An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces.

Intracortical brain computer interfaces (iBCIs) utilizing extracellular recordings mainly employ in vivo signal processing application-specific integrated circuits (ASICs) to detect action potentials (spikes). Conventionally, "brain-switches" based on spiking activity have been employed to realize asynchronous (self-paced) iBCIs, estimating when the user involves in the underlying BCI task. Several studies have demonstrated that local field potentials (LFPs) can effectively replace action potentials, drastically reducing the power consumption and processing requirements of in vivo ASICs. This article presents the first LFP-based brain-switch design and implementation using gated recurrent neural networks (RNNs). Compared to the previously reported brain-switches, our design requires no exhaustive learning phase for the estimation of optimal recording channels or frequency band selection, making it more applicable to practical asynchronous iBCIs. The synthesized ASIC of the designed in vivo LFP-based feature extraction unit, in a standard 180-nm CMOS process, occupies only 0.09 mm2 of silicon area, and the post place-and-route synthesis results indicate that it consumes 91.87 nW of power while operating at 2 kHz. Compared to the previously published ASICs, the proposed LFP-based brain-switch consumes the least power for in vivo digital signal processing and achieves comparable state estimation performance to that of spike-based brain-switches.

Iterative Patient Testing of a Stimuli-Responsive Swallowing Activity Sensor to Promote Extended User Engagement During the First Year After Radiation: Multiphase Remote and In-Person Observational Cohort Study.

BACKGROUND: Frequent sensor-assisted monitoring of changes in swallowing function may help improve detection of radiation-associated dysphagia before it becomes permanent. While our group has prototyped an epidermal strain/surface electromyography sensor that can detect minute changes in swallowing muscle movement, it is unknown whether patients with head and neck cancer would be willing to wear such a device at home after radiation for several months. OBJECTIVE: We iteratively assessed patients' design preferences and perceived barriers to long-term use of the prototype sensor. METHODS: In study 1 (questionnaire only), survivors of pharyngeal cancer who were 3-5 years post treatment and part of a larger prospective study were asked their design preferences for a hypothetical throat sensor and rated their willingness to use the sensor at home during the first year after radiation. In studies 2 and 3 (iterative user testing), patients with and survivors of head and neck cancer attending visits at MD Anderson's Head and Neck Cancer Center were recruited for two rounds of on-throat testing with prototype sensors while completing a series of swallowing tasks. Afterward, participants were asked about their willingness to use the sensor during the first year post radiation. In study 2, patients also rated the sensor's ease of use and comfort, whereas in study 3, preferences were elicited regarding haptic feedback. RESULTS: The majority of respondents in study 1 (116/138, 84%) were willing to wear the sensor 9 months after radiation, and participant willingness rates were similar in studies 2 (10/14, 71.4%) and 3 (12/14, 85.7%). The most prevalent reasons for participants' unwillingness to wear the sensor were 9 months being excessive, unwanted increase in responsibility, and feeling self-conscious. Across all three studies, the sensor's ability to detect developing dysphagia increased willingness the most compared to its appearance and ability to increase adherence to preventive speech pathology exercises. Direct haptic signaling was also rated highly, especially to indicate correct sensor placement and swallowing exercise performance. CONCLUSIONS: Patients and survivors were receptive to the idea of wearing a personalized risk sensor for an extended period during the first year after radiation, although this may have been limited to well-educated non-Hispanic participants. A significant minority of patients expressed concern with various aspects of the sensor's burden and its appearance. TRIAL REGISTRATION: ClinicalTrials.gov NCT03010150; https://clinicaltrials.gov/study/NCT03010150.

A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 µ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 µV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System with Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4× and 4.3× in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39× with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6× and 2.8× savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1× in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 µW of power while achieving 2.19 µVrms and 2.4 µVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

Efficient In Vivo Neural Signal Compression Using an Autoencoder-based Neural Network.

Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio.

A 2.2 nW Analog Electrocardiogram Processor based on Stochastic Resonance Achieving a 99.94% QRS Complex Detection Sensitivity.

