An Implantable Neuromorphic Sensing System Featuring Near-sensor Computation and Send-on-Delta Transmission for Wireless Neural Sensing of Peripheral Nerves

A 62.2dB SNDR Event-Driven Level-Crossing ADC With SAR-Assisted Delay Compensation Loop for Time-Sparse Biomedical Signal Acquisition

This paper proposed an event-driven clockless level-crossing ADC (LC-ADC) suitable for biomedical applications. Thanks to the LC loop, the sampling rate of the converter automatically adapts to the input activities. Activity-dependent power consumption and data compression can thus be realized, saving system power, especially during time-sparse signal acquisition. Meanwhile, a SAR-assisted loop is exploited to resolve the loop-delay-induced distortion in conventional LC-ADC. Therefore, the resolution and power efficiency of the LC-ADC are improved effectively while maintaining the event-driven feature. Implemented in a 55nm process, the proposed LC-ADC achieves a scalable power consumption and a peak SNDR of 62.2dB for a 20kHz input. It also achieves a Walden FoM of 29.7fJ/conv.-step and a Schreier FoM of 158.6dB, which is best in class, without using off-chip calibration. Sub µW power is realized when the input frequency is below 1.5kHz. The proposed LC-ADC is also verified by simulated electrocardiogram (ECG), neural spike, and electromyogram (EMG) signals. It provides a ∼7X data compression for ECG input, providing an attractive solution for time-sparse signal acquisition in biomedical applications.

An Event-Based Neural Compressive Telemetry With >11× Loss-Less Data Reduction for High-Bandwidth Intracortical Brain Computer Interfaces

Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 µW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 µV.

The rise of bioelectronic medicine

Bioelectronic Medicine (BEM), which uses implantable electronic medical devices to interface with electrically active tissues, aspires to revolutionize the way we understand and fight disease. By leveraging knowledge from microelectronics, materials science, information technology, neuroscience and medicine, BEM promises to offer novel solutions that address unmet clinical needs and change the concept of therapeutics. This perspective communicates our vision for the future of BEM and presents the necessary steps that need to be taken and the challenges that need to be faced before this new technology can flourish.

A Standard-Cell-Based Neuro-Inspired Integrate-and-Fire Analog-to-Time Converter for Biological and Low-Frequency Signals - Comparison With Analog Version

Continuous-time asynchronous data converters namely, analog-to-digital converters and analog-to-time converters, can be beneficial for certain types of applications, such as, processing of biological signals with sparse information. A particular case of these converters is the integrate-and-fire converter (IFC) that is inspired by the neural system. If it is possible to develop a standard-cell-based (SCB) IFC circuit to perform well in advanced technology nodes, it will benefit from the simplicity of SCB circuit designs and can be implemented in widely available field-programmable gate arrays (FPGAs). This way, this paper proposes two IFC circuits designed and prototyped in a 130 nm CMOS standard process. The first is a novel SCB open-loop dynamic IFC. The latter, is a closed-loop analog IFC with conventional blocks. This paper presents a through comparison between the two IFC circuits. They have a power dissipation of 59 μW and 53 μW, and an energy per pulse of 18 pJ and 1060 pJ, SCB and analog IFC, respectively. The SCB IFC has one of the lowest energy per pulse consumption reported for IFC circuits. The analog IFC, being fully differential, is to our knowledge the first of its kind. Moreover, they do not require an external clock. They can convert signals with a peak-to-peak amplitude from 1.6 mV to 28 mV and 0.6 mV to 2.4 mV, and a frequency range of 2 Hz to 42 kHz and 10 Hz to 4 kHz, SCB and analog IFC, respectively. Presenting low normalized RMS conversion plus reconstruction errors, below 5.2%. The maximum pulse density (average firing-rate) is 3300 kHz, for the SCB and 50 kHz, for the analog IFC.

An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors

Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.

Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.

Second-Order Level-Crossing Sampling Analog to Digital Converter for Electrocardiogram Delineation and Premature Ventricular Contraction Detection

This article presents an electrocardiogram (ECG) delineation and arrhythmia heartbeat detection system using a novel second-order level-crossing sampling analog to digital converter (ADC) for real-time data compression and feature extraction. The proposed system consists of the front-end integrated circuit of the data converter, the delineation algorithm, and the arrhythmia detection algorithm. Compared with conventional level-sampling ADCs, the proposed circuit updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has sloped sampling levels. The computing is done digitally and is implemented by modifying the digital control logic of a conventional Successive-approximation-register (SAR) ADC. The system separates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the digital value of the selected sampling points and the timestamp between the selected sampling points. The main advantages are data savings for the data converter and the following digital signal processing or communication circuits, which are ideal for low-power sensors. The test chip was fabricated using a 180 nm CMOS process. When sensing sparse signals such as ECG signals the proposed ADC achieves a compression factor of 8.33. The delineation algorithm uses a triangle filter method to locate the fiducial points and measures the intervals, slopes, and morphology of the QRS complex and the P/T waves. Those extracted features are then used in the arrhythmia heartbeat detection algorithm to identify Premature Ventricular Contraction (PVC). The overall performance of the system is evaluated using the MIT-BIH database and the QT database, which is also compared with the recently reported systems. The accuracy, sensitivity, specificity, PPV, and F1 score are 97.3%, 89.6%, 97.8%, 73.3%, and 0.81 for detecting PVC.

The Fifth Bioelectronic Medicine Summit: today's tools, tomorrow's therapies

The emerging field of bioelectronic medicine (BEM) is poised to make a significant impact on the treatment of several neurological and inflammatory disorders. With several BEM therapies being recently approved for clinical use and others in late-phase clinical trials, the 2022 BEM summit was a timely scientific meeting convening a wide range of experts to discuss the latest developments in the field. The BEM Summit was held over two days in New York with more than thirty-five invited speakers and panelists comprised of researchers and experts from both academia and industry. The goal of the meeting was to bring international leaders together to discuss advances and cultivate collaborations in this emerging field that incorporates aspects of neuroscience, physiology, molecular medicine, engineering, and technology. This Meeting Report recaps the latest findings discussed at the Meeting and summarizes the main developments in this rapidly advancing interdisciplinary field. Our hope is that this Meeting Report will encourage researchers from academia and industry to push the field forward and generate new multidisciplinary collaborations that will form the basis of new discoveries that we can discuss at the next BEM Summit.