<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> The next generation prosthetic hand that moves and feels like a real requires robust neural interconnection between the human minds machines. xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Here we present neuroprosthetic system to demonstrate principle by employing an artificial intelligence (AI) agent translate amputee's movement intent through peripheral nerve...

This paper presents a frequency-shaping (FS) neural recording architecture and its implementation in 0.13 μm CMOS process. Compared with conventional counterpart, the proposed inherently rejects electrode offset, increases input impedance 5-10 fold, compresses data dynamic range (DR) by 4.5-bit, simultaneously records local field potentials (LFPs) extracellular spikes, is more suitable for long-term experiments. Measured at 40 kHz sampling clock ± 0.6 V supply, recorder consumes 50 μW/ch, of...

Abstract Objective . While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities make an active prosthesis useful. Approach Here we present technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep...

Objective: Deep learning-based neural decoders have emerged as the prominent approach to enable dexterous and intuitive control