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Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based technique which may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performing different tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPs with good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUA and MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate that LFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spike relationship and for the development of LFP-based BMIs.

Neuromodulation is an established treatment for numerous neurological conditions, but to expand the therapeutic scope there is a need to improve the spatial, temporal and cell-type specificity of stimulation. Optogenetics is a promising area of current research, enabling optical stimulation of genetically-defined cell types without interfering with concurrent electrical recording for closed-loop control of neural activity. We are developing an open-source system to provide a platform for closed-loop optogenetic neuromodulation, incorporating custom integrated circuitry for recording and stimulation, real-time closed-loop algorithms running on a microcontroller and experimental control via a PC interface. We include commercial components to validate performance, with the ultimate aim of translating this approach to humans. In the meantime our system is flexible and expandable for use in a variety of preclinical neuroscientific applications. The platform consists of a Controlling Abnormal Network Dynamics using Optogenetics (CANDO) Control System (CS) that interfaces with up to four CANDO headstages responsible for electrical recording and optical stimulation through custom CANDO LED optrodes. Control of the hardware, inbuilt algorithms and data acquisition is enabled via the CANDO GUI (Graphical User Interface). Here we describe the design and implementation of this system, and demonstrate how it can be used to modulate neuronal oscillations in vitro and in vivo.

The introduction of large-scale chemical sensing systems in CMOS which integrate millions of ISFET sensors have allowed applications such as DNA sequencing and fine-pixel chemical imaging systems to be realised. Using CMOS ISFETs provides advantages of digitisation directly at the sensor as well as correcting for non-linearity in its response. However, for this to be beneficial and scale, the readout circuits need to have the minimum possible footprint and power consumption. Within this context, this paper analyses an ISFET based pH-to-time readout using an inverter in the time-domain as a level-crossing detector and presents a 32 × 32 array with in-pixel digitisation for pH sensing. The inverter-based sensing pixel, controlled by a triangular waveform, converts the pH response into a time-domain signal whilst also compensating for sensor offset and thus resulting in an increase in dynamic range. The sensor pixels interface to a 15-bit asynchronous column-wise time-to-digital converter (TDC), enabling fast asynchronous conversion whilst using minimal silicon area. Parallel outputs of 32 TDC interfaces are serialised to achieve fast data throughput. This system is implemented in a standard 0.18 $\,\mu$ m CMOS technology, with a pixel size of 26 $\mu$ m × 26 $\mu$ m and a TDC area of 26 $\mu$ m × 180 $\mu$ m. Additionally, we investigate the use of additional offset compensation by having half of the array implemented with the floating gate tied down via a well diode. Measured results demonstrate the system is able to sense reliably with an average pH sensitivity of 30 mV/pH, whilst being able to compensate for sensor offset by up to $\pm$ 7 V. A resolution of 0.013 pH is achieved and noise measurements show an integrated noise of 0.08 pH within 2–500 Hz and SFDR of 42.6 dB. The total power consumption of the system is measured to be 11.286 mW when operating at a high frame rate of 1 KFPS.

The lack of open-source memory compilers in academia typically causes significant delays in research and design implementations. This paper presents an open-source memory compiler that is directly integrated within the Cadence Virtuoso environment using physical verification tools provided by Mentor Graphics (Calibre). It facilitates the entire memory generation process from netlist generation to layout implementation, and physical implementation verification. To the best of our knowledge, this is the first open-source memory compiler that has been developed specifically to automate Resistive Random Access Memory (RRAM) generation. RRAM holds the promise of achieving high speed, high density and non-volatility. A novel RRAM architecture, additionally is proposed, and a number of generated RRAM arrays are evaluated to identify their worst case control line parasitics and worst case settling time across the memristors of their cells. The total capacitance of lines SEL, N and P is 5.83 fF/cell, 3.31 fF/cell and 2.48 fF/cell respectively, while the total calculated resistance for SEL is 1.28 Ohm/cell and 0.14 Ohm/cell for both N and P lines., 4 pages

This paper describes Tiresias, a low-cost, unobtrusive networked radar system designed to monitor vulnerable patients in domestic environments and provide high quality behavioural and health data. Dementia is a disease that affects millions worldwide and progressively degrades an individual's ability to care for themselves. Eventually most people living with dementia will need to reside in assisted living facilities as they become unable to care for themselves. Understanding the effects dementia has on ability to self-care and extending the length of time people living with dementia can remain living independently are key goals of dementia research and care. The networked radar system proposed in this paper is designed to provide high quality behavioural and health data from domestic environments. This is achieved using multiple radar sensors networked together with their data outputs integrated and processed to produce high confidence measures of position and movement. It is hoped the data produced by this system will both provide insights into how dementia progresses, and also help monitor vulnerable individuals in their own homes, allowing them to remain independent longer than would otherwise be possible.

This paper describes preliminary work towards an automated algorithm for labelling Neuropixel data that exploits the fact that adjacent recording sites are spatially oversampled. This is achieved by combining classical single channel spike sorting with spatial spike grouping, resulting in an improvement in both accuracy and robustness. This is additionally complemented by an automated method for channel selection that determines which channels contain high quality data. The algorithm has been applied to a freely accessible dataset, produced by Cortex Lab, UCL. This has been evaluated to have a accuracy of over 77% compared to a manually curated ground truth.