This paper presents an ultra-low power electrocardiogram (ECG) processor that can detect QRS-waves in real time as the data streams in. The processor performs out-of-band noise suppression via a linear filter, and in-band noise suppression via a nonlinear filter. The nonlinear filter also enhances the QRS-waves by facilitating stochastic resonance. The processor identifies the QRS-waves on noise-suppressed and enhanced recordings using a constant threshold detector. For energy-efficiency and compactness, the processor exploits current-mode analog signal processing techniques, which significantly reduces the design complexity when implementing the second-order dynamics of the nonlinear filter. The processor is designed and implemented in TSMC 65 nm CMOS technology. In terms of detection performance, the processor achieves an average F1 = 99.88% over the MIT-BIH Arrhythmia database and outperforms all previous ultra-low power ECG processors. The processor is the first that is validated against noisy ECG recordings of MIT-BIH NST and TELE databases, where it achieves better detection performances than most digital algorithms run on digital platforms. The design has a footprint of 0.08 mm2 and dissipates 2.2 nW when supplied by a single 1V supply, making it the first ultra-low power and real-time processor that facilitates stochastic resonance.

Power-efficientin vivobrain-machine interfaces via brain-state estimation.

Objective.Advances in brain-machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will.Approach.To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of anin vivointention-aware interface via brain-state estimation.Main Results.It is shown that incorporating brain-state estimation reduces thein vivopower consumption and reduces total energy dissipation by over 1.8× compared to those of the current systems, enabling longer better life for implanted circuits. The synthesized application-specific integrated circuit (ASIC) of the designed intention-aware multi-unit spike detection system in a standard 180 nm CMOS process occupies 0.03 mm2of silicon area and consumes 0.63 µW of power per channel, which is the least power consumption among the currentin vivoASIC realizations.Significance.The proposed interface is the first practical approach towards realizing asynchronous BMIs while reducing the power consumption of the BMI interface and enhancing neural decoding performance compared to those of the conventional synchronous BMIs.

A self-powered ingestible wireless biosensing system for real-time in situ monitoring of gastrointestinal tract metabolites.

Information related to the diverse and dynamic metabolite composition of the small intestine is crucial for the diagnosis and treatment of various diseases. However, our current understanding of the physiochemical dynamics of metabolic processes within the small intestine is limited due to the lack of in situ access to the intestinal environment. Here, we report a demonstration of a battery-free ingestible biosensing system for monitoring metabolites in the small intestine. As a proof of concept, we monitor the intestinal glucose dynamics on a porcine model. Battery-free operation is achieved through a self-powered glucose biofuel cell/biosensor integrated into a circuit that performs energy harvesting, biosensing, and wireless telemetry via a power-to-frequency conversion scheme using magnetic human body communication. Such long-term biochemical analysis could potentially provide critical information regarding the complex and dynamic small intestine metabolic profiles.

A Stochastic Resonance P- and T-wave Detection Algorithm.

An algorithm to detect P- and T-waves in an electrocardiogram (ECG) signal is presented. The algorithm has physical origins inspired by weak signal detection by leveraging stochastic resonance (SR) in a well potential. Specifically, a particle inside an underdamped monostable well is introduced with the ECG signal. The parameters defining the well and system characteristics are optimized towards enhancing the P-, R-, and T -waves while suppressing the other portions including noise-only sections. The enhanced features are detected by thresholding. Based on the performance obtained from the QT database, the algorithm achieves an average sensitivity of 99.97% for P-waves and an average sensitivity of 99.35% for T-waves, better than most P- and T-wave detection algorithms reported. Clinical Relevance- The proposed SR algorithm achieves high P- and T-wave detection performance and can potentially be integrated with implantable long-term cardiac monitors for patients experiencing rare symptoms without deteriorating the battery life.

In vivoneural spike detection with adaptive noise estimation.

Objective.The ability to reliably detect neural spikes from a relatively large population of neurons contaminated with noise is imperative for reliable decoding of recorded neural information.Approach.This article first analyzes the accuracy and feasibility of various potential spike detection techniques forin vivorealizations. Then an accurate and computationally-efficient spike detection module that can autonomously adapt to variations in recording channels' statistics is presented.Main results.The accuracy of the chosen candidate spike detection technique is evaluated using both synthetic and real neural recordings. The designed detector also offers the highest decoding performance over two animal behavioral datasets among alternative detection methods.Significance.The implementation results of the designed 128-channel spike detection module in a standard 180 nm CMOS process is among the most area and power-efficient spike detection ASICs and operates within the tissue-safe constraints for brain implants, while offering adaptive noise estimation.

