implantable Neural Recording and Stimulation (WINeRS) system offers 32-channel peripheral nerve recording and 4-channel current-controlled stimulation capabilities in a 3 × 1.5 × 0.5 cm3 package. A bi-directional data link is established by on-off keying pulse-position modulation (OOK-PPM) in near field for narrow-band downlink and 433 MHz OOK for wideband uplink. An external wideband receiver is designed by adopting a commercial software defined radio (SDR) for a robust wideband data acquisition on a PC. The WINeRS-8 prototypes in two forms of battery-powered headstage and wirelessly-powered implant are validated in vivo, and compared with a commercial system. In the animal study, evoked compound action potentials were recorded to verify the stimulation and recording capabilities of the WINeRS-8 system with 32-ch penetrating and 4-ch cuff electrodes on the sciatic nerve of awake freely-behaving rats. Compared to the conventional battery-powered system, WINeRS can be used in closed-loop recording and stimulation experiments over extended periods without adding the burden of carrying batteries on the animal subject or interrupting the experiment.

A battery-less 15 Mbps data transmission in wireless neural signal recording system for BMI

A battery-less 15 Mbps data transmission in wireless neural signal recording system for BMI

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 130nm CMOS to achieve 0.75μW power and 0.023mm2 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.

Toward a distributed free-floating wireless implantable neural recording system.

To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

A 64-Channel 965- <inline-formula> <tex-math notation="LaTeX">$\mu\text{W}$</tex-math> </inline-formula> Neural Recording SoC With UWB Wireless Transmission in 130-nm CMOS

This brief presents a 64-channel neural recording system-on-chip (SoC) with a 20-Mb/s wireless telemetry. Each channel of the analog front end consists of a low-noise bandpass amplifier, featuring a noise efficiency factor of 3.11 with an input-referred noise of 5.6 $\mu\text{V}_{\mathrm{rms}}$ in a 0.001- to 10-kHz band and a 31.25-kSps 6-fJ/conversion-step 10-bit SAR analog-to-digital converter. The recorded signals are multiplexed in the digital domain and transmitted via an 11.7% efficiency pulse-position modulation ultrawideband transmitter, reaching a transmission range in excess of 7.5 m. The chip has been fabricated in a 130-nm CMOS process, measures 25 mm2, and dissipates 965 $\mu\text{W}$ from a 0.5-V supply. This SoC features the lowest power per channel (15 $\mu\text{W}$ ) and the lowest energy per bit (48.2 pJ) among state-of-the-art wireless neural recording systems with a number of channels larger than 32. The proposed circuit is able to transmit the raw neural signal in a large bandwidth (up to 10 kHz) without performing any data compression or losing vital information, such as local field potentials.

A digital multichannel neural signal processing system using compressed sensing

This paper concerns a wireless multichannel neural recording system using a compressed sensing technique to compress the recorded data. We put forth a single and a multichannel system applying a Minimum Euclidean or Manhattan Distance Cluster-based (MDC) deterministic compressed sensing matrix. The single-channel signal processing system is composed of spike detection and data compression blocks. For the construction of the MDC matrix, the distance σ is an important parameter, which can take a value of 4 or 5. In addition, the sharing strategy is used to construct a multichannel system, and we analyze the influence of the number of the channels; scan rate on the reconstruction error, compression rate and power consumption; the influence of the signal-to-noise ratio; and reconstruction performance on neural signals. Based on the results, a 256-channel digital signal processing system, implemented in a 130-nm CMOS process, is proposed. This system has power consumption per channel of 12.5 μW and silicon area per channel of 0.03 mm2, and provides data reduction of around 90% while enabling accurate reconstruction of the original signals.

An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End.

An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the DSCS-AFE produces a pseudo-digital pulse width modulated (PWM) signal. A circular shift register (CSR) time division multiplexes (TDM) the PWM pulses to create a TDM-PWM signal, which is fed into an on-chip 915 MHz transmitter (Tx). The AFE and Tx are supplied at 1.8 V and 4.2 V, respectively, by a power management block, which includes a high efficiency active rectifier and automatic resonance tuning (ART), operating at 13.56 MHz. The 8-ch system-on-a-chip (SoC) was fabricated in a 0.35-μm CMOS process, occupying 5.0 × 2.5 mm2 and consumed 51.4 mW. For each channel, the sampling rate is 21.48 kHz and the power consumption is 19.3 μW. In vivo experiments were conducted on freely behaving rats in an energized homecage by continuously delivering 51.4 mW to the WINeR-7 system in a closed-loop fashion and recording local field potentials (LFP).

24 Channel dual-band wireless neural recorder with activity-dependent power consumption

A complete light weight 24 channel wireless neural recording system enabled by a custom IC is presented. By transmitting only the action potential (AP) data, the system power consumption is scaled based on the activity of the neural signal. By detecting an AP event prior to digitization, the ASIC can reduce the power consumed in all subsequent stages and yet conserves the entire AP shape without affecting the classification performance. The complete 2.5 cm3 head stage weighing below 7 g can operate for 3 h. The ASIC achieves 70 dB CMRR.

Neural signal compression using a minimum Euclidean or Manhattan distance cluster-based deterministic compressed sensing matrix

Multichannel wireless neural signal recording systems are a prominent topic in biomedical research, but because of several limitations, such as power consumption, the device size, and enormous quantities of data, it is necessary to compress the recorded data. Compressed sensing theory can be employed to compress neural signals. However, a neural signal is usually not sparse in the time domain and contains a large number of similar non-zero points. In this article, we propose a new method for compressing not only a sparse signal but also a non-sparse signal that has identical points. First, several concepts about the identical items of the signal are introduced; thus, a method for constructing the Minimum Euclidean or Manhattan Distance Cluster-based (MDC) deterministic compressed sensing matrix is given. Moreover, the Restricted Isometry Property of the MDC matrix is supported. Third, three groups of real neural signals are used for validation. Six different random or deterministic sensing matrices under diverse reconstruction algorithms are used for the simulation. From the simulation results, it can be demonstrated that the MDC matrix can largely compress neural signals and also have a small reconstruction error. For a six-thousand-point signal, the compression rate can be up to 98%, whereas the reconstruction error is less than 0.1. In addition, from the simulation results, the MDC matrix is optimal for a signal that has an extended length. Finally, the MDC matrix can be constructed by zeros and ones; additionally, it has a simple construction structure that is highly practicable for the design of an implantable neural recording device.