Here we report on a novel optoelectronic architecture capable to add in-phase the current pulses generated by each one of the fast Si photodiodes forming an array illuminated by laser pulses. The aim is to increase the total photodiode sensitive area to achieve higher output pulsed current values by maintaining unaltered the response time, the bandwidth and the reverse bias voltage proper of each single photodiode. This result is important for many modern applications in biophysics and biomedicine like the brain machine interfaces, that require real time evaluation of environmental/sample changes through the acquisition of signals at high data rates under high signal-to-noise ratio conditions. As a consequence, these applications use sub-nanosecond laser pulses that are revealed by large bandwidth photodiodes having small sensitive area to attain low values of the internal junction capacitance. Thus, any small optical misalignment between the laser beam and the photodiode sensitive area in transcutaneous optical biotelemetry strongly decreases the system detection efficiency and performances. The proposed optoelectronic system resolves this problem being capable to sum in-phase the current pulses generated by each one of the photodiodes forming the array into a single current pulse maintaining the condition of fast response in terms of rise and fall times and bandwidth. This solution employs a Kirchhoff node to sum the different photocurrents, a decoupling current buffer and a transimpedance amplifier to amplify the current pulses summed at the node. We investigated the performances of the optoelectronic system by using photodiodes with different sensitive areas and junction capacitances (i.e., different bandwidths and rise and fall times) for laser pulses repetition rates up to 200 MHz. We also experimentally characterized the circuitry composed by an array of 4 fast photodiodes by using 800 ps laser pulses at a repetition rate of 200 MHz proving that the achieved response times and bandwidth remain the same of those ones of each single photodiode. Moreover, we demonstrated that the maximum value (i.e., the peak) of the obtained output current pulses is multiplied by a factor 4, i.e., equal to the number of the photodiodes forming the array having an overall sensitive area enhanced by the same factor.

Chronic in-vivo neurophysiology experiments require highly miniaturized, remotely powered multi-channel neural interfaces which are currently lacking in power or flexibility post implantation. In this article, to resolve this problem we present the SenseBack system, a post-implantation reprogrammable wireless 32-channel bidirectional neural interfacing that can enable chronic peripheral electrophysiology experiments in freely behaving small animals. The large number of channels for a peripheral neural interface, coupled with fully implantable hardware and complete software flexibility enable complex in-vivo studies where the system can adapt to evolving study needs as they arise. In complementary ex-vivo and in-vivo preparations, we demonstrate that this system can record neural signals and perform high-voltage, bipolar stimulation on any channel. In addition, we demonstrate transcutaneous power delivery and Bluetooth 5 data communication with a PC. The SenseBack system is capable of stimulation on any channel with p20 V of compliance and up to 315 μA of current, and highly configurable recording with per-channel adjustable gain and filtering with 8 sets of 10-bit ADCs to sample data at 20 kHz for each channel. To the best of our knowledge this is the first such implantable research platform offering this level of performance and flexibility post-implantation (including complete reprogramming even after encapsulation) for small animal electrophysiology. Here we present initial acute trials, demonstrations and progress towards a system that we expect to enable a wide range of electrophysiology experiments in freely behaving animals.

This paper investigates to what extent Long Short-Term Memory (LSTM) decoders can use Local Field Potentials (LFPs) to predict Single-Unit Activity (SUA) in Macaque Primary Motor cortex. The motivation is to determine to what degree the LFP signal can be used as a proxy for SUA, for both neuroscience and Brain-Computer Interface (BCI) applications. Firstly, the results suggest that the prediction quality varies significantly by implant location or animal. However, within each implant location / animal, the prediction quality seems to be correlated with the amount of power in certain LFP frequency bands (0-10, 10-20 and 40-50Hz, standardised LFPs). Secondly, the results suggest that bipolar LFPs are more informative as to SUA than unipolar LFPs. This suggests common mode rejection aids in the elimination of non-local neural information. Thirdly, the best individual bipolar LFPs generally perform better than when using all available unipolar LFPs. This suggests that LFP channel selection may be a simple but effective means of lossy data compression in Wireless Intracortical LFP-based BCIs. Overall, LFPs were moderately predictive of SUA, and improvements can likely be made.

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for extracting firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose Bayesian adaptive kernel smoother (BAKS) as the feature extraction method and long short-term memory (LSTM)-based deep learning as the decoding algorithm. We evaluated the proposed methods for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the motor cortex of a monkey. Experimental results showed that BAKS coupled with LSTM outperformed other combinations of feature extraction method (binning or fixed kernel smoother) and decoding algorithm (Kalman filter or Wiener filter). Overall results demonstrate the effectiveness of BAKS and LSTM for improving the decoding performance of MUA-based BMIs.

ObjectiveThere has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs.ApproachTo address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks.Main resultsExperimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions.SignificanceOverall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.

Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Understanding the relationship between these two signals is essential for gaining deeper insight into neuronal coding and information processing in the brain and is also relevant to brain-machine interface (BMI) research. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. These spiking activities that are typically extracted via threshold-based technique may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another spiking activity in the form of a continuous signal, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by employing a deep learning method to infer ESA from LFPs intracortically recorded from the motor cortex area of two monkeys performing different tasks. Results from long-term recording sessions and across different tasks revealed that the inference accuracy of ESA yielded consistently and significantly higher accuracy than that of SUA and MUA. In addition, local motor potential (LMP) was found to be the most highly predictive feature compared to other LFP features. The overall results indicate that LFPs contain substantial information about the spikes, particularly ESA, which could be useful for the development of LFP-based BMIs. The results also suggest the potential use of ESA as an alternative neuronal population activity measure for analysing neural responses to stimuli or behavioural tasks.

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.

Next generation implantable neural interfaces are targeting devices with mm-scale form factors that are freely floating and completely wireless. Scalability to more recording (or stimulation) channels will be achieved through distributing multiple devices, instead of the current approach that uses a single centralized implant wired to individual electrodes or arrays. In this way, challenges associated with tethers, micromotion, and reliability of wiring is mitigated. This concept is now being applied to both central and peripheral nervous system interfaces. One key requirement, however, is to maximize specific absorption rate (SAR) constrained achievable wireless power transfer efficiency (PTE) of these inductive links with mm-sized receivers. Chip-scale coil structures for microsystem integration that can provide efficient near-field coupling are investigated. We develop near-optimal geometries for three specific coil structures: in-CMOS, above-CMOS (planar coil post-fabricated on a substrate), and around-CMOS (helical wirewound coil around substrate). We develop analytical and simulation models that have been validated in air and biological tissues by fabrications and experimental measurements. Specifically, we prototype structures that are constrained to a 4 mm $ \times$ 4 mm silicon substrate, i.e., the planar in-/above-CMOS coils have outer diameters $ 4 mm, whereas the around-CMOS coil has an inner diameter of 4 mm. The in-CMOS and above-CMOS coils have metal film thicknesses of 3- $\mu$ m aluminium and 25- $\mu$ m gold, respectively, whereas the around-CMOS coil is fabricated by winding a 25- $\mu$ m gold bonding wire around the substrate. The measured quality factors ( Q ) of the mm-scale Rx coils are 10.5 @450.3 MHz (in-CMOS), 24.61 @85 MHz (above-CMOS), and 26.23 @283 MHz (around-CMOS). Also, PTE of 2-coil links based on three types of chip-scale coils is measured in air and tissue environment to demonstrate tissue loss for bio-implants. The SAR-constrained maximum PTE measured (together with resonant frequencies, in tissue) are 1.64% @355.8 MHz (in-CMOS), 2.09% @82.9 MHz (above-CMOS), and 3.05% @318.8 MHz (around-CMOS).

The instrumentation systems for implantable brain–machine interfaces represent one of the most demanding applications for ultra low-power analogue-to-digital-converters (ADC) to date. To address this challenge, this paper proposes a $\Delta \Sigma $ SAR topology for very large sensor arrays that allows an exceptional reduction in silicon footprint by using a continuous time 0–2MASH topology. This configuration uses a specialized FIR window to decimate the $\Delta \Sigma $ modulator output and reject mismatch errors from the SAR quantizer, which mitigates the overhead from dynamic element matching techniques commonly used to achieve high precision. A fully differential prototype was fabricated using $0.18\,\mu $ m CMOS to demonstrate 10.8 ENOB precision with a 0.016 mm2 silicon footprint. Moreover, a 14fJ/conv figure-of-merit can be achieved, while resolving signals with the maximum input amplitude of ±1.2 Vpp sampled at 200 kS/s. The ADC topology exhibits a number of promising characteristics for both high speed and ultra low-power systems due to the reduced complexity, switching noise, sampling load, and oversampling ratio, which are critical parameters for many sensor applications.

Objective: This paper describes the design, testing and use of a novel multichannel block-capable stimulator for acute neurophysiology experiments to study highly selective neural interfacing techniques. This paper demonstrates the stimulator's ability to excite and inhibit nerve activity in the rat sciatic nerve model concurrently using monophasic and biphasic nerve stimulation as well as high-frequency alternating current (HFAC). Approach: The proposed stimulator uses a Howland Current Pump circuit as the main analogue stimulator element. 4 current output channels with a common return path were implemented on printed circuit board using Commercial Off-The-Shelf components. Programmable operation is carried out by an ARM Cortex-M4 Microcontroller on the Freescale freedom development platform (K64F). Main Results: This stimulator design achieves +-10 mA of output current with +-15V of compliance and less than 6 uA of resolution using a quad-channel 12-bit external DAC, for four independently driven channels. This allows the stimulator to carry out both excitatory and inhibitory (HFAC block) stimulation. DC Output impedance is above 1 Mohm. Overall cost is less than USD 450 or GBP 350 and device size is approximately 9 cm x 6 cm x 5 cm. Significance: Experimental neurophysiology often requires significant investment in bulky equipment for specific stimulation requirements, especially when using HFAC block. Different stimulators have limited means of communicating with each other, making protocols more complicated. This device provides an effective solution for multi-channel stimulation and block of nerves, enabling studies on selective neural interfacing in acute scenarios with an affordable, portable and space-saving design for the laboratory. The stimulator can be further upgraded with additional modules to extend functionality while maintaining straightforward programming and integration of functions with one controller.