An integrated wearable microneedle array for the continuous monitoring of multiple biomarkers in interstitial fluid.

Implementations of wearable microneedle-based arrays of sensors for the monitoring of multiple biomarkers in interstitial fluid have lacked system integration and evidence of robust analytical performance. Here we report the development and testing of a fully integrated wearable array of microneedles for the wireless and continuous real-time sensing of two metabolites (lactate and glucose, or alcohol and glucose) in the interstitial fluid of volunteers performing common daily activities. The device works with a custom smartphone app for data capture and visualization, comprises reusable electronics and a disposable microneedle array, and is optimized for system integration, cost-effective fabrication via advanced micromachining, easier assembly, biocompatibility, pain-free skin penetration and enhanced sensitivity. Single-analyte and dual-analyte measurements correlated well with the corresponding gold-standard measurements in blood or breath. Further validation of the technology in large populations with concurrent validation of sensor readouts through centralized laboratory tests should determine the robustness and utility of real-time simultaneous monitoring of several biomarkers in interstitial fluid.

A Stochastic Resonance Electrocardiogram Enhancement Algorithm for Robust QRS Detection.

This study presents a new QRS detection algorithm making use of the background noise that is inevitably present in electrocardiogram (ECG) recordings. The algorithm suppresses noise, enhances the QRS-waves, and applies a threshold for QRS detection. Noise suppression and QRS enhancement are performed by a band-pass filter stage followed by a nonlinear stage based on the interaction of a particle inside an underdamped monostable potential well. The nonlinear stage maximizes the output when there is a QRS-wave and minimizes the output otherwise. One of the instruments that the nonlinear stage uses to enhance the QRS-waves is stochastic resonance, where the output is maximized for a non-zero intensity background noise. In terms of QRS-wave detection F1 score, which ranges from 98.87% to 99.99% on four major benchmarking databases (MIT-BIH Arrhythmia, QT, European ST-T, and MIT-BIH Noise Stress Test), the algorithm outperforms all existing ECG processing algorithms. The study, for the first time, demonstrates QRS-enhancement by facilitating stochastic resonance while suppressing in-band noise of ECG signals. Detecting QRS-waves as the ECG data streams, having a complexity of O(n), and not requiring any training data make the algorithm convenient for real-time ECG monitoring applications with limited computational resources.

A 1.2nW Analog Electrocardiogram Processor Achieving a 99.63% QRS Complex Detection Sensitivity.

An energy-efficient electrocardiogram (ECG) processor for real-time QRS detection is presented. The proposed algorithm is based on the Pan-Tompkins algorithm and it is implemented in the analog domain leveraging ultra-low power analog electronics biased in subthreshold. Operational transconductance amplifiers with ∼100 mV linear range are used in almost all of the processing blocks, while squaring is performed on current signals. Additionally, instead of adaptive thresholding, a fixed-level thresholding is performed, thereby eliminating the need for additional blocks such as memory and threshold update. The processor is designed in 65 nm TSMC CMOS technology and has a footprint of 0.078 mm2. When supplied by a 1 V supply, the processor consumes 1.2 nW. Using the recordings in the MIT-BIH database, the processor achieves an average QRS detection sensitivity of 99.63% and positive predictivity of 99.47%.

Investigating well potential parameters on neural spike enhancement in a stochastic-resonance pre-emphasis algorithm.

