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.

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.

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.

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.

In-ear integrated sensor array for the continuous monitoring of brain activity and of lactate in sweat.

Owing to the proximity of the ear canal to the central nervous system, in-ear electrophysiological systems can be used to unobtrusively monitor brain states. Here, by taking advantage of the ear's exocrine sweat glands, we describe an in-ear integrated array of electrochemical and electrophysiological sensors placed on a flexible substrate surrounding a user-generic earphone for the simultaneous monitoring of lactate concentration and brain states via electroencephalography, electrooculography and electrodermal activity. In volunteers performing an acute bout of exercise, the device detected elevated lactate levels in sweat concurrently with the modulation of brain activity across all electroencephalography frequency bands. Simultaneous and continuous unobtrusive in-ear monitoring of metabolic biomarkers and brain electrophysiology may allow for the discovery of dynamic and synergetic interactions between brain and body biomarkers in real-world settings for long-term health monitoring or for the detection or monitoring of neurodegenerative diseases.

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.

Nearly five-year continuous atmospheric measurements of black carbon over a suburban area in central France.

Atmospheric black carbon (BC) concentration over a nearly 5 year period (mid-2017-2021) was continuously monitored over a suburban area of Orléans city (France). Annual mean atmospheric BC concentration were 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48 ± 0.46 and 0.50 ± 0.72 µg m-3, respectively, for the year of 2017, 2018, 2019, 2020 and 2021. Seasonal pattern was also observed with maximum concentration (0.70 ± 0.18 µg m-3) in winter and minimum concentration (0.38 ± 0.04 µg m-3) in summer. We found a different diurnal pattern between cold (winter and fall) and warm (spring and summer) seasons. Further, fossil fuel burning contributed >90 % of atmospheric BC in the summer and biomass burning had a contribution equivalent to that of the fossil fuel in the winter. Significant week days effect on BC concentrations was observed, indicating the important role of local emissions such as car exhaust in BC level at this site. The behavior of atmospheric BC level with COVID-19 lockdown was also analyzed. We found that during the lockdown in warm season (first lockdown: 27 March-10 May 2020 and third lockdown 17 March-3 May 2021) BC concentration were lower than in cold season (second lockdown: 29 October-15 December 2020), which could be mainly related to the BC emission from biomass burning for heating. This study provides a long-term BC measurement database input for air quality and climate models. The analysis of especially weekend and lockdown effect showed implications on future policymaking toward improving local and regional air quality as well.

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.

Design and printing of proprioceptive three-dimensional architected robotic metamaterials.

Advances in additive manufacturing techniques have enabled the creation of stimuli-responsive materials with designed three-dimensional (3D) architectures. Unlike biological systems in which functions such as sensing, actuation, and control are closely integrated, few architected materials have comparable system complexity. We report a design and manufacturing route to create a class of robotic metamaterials capable of motion with multiple degrees of freedom, amplification of strain in a prescribed direction in response to an electric field (and vice versa), and thus, programmed motions with self-sensing and feedback control. These robotic metamaterials consist of networks of piezoelectric, conductive, and structural elements interwoven into a designed 3D lattice. The resulting architected materials function as proprioceptive microrobots that actively sense and move.

Investigation of metal mobility in gold and silver mine tailings by single-step and sequential extractions.

Metal leachate from mine tailings has the potential to release toxic metals into the surrounding environment. A single-step extraction procedure mimicking rainwater and a three-step BCR sequential extraction procedure (acid, reducing and oxidizing conditions) were applied to gold (GMT) and silver (SMT) mine tailings. Major (Al, Ca, Fe, Mg, and Mn) and trace metals were monitored to better understand the mobility and geochemistry of these metals when exposed to various environmental leaching conditions. Rainwater extraction released only small quantities of metals, while the three-step BCR extraction was more effective in mobilizing metals from the tailings. Under the acidic conditions of BCR step 1, Ca, Mg, Cd, Cu, and Mn were released in high concentrations. The dissolution of Fe, Ca, and Mg were dominant along with Pb in step 2 (reducing conditions). In step 3 (oxidizing conditions), Fe was the most dominant species together with Co, Cu, Ni, and Se. A high fraction of Al, Be, Cr, Li, Mo, Sb, Tl, and V remained in the residue. From SMT, larger quantities of As, Ca, Cd, and Zn were released compared to GMT. The BCR extraction could be applied to tailings to predict the potential release of toxic metals from mine wastes; however, excessive amounts of Ca and Fe in the tailings could cause carry-overs and incomplete extraction and carry-overs, resulting in a misinterpretation of results.

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.

Channel Characterization of Magnetic Human Body Communication.

OBJECTIVE: The objective of this paper is to model and experimentally validate the path loss benefits of magnetic human body communication (mHBC) using small form-factor-accurate coils operating under realistic conditions. METHODS: A radiating near-field coupling model and numerical simulations are presented to show that the magnetic-dominant near-field coupling between resonant coils offers low path loss across the body and exhibits extra robustness to antenna misalignment compared to far-field RF schemes. To overcome the pitfalls in conventional vector-network-analyzer-based measurement configurations, we propose a standardized setup applied to broadband channel loss measurement with portable instruments. Two types of PCB coils for mHBC communication, designed for large devices such as smartphones and small devices such as earbuds, respectively, are built and measured. RESULTS: The mHBC link for the ear-to-ear non-line-of-sight (NLOS) path measures up to -23.1 dB and -31.2 dB with large and small coils, respectively, which is 50 dB more efficient than the conventional Bluetooth channels utilizing antennas of similar sizes. Ear-to-pocket and pocket-to-pocket channels also show at least 16 dB higher transmission than the Bluetooth channel. CONCLUSION: In terms of path loss, the mHBC approach offers compelling performance for short-range applications over the body region. For coils with dimensions of several centimeters, working between 100 MHz and 200 MHz minimizes the channel loss while keeping the bandwidth above 1 MHz. SIGNIFICANCE: The extremely high efficiency of the proposed mHBC channel provides a solution to the energy problem for miniaturized wearables, potentially leading to new wearable device designs.

A 174.7-dB FoM, 2nd-Order VCO-Based ExG-to-Digital Front-End Using a Multi-Phase Gated-Inverted-Ring Oscillator Quantizer.

This paper presents a second-order voltage-controlled oscillator (VCO)-based front-end for the direct digitization of biopotential signals. This work addresses the non-linearity of VCO-based ADC architectures with a mismatch resilient, multi-phase quantizer, a gated-inverted-ring oscillator (GIRO), achieving >110-dB SFDR. Leveraging the time-domain encoding of the first integrator, the ADC's power is dynamically scaled with the input amplitude enabling up to 35% power savings in the absence of motion artifacts or interference. An auxiliary input-impedance booster increases the ADC's input impedance to 50 MΩ across the entire bandwidth. Fabricated in a 65-nm CMOS process, this ADC achieves 92.3-dB SNDR in a 1 kHz BW while consuming 5.8 µW for a 174.7 dB Schreier FoM.

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.