Heart rate variability (HRV), as measured by ultra-wideband (UWB) radar, enables contactless monitoring of physiological functioning in the human body. In the current study, we verified the reliability of HRV extraction from radar data, under limited transmitter power. In addition, we conducted a feasibility study of mental state classification from HRV data, measured using radar. Specifically, arctangent demodulation with calibration and low rank approximation have been used for radar signal pre-processing. An adaptive continuous wavelet filter and moving average filter were utilized for HRV extraction. For the mental state classification task, performance of support vector machine, k-nearest neighbors and random forest classifiers have been compared. The developed system has been validated on human participants, with 10 participants for HRV extraction, and three participants for the proof-of-concept mental state classification study. The results of HRV extraction demonstrate the reliability of time-domain parameter extraction from radar data. However, frequency-domain HRV parameters proved to be unreliable under low SNR. The best average overall mental state classification accuracy achieved was 82.34%, which has important implications for the feasibility of mental health monitoring using UWB radar.

Designing systems with many recording channels is a major challenge in brain-machine interfaces. Power, bandwidth, and size requirements impose tight design constraints for implementing the required processing within an acceptable latency and battery life. Moreover, the variety of brain decoding algorithms require highly versatile systems that can be rapidly adapted to execute different tasks from experiment to experiment as for example microcontrollers (MCUs). However, state of the art MCUs lack of performance and consume too much power to be used as generic platforms for neural decoding applications. To overcome the aforementioned limitations, this paper presents an MCU-based system consisting of a 64-channel event-based neural interface and a Parallel Ultra-Low-Power (PULP) platform that acquires and processes the neural activity. The flexibility of the system (called Neuro-PULP) has been demonstrated through the deployment of two applications: one compressing raw data in streaming mode for wireless transmission, and one generating the cluster and time-stamp of detected spikes, leveraging a low-power event-mode. The event-based approach, coupled with the energy efficiency of the PULP architecture, leads to a more than 4x improvement in energy efficiency with respect to state of the art systems based on FPGAs, leading to the average power consumption of $114 \mu \mathrm {W} /$channel, yet retaining the flexibility of fully programmable processor-based architectures.

The recent evolution of implantable medical devices from single-unit stimulators to modern implantable microsystems, has driven the need for distributed technologies, in which both the implant system and functions are partitioned across multiple active devices. This multi-module approach is made possible thanks to novel network architectures, allowing for in-body power and data communications to be performed using implantable leads. This paper discusses the challenges in implementing such interfacing system and presents a platform based on one central implant (CI) and multiple peripheral implants (PIs) using a custom 4WiCS communication protocol. This is implemented in PCB technology and tested to demonstrate intrabody communication capabilities and power transfer within the network. Measured results show CI-to-PI power delivery achieves 70 % efficiency in expected load condition, while establishing full-duplex data link with up to 4 PIs simultaneously.

This paper presents a robust EM-lens-enhanced wireless power transmission system and a novel multiple-node data communication method for distributed implantable medical devices. The proposed techniques can solve the common issues caused by multiple implanted devices, such as low power transfer efficiency through biological tissues, uneven delivered power for distributed devices, interference between simultaneous wireless power and data transmission, and intermodulation distortion between various data channels. A prototype system has been designed and manufactured with discrete components on FR4 substrate as a proof of concept. The EM-Lens-enhanced inductive links can expand the power coverage of transmitting (Tx) coil from 9mm×5mm to 14mm×13mm, and double the recovered DC voltage from 1.8V to 3.2V at 12.5mm distance. Data communication is achieved by novel low-power backscatter-CDMA scheme. This permits transmission of data from several nodes all operating with the same carrier frequency simultaneously reflecting the power carriers to the primary side. In this paper, we demonstrate simultaneous communication between two nodes at 125kbps with 1.05mW power consumption.

Neural interfaces, and more specifically ones of the invasive/implantable variety, today are a topic of much controversy, often making the general public uncomfortable and intimidated. We have thus devised a bespoke interactive demo to help people understand brain implants and their need in the age of wearable devices, with the secondary objective of introducing the wireless cortical neural probe that we, at NGNI (Next Generation Neural Interfaces) lab, are developing.

In recent years, local field potentials (LFPs) have emerged as a promising alternative input signal for brain-machine interfaces (BMIs). Several studies have demonstrated that LFP-based BMIs could provide long-term recording stability and, at the same time, comparable decoding performance to their spike counterparts. However, despite the compelling results, most LFP-based BMIs still make use of hand-crafted features which is a time-consuming process and can be suboptimal. In this paper, we propose an end-to-end system approach based on temporal convolutional network (TCN) to automatically extract features and decode kinematics of hand movements directly from raw LFP signals. We benchmark its decoding performance against traditional approaches incorporating long short-term memory (LSTM) decoders driven by hand-crafted LFP features. Experimental results demonstrate significant performance improvement of the proposed approach compared to the traditional approaches, demonstrating the suitability and the potential of TCN-based end-to-end systems in providing stable and high decoding performance LFP-based BMIs.