Objective.Background noise experienced during extracellular neural recording limits the number of spikes that can be reliably detected, which ultimately limits the performance of next-generation neuroscientific work. In this study, we aim to utilize stochastic resonance (SR), a technique that can help identify weak signals in noisy environments, to enhance spike detectability.Approach.Previously, an SR-based pre-emphasis algorithm was proposed, where a particle inside a 1D potential well is exerted by a force defined by the extracellular recording, and the output is obtained as the displacement of the particle. In this study, we investigate how the well shape and damping status impact the output signal-to-noise ratio (SNR). We compare the overdamped and underdamped solutions of shallow- and steep-wall monostable wells and bistable wells in terms of SNR improvement using two synthetic datasets. Then, we assess the spike detection performance when thresholding is applied on the output of the well shape-damping status configuration giving the best SNR enhancement.Main results.The SNR depends on the well-shape and damping-status type as well as the input noise level. The underdamped solution of the shallow-wall monostable well can yield to more than four orders of magnitude greater SNR improvement compared to other configurations for low noise intensities. Using this configuration also results in better spike detection sensitivity and positive predictivity than the state-of-the-art spike detection algorithms for a public synthetic dataset. For larger noise intensities, the overdamped solution of the steep-wall monostable well provides better spike enhancement than the others.Significance.The dependence of SNR improvement on the input signal noise level can be used to design a detector with multiple outputs, each more sensitive to a certain distance from the electrode. Such a detector can potentially enhance the performance of a successive spike sorting stage.

Microneedle-Based Detection of Ketone Bodies along with Glucose and Lactate: Toward Real-Time Continuous Interstitial Fluid Monitoring of Diabetic Ketosis and Ketoacidosis.

Diabetic ketoacidosis (DKA), a severe complication of diabetes mellitus with potentially fatal consequences, is characterized by hyperglycemia and metabolic acidosis due to the accumulation of ketone bodies, which requires people with diabetes to monitor both glucose and ketone bodies. However, despite major advances in diabetes management mainly since the emergence of new-generation continuous glucose monitoring (CGM) devices capable of in vivo monitoring of glucose directly in the interstitial fluid (ISF), the continuous monitoring of ketone bodies is yet to be addressed. Here, we present the first use of a real-time continuous ketone bodies monitoring (CKM) microneedle platform. The system is based on the electrochemical monitoring of ß-hydroxybutyrate (HB) as the dominant biomarker of ketone formation. Such real-time HB detection has been realized using the ß-hydroxybutyrate dehydrogenase (HBD) enzymatic reaction and by addressing the major challenges associated with the stable confinement of the enzyme/cofactor couple (HBD/NAD+) and with a stable and selective low-potential fouling-free anodic detection of NADH. The resulting CKM microneedle device displays an attractive analytical performance, with high sensitivity (with low detection limit, 50 µM), high selectivity in the presence of potential interferences, along with good stability during prolonged operation in artificial ISF. The potential applicability of this microneedle sensor toward minimally invasive monitoring of ketone bodies has been demonstrated in a phantom gel skin-mimicking model. The ability to detect HB along with glucose and lactate on a single microneedle array has been demonstrated. These findings pave the way for CKM and for the simultaneous microneedle-based monitoring of multiple diabetes-related biomarkers toward a tight glycemic control.

A 3 mm × 3 mm Fully Integrated Wireless Power Receiver and Neural Interface System-on-Chip.

A miniaturized, fully integrated wireless power receiver system-on-chip with embedded 16-channel electrode array and data transceiver for electrocortical neural recording and stimulation is presented. An H-tree power and signal distribution network throughout the SoC maintains high quality factor up to 11 in the on-chip receiver coil at 144 MHz resonant frequency while rejecting RF interference in sensitive neural interface circuits owing to its perpendicular and equidistant geometry. A multi-mode buck-boost resonant regulating rectifier (B 2R 3) offers greater than 11-dB input dynamic range in RF reception and less than 1 mV overshoot in transient load regulation. At 10 mm link distance, the 9 mm 2 neural interface SoC fabricated in a 180 nm silicon-on-insulator (SOI) process attains an overall wireless power transmission system efficiency (WSE) of 3.4% in driving a 160  µW load yielding a WSE figure-of-merit of 131, while maintaining signal integrity in analog recording and wireless data transmission that comprise the on-chip load.

Robust Biopotential Acquisition via a Distributed Multi-Channel FM-ADC.