This paper presents an oscillator based potentiostat with switch-cap feedback for current sensing applications. In this work, a sigma-delta modulator is realized using current as input and charge as the feedback. The current input is integrated at the input capacitance and the voltage difference between the integrated value and reference input is converted to current via a transconductor. This current is then fed into a current controlled ring oscillator and the frequency output drives the switch-cap circuits to compensate the input current. Therefore, the current input can be directly readout as frequency deviation, or can be further converted to digital output via a frequency to digital converter. The proposed system is implemented in a typical 0.18µm CMOS technology, with total area of 80 × 250µm2. A differential structure was implemented to minimize the parasitic and kick back influence, with oscillator base frequency at 491MHz. A counter based frequency to digital converter with an additional CIC filter was implemented to convert the differential frequency signal to digital domains at 16MHz sampling frequency. Simulation results demonstrated that a dynamic range of 52dB was achieved with input range of ±2.5µA.

This paper presents a 32×32 ISFET sensing array with in-pixel digitisation for pH sensing. The in-pixel digitisation is achieved using an inverter-based sensing pixel that is controlled by a triangular waveform. This converts the pH response of the ISFET into a time-domain signal whilst also increasing dynamic range and thus the ability to tolerate sensor offset. The pixels are interfaced to a 15-bit asynchronous column-wise time-to-digital converter (TDC), enabling fast sensor readout whilst using minimal silicon area. Parallel output of 32 TDC interfaces are serialised to achieve fast data though-put. This system is implemented in a standard 0.18 μm standard CMOS technology, with a pixel size of 26 μm × 26 μm and a TDC of 26 μm × 180 μm. Simulation results demonstrate that chemical sampling of up to 5k frames per second can be achieved with a clock frequency of 160 MHz and a TDC resolution of 190 ps. The total power consumption of the overall system is 7.34 mW.

Objective. Brain–machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs. Approach. We propose entire spiking activity (ESA)—an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique—as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks. Main results. Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long-term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data. Significance. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

This paper reports a novel medication release and delivery mechanism as part of a next generation wireless capsule endoscope (WCE) for targeted drug delivery. This subsystem occupies a volume of only 17.9mm3 for the purpose of delivering a 1 ml payload to a target site of interest in the small intestinal tract. An in-depth analysis of the method employed to release and deliver the medication is described and a series of experiments is presented which validates the drug delivery system. The results show that a variable pitch conical compression spring manufactured from stainless steel can deliver 0.59 N when it is fully compressed and that this would be sufficient force to deliver the onboard medication.

Objective: This paper describes the design, testing and use of a novel multichannel block-capable stimulator for acute neurophysiology experiments to study highly selective neural interfacing techniques. This paper demonstrates the stimulator's ability to excite and inhibit nerve activity in the rat sciatic nerve model concurrently using monophasic and biphasic nerve stimulation as well as high-frequency alternating current (HFAC). Approach: The proposed stimulator uses a Howland Current Pump circuit as the main analogue stimulator element. 4 current output channels with a common return path were implemented on printed circuit board using Commercial Off-The-Shelf components. Programmable operation is carried out by an ARM Cortex-M4 Microcontroller on the Freescale freedom development platform (K64F). Main Results: This stimulator design achieves +-10 mA of output current with +-15 V of compliance and less than 6 uA of resolution using a quad-channel 12-bit external DAC, for four independently driven channels. This allows the stimulator to carry out both excitatory and inhibitory (HFAC block) stimulation. DC Output impedance is above 1 Mohm. Overall cost for materials i.e. PCB boards and electronic components is less than USD 450 or GBP 350 and device size is approximately 9 cm x 6 cm x 5 cm. Significance: Experimental neurophysiology often requires significant investment in bulky equipment for specific stimulation requirements, especially when using HFAC block. Different stimulators have limited means of communicating with each other, making protocols more complicated. This device provides an effective solution for multi-channel stimulation and block of nerves, enabling studies on selective neural interfacing in acute scenarios with an affordable, portable and space-saving design for the laboratory. The stimulator can be further upgraded with additional modules to extend functionality while maintaining straightforward programming and integration of functions with one controller. Additionally, all source files including all code and PCB design files are freely available to the community to use and further develop.

This paper presents a novel wireless power transfer (WPT) scheme that consists of a two-tier hierarchy of near-field inductively coupled links. Key aims are to provide efficient power transfer efficiency (PTE) and uniform energy distribution for mm-scale free-positioned neural implants. The top tier facilitates a transcutaneous link from a scalp-worn (em-scale) primary coil to a subcutaneous array of smaller, parallel-connected secondary coils. These are then wired through the skull to a corresponding set of parallel connected primary coils in the lower tier, placed epidurally. These then inductively couple to freely positioned (mm-scale) secondary coils within each subdural implant. This architecture has three key advantages: (1) the opportunity to achieve efficient energy transfer by utilising two short-distance inductive links; (2) good uniformity of the transdural power distribution through the multiple (redundant) coils; and (3) a reduced risk of infection by maintaining the dura protecting the blood-brain barrier. The functionality of this approach has been verified and optimised through HFSS simulations, to demonstrate the robustness against positional and angular misalignment. The average 11.9% PTE and 26.6% power distribution deviation (PDD) have been achieved for the Rx coil in parallel with the epidural coil array. The average PTE and PDD degenerate to 2.6% and 62.8% when the Rx coil is perpendicular to the epidural coil array.

Recent developments in the design of active im- plantable devices have achieved significant advances, for example, an increased number of recording channels, but too often practical clinical applications are restricted by device longevity. It is important however to complement efforts for increased func- tionality with translational work to develop implant technologies that are safe and reliable to be hosted inside the human body over long periods of time. This paper first examines techniques currently used to evaluate micro-package hermeticity and key challenges, highlighting the need for new, in situ instrumentation that can monitor the encapsulation status over time. Two novel circuits are then proposed to tackle the specific issue of moisture penetration inside a sub-mm, silicon-based package. They both share the use of metal tracks on the different layers of the CMOS stack to measure changes in impedance caused by moisture present in leak cracks or diffused into the oxide layers.

This paper reports on the implementation and characterisation of a thermally controlled device for in vitro biomedical applications, based on standard Printed Circuit Board (PCB) technology. This is proposed as a low cost alternative to state-of-the-art microfluidic devices and Lab-on-Chip (LoC) platforms, which we refer to as the thermal Lab-on-PCB concept. In total, six different prototype boards have been manufactured to implement as many mini-hotplate arrays. 3D multiphysics software simulations show the thermal response of the modelled mini-hotplate boards to electrical current stimulation, highlight- ing their versatile heating capability. A comparison with the results obtained by the characterisation of the fabricated PCBs demonstrates the dual temperature sensing/heating property of the mini-hotplate, exploitable in a larger range of temperature with respect to the typical operating range of LoC devices. The thermal system is controllable by means of external off-the-shelf circuitry designed and implemented on a single-channel control board prototype.

Neuromodulation technologies are progressing from pacemaking and sensory operations to full closed-loop control. In particular, optogenetics—the genetic modification of light sensitivity into neural tissue allows for simultaneous optical stimulation and electronic recording. This paper presents a neural interface application-specified integrated circuit (ASIC) for intelligent optoelectronic probes. The architecture is designed to enable simultaneous optical neural stimulation and electronic recording. It provides four low noise (2.08 μ V $_{\text{rms}}$ ) recording channels optimized for recording local field potentials (LFPs) (0.1–300 Hz bandwidth, $\pm$ 5 mV range, sampled 10-bit@4 kHz), which are more stable for chronic applications. For stimulation, it provides six independently addressable optical driver circuits, which can provide both intensity (8-bit resolution across a 1.1 mA range) and pulse-width modulation for high-radiance light emitting diodes (LEDs). The system includes a fully digital interface using a serial peripheral interface (SPI) protocol to allow for use with embedded controllers. The SPI interface is embedded within a finite state machine (FSM), which implements a command interpreter that can send out LFP data whilst receiving instructions to control LED emission. The circuit has been implemented in a commercially available 0.35 μ m CMOS technology occupying a 1.95 mm $\times$ 1.10 mm footprint for mounting onto the head of a silicon probe. Measured results are given for a variety of bench-top, in vitro and in vivo experiments, quantifying system performance and also demonstrating concurrent recording and stimulation within relevant experimental models.

Objective Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain-machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation. Approach We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. Main results The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. Significance The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals-revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.

Next generation brain machine interfaces fundamentally need to improve the information transfer rate and chronic consistency when observing neural activity over a long period of time. Towards this aim, this paper presents a novel System-on-Chip (SoC) for a mm-scale wireless neural recording node that can be implanted in a distributed fashion. The proposed self-regulating architecture allows each implant to operate autonomously and adaptively load the electromagnetic field to extract a precise amount of power for full-system operation. This can allow for a large number of recording sites across multiple implants extending through cortical regions without increased control overhead in the external head-stage. By observing local field potentials (LFPs) only, chronic stability is improved and good coverage is achieved whilst reducing the spatial density of recording sites. The system features a ΔΣ based instrumentation circuit that digitises high fidelity signal features at the sensor interface thereby minimising analogue resource requirements while maintaining exceptional noise efficiency. This has been implemented in a 0.35 μm CMOS technology allowing for wafer-scale post-processing for integration of electrodes, RF coil, electronics and packaging within a 3D structure. The presented configuration will record LFPs from 8 electrodes with a 825 Hz bandwidth and an input referred noise figure of 1.77μVrms. The resulting electronics has a core area of 2.1 mm2 and a power budget of 92 μW