This contribution presents an active electrode system for biopotential acquisition using a distributed multi-channel FM-modulated analog front-end and ADC architecture. Each electrode captures one biopotential signal and converts to a frequency modulated signal using a VCO tuned to a unique frequency. Each electrode then buffers its output onto a shared analog line that aggregates all of the FM-modulated channels. This aggregation results in rugged, wearable form factor by eliminating wire clutter of traditional systems. A gateway integrated circuit then digitizes the composite FM signal and transmits for further processing. The coding gain due to bandwidth expansion of FM provides a large usable dynamic range (DR) and the single ADC for multiple channels results in significant power savings. Finally, the use of FM signals between the transducers and ADC provides resilience to motion and EMI artifacts. The system is implemented in 65 nm silicon using two distinct 1 mm 2 chip designs. Six-channel operation is demonstrated using FM channels with center frequencies around 15 MHz and the system achieves a usable DR of over 100 dB, while achieving figure of merit competitive with state of the art prior works using traditional approaches.

A Fully Integrated RF-Powered Energy-Replenishing Current-Controlled Stimulator.

This paper presents a fully-integrated current-controlled stimulator that is powered directly from on-chip coil antenna and achieves adiabatic energy-replenishing operation without any bulky external components. Adiabatic supply voltages, which can reach a differential range of up to 7.2 V, are directly generated from an on-chip 190-MHz resonant LC tank via a self-cascading/folding rectifier network, bypassing the losses that would otherwise be introduced by the 0.8 V system supply-generating rectifier and regulator. The stimulator occupies 0.22 mm 2 in a 180 nm silicon-on-insulator process and produces differential currents up to 145  µA. Using a charge replenishing scheme, the stimulator redirects the charges accumulated across the electrodes to the system power supplies for 63.1% of stimulation energy recycling. To benchmark the efficiency of stimulation, a figure of merit termed the stimulator efficiency factor (SEF) is introduced. The adiabatic power rails and energy replenishment scheme enabled our stimulator to achieve an SEF of 6.0.

A Wearable, Extensible, Open-Source Platform for Hearing Healthcare Research.

Hearing loss is one of the most common conditions affecting older adults worldwide. Frequent complaints from the users of modern hearing aids include poor speech intelligibility in noisy environments and high cost, among other issues. However, the signal processing and audiological research needed to address these problems has long been hampered by proprietary development systems, underpowered embedded processors, and the difficulty of performing tests in real-world acoustical environments. To facilitate existing research in hearing healthcare and enable new investigations beyond what is currently possible, we have developed a modern, open-source hearing research platform, Open Speech Platform (OSP). This paper presents the system design of the complete OSP wearable platform, from hardware through firmware and software to user applications. The platform provides a complete suite of basic and advanced hearing aid features which can be adapted by researchers. It serves web apps directly from a hotspot on the wearable hardware, enabling users and researchers to control the system in real time. In addition, it can simultaneously acquire high-quality electroencephalography (EEG) or other electrophysiological signals closely synchronized to the audio. All of these features are provided in a wearable form factor with enough battery life for hours of operation in the field.

Simultaneous Monitoring of Sweat and Interstitial Fluid Using a Single Wearable Biosensor Platform.

The development of wearable biosensors for continuous noninvasive monitoring of target biomarkers is limited to assays of a single sampled biofluid. An example of simultaneous noninvasive sampling and analysis of two different biofluids using a single wearable epidermal platform is demonstrated here. The concept is successfully realized through sweat stimulation (via transdermal pilocarpine delivery) at an anode, alongside extraction of interstitial fluid (ISF) at a cathode. The system thus allows on-demand, controlled sampling of the two epidermal biofluids at the same time, at two physically separate locations (on the same flexible platform) containing different electrochemical biosensors for monitoring the corresponding biomarkers. Such a dual biofluid sampling and analysis concept is implemented using a cost-effective screen-printing technique with body-compliant temporary tattoo materials and conformal wireless readout circuits to enable real-time measurement of biomarkers in the sampled epidermal biofluids. The performance of the developed wearable device is demonstrated by measuring sweat-alcohol and ISF-glucose in human subjects consuming food and alcoholic drinks. The different compositions of sweat and ISF with good correlations of their chemical constituents to their blood levels make the developed platform extremely attractive for enhancing the power and scope of next-generation noninvasive epidermal biosensing systems.