This paper describes an integrated communication system, implementing a UWB-inspired pulsed coding technique, for an optical transcutaneous biotelemetry. The system consists of both a transmitter and a receiver facilitating a bidirectional link. The transmitter includes a digital data coding circuit and is capable of generating sub-nanosecond current pulses and directly driving an off-chip semiconductor laser diode including all bias and drive circuits. The receiver includes an integrated compact PN-junction photodiode together with signal conditioning, de- tection and digital data decoding circuits to enable a high bit rate, energy efficient communication. The proposed solution has been implemented in a commercially available 0.35 μ m CMOS technology provided by AMS. The circuit core occupies a compact silicon footprint of less than 0.13 mm 2 (only 113 transistors and 1 resistor). Post-layout simulations have validated the overall system operation demonstrating the ability to operate at bit rates up to 500 Mbps with pulse widths of 300 ps with a total power efficiency (transmitter + receiver) lower than 74 pJ/bit. This makes the system ideally suited for demanding applications that require high bit rates at extremely low energy levels. One such application is implantable brain machine interfaces requiring high uplink bitrates to transmit recorded data externally through a transcutaneous communication channel.

We present a finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT). By simulating only the end of the two arms of the interferometer and computing the interference signal in post processing, it is possible to reduce the computational time required by the simulations and, thus, to simulate much bigger environments. Moreover, it is possible to simulate successive A lines and thus obtaining a cross section of the sample considered. In this paper we present the model applied to two different samples: a glass rod filled with water-sucrose solution at different concentrations and a peripheral nerve. This work demonstrates the feasibility of using OCT for non-invasive, direct optical monitoring of peripheral nerve activity, which is a long-sought goal of neuroscience.

A closed-loop optogenetic system for curing epilepsy is presented in this work. As it shown at figure 1, the system consists of a cortical brain implant with LEDs and recording electrodes, a customer designed CMOS chip[1][2][3] and a controller. The brain activities are recorded by the implant with recording electronics in a CMOS chip, the signals are processed by the controller, and the results are send back to the CMOS chip for delivering LED stimulation commands.

Next generation brain machine interfaces are targeting millimeter-scale implants that are freely floating and completely wireless. It is essential these systems achieve good power transmission efficiency but are also compatible with microsystem technologies. This paper presents two schemes for implementing mm-scale coils for power delivery by electromagnetic coupling — on-chip and wire-wound. A set of on-chip coils have been fabricated using a 0.35 μm CMOS technology with thick top metal option (3 μm aluminium). These achieve a maximum Q-factor of 16.37. The second approach describes wire-wound coils that have been fabricated using bondwire (25 μm gold), achieving a Q-factor of 24.54. This work develops the relevant analytical models, equivalent simulation models, and reports results using both finite element modeling (simulation) and experimental measurement of the fabricated devices. Finally, we compare results and discuss the relative merits of each approach.

Emerging applications for implantable devices are requiring multi-unit systems with intrabody transmission of power and data through wireline interfaces. This paper proposes a novel method for power delivery within such a configuration that makes use of closed loop dynamic regulation. This is implemented for an implantable application requiring a single master and multiple identical slave devices utilising a parallel-connected 4-wire interface. The power regulation is achieved within the master unit through closed loop monitoring of the current consumption to the wired link. Simultaneous power transfer and full-duplex data communication is achieved by superimposing the power carrier and downlink data over two wires and uplink data over a second pair of wires. Measured results using a fully isolated (AC coupled) 4-wire lead, demonstrate this implementation can transmit up to 120 mW of power at 6 V (at the slave device, after eliminating any losses). The master device has a maximum efficiency of 80 % including a dominant dynamic power loss. A 6 V constant supply at the slave device is recovered 1.5 ms after a step of 22 mA.

This paper proposes a novel method for integrating CMOS microelectronics with microwire-based electrodes for next generation implantable brain machine interfaces. There is strong evidence to suggest that microwire-based electrodes outperform micromachined and polymer-based electrodes in terms of signal integrity and chronic viability. Furthermore, it has been shown that the recording of Local Field Potentials (LFPs) is more robust to tissue damage and scar tissue growth when compared to action potentials. This work therefore investigates the suitability of microwire electrodes for LFP recording by studying the electrical properties of key materials. We identify Niobium (Nb) as a candidate material with highly desirable properties. There is however also an inherent incompatibility when it comes to connection of microwire-based electrodes to silicon chips. Here we present a new process flow utilising a recessed glass substrate for mechanical support, silicon interposer for interconnection, and electroplating for contact adhesion. Furthermore, the proposed structure lends itself to hermetic encapsulation towards gas cavity based micropackages.

The feasibility of using time domain optical coherence tomography (TD-OCT) to detect compound action potential in a peripheral nerve and the setup characteristics, are studied through the use of finite-difference time-domain (FDTD) technique.

This paper presents a hardware realisation of a novel ECG baseline drift removal that preserves the ECG signal integrity. The microcontroller implementation detects the fiducial markers of the ECG signal and the baseline wander estimation is achieved through a weighted piecewise linear interpolation. This estimated drift is then removed to recover a “clean” ECG signal without significantly distorting the ST segment. Experimental results using real data from the MIT-BIH Arrhythmia Database (recording 100 and 101) with added baseline wander (BWM1) from the MIT-BIH Noise Stress Database show an average root mean square error of 34.3 μV (mean), 30.4 μV (median) and 18.4 μV (standard deviation) per heart beat.

This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 μm CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.

The paper presents a novel topology of a continuous-time analogue-to-digital converter (CT-ADC) featuring ultra-low static power consumption, activity-dependent dynamic consumption, and a compact footprint. This is achieved by utilising a novel charge-packet based threshold generation method, that alleviates the requirement for a conventional feedback DAC. The circuit has a static power consumption of 3.75 μW, with dynamic energy of 1.39pJ/conversion level. This type of converter is thus particularly well-suited for biosignals that are generally sparse in nature. The circuit has been optimised for neural spike recording by capturing a 3 kHz bandwidth with 8-bit resolution. For a typical extracellular neural recording the average power consumption is in the order of ∼4 μW. The circuit has been implemented in a commercially available 0.35 μm CMOS technology with core occupying a footprint of 0.12 mm2.

This paper presents a multichannel, ultra-low-noise arbitrary signal generation platform for emulating a wide range of different biopotential signals (e.g. ECG, EEG, etc). This is intended for use in the test, measurement and demonstration of bioinstrumentation and medical devices that interface to electrode inputs. The system is organized in 3 key blocks for generating, processing and converting the digital data into a parallel high performance analogue output. These blocks consist of: (1) a Raspberry Pi 3 (RPi3) board; (2) a custom Field Programmable Gate Array (FPGA) board with low-power IGLOO® Nano device; and (3) analogue board including the Digital-to-Analogue Converters (DACs) and output circuits. By implementing the system this way, good isolation can be achieved between the different power and signal domains. This mixed-signal architecture takes in a high bitrate SDIO (Secure Digital Input Output) stream, recodes and packetizes this to drive two multichannel DACs, with parallel analogue outputs that are then attenuated and filtered. The system achieves 32-parallel output channels each sampled at 48kS/s, with a 10 kHz bandwidth, 110 dB dynamic range and μV-level output noise.

This article reports an implantable transcutaneous telemetry for a brain machine interface that uses a novel optical communication system to achieve a highly energy-efficient link. Based on an pulse-based coding scheme, the system uses sub-nanosecond laser pulses to achieve data rates up to 300 Mbps with relatively low power levels when compared to other methods of wireless communication. This has been implemented using a combination of discrete components (semiconductor laser and driver, fast-response Si photodiode and interface) integrated at board level together with reconfigurable logic (encoder, decoder and processing circuits implemented using Xilinx KCU105 board with Kintex UltraScale FPGA). Experimental validation has been performed using a tissue sample that achieves representative level of attenuation/scattering (porcine skin) in the optical path. Results reveal that the system can operate at data rates up to 300 Mbps with a bit error rate (BER) of less than 10 $^{-10}$ , and an energy efficiency of 37 pJ/bit. This can communicate, for example, 1,024 channels of broadband neural data sampled at 18 kHz, 16-bit with only 11 mW power consumption.

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.

Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N − 3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation.

This paper describes a novel system for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires a single Chest Device be connected to a Brain Implant consisting of multiple identical optrodes that record neural activity and provide closed loop optical stimulation. The interface is integrated within each optrode SoC allowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate (1.6 Mbps) that is higher than that of the chest-to-head downlink (100kbps) superimposed on a power carrier. On-chip power management provides an unregulated 5 V DC supply with up to 2.5 mA output current for stimulation, and a regulated 3.3 V with 60 dB PSRR for recording and logic circuits. The circuit has been implemented in a 0.35 μm CMOS technology, occupying 1.4 mm 2 silicon area, and requiring a 62.2 μA average current consumption.

This paper reports on a pulsed coding technique based on optical Ultra-wideband (UWB) modulation for wireless implantable biotelemetry systems allowing for high data rate link whilst enabling significant power reduction compared to the state-of-the-art. This optical data coding approach is suitable for emerging biomedical applications like transcutaneous neural wireless communication systems. The overall architecture implementing this optical modulation technique employs sub-nanosecond pulsed laser as the data transmitter and small sensitive area photodiode as the data receiver. Moreover, it includes coding and decoding digital systems, biasing and driving analogue circuits for laser pulse generation and photodiode signal conditioning. The complete system has been implemented on Field-Programmable Gate Array (FPGA) and prototype Printed Circuit Board (PCB) with discrete off-the-shelf components. By inserting a diffuser between the transmitter and the receiver to emulate skin/tissue, the system is capable to achieve a 128 Mbps data rate with a bit error rate less than 10−9 and an estimated total power consumption of about 5 mW corresponding to a power efficiency of 35.9 pJ/bit. These results could allow, for example, the transmission of an 800-channel neural recording interface sampled at 16 kHz with 10-bit resolution.

This paper presents a novel method for improving the performance of template matching in neural spike sorting for similar shaped spikes, without increasing computational complexity. Mean templates for similar shaped spikes are enhanced to emphasise distinguishing features. Template optimisation is based on the separation and variance of sample distributions. Improved spike sorting performance is demonstrated on simulated neural recordings with two and three neuron spike shapes. The method is designed for implementation on a Next Generation Neural Interface (NGNI) device at Imperial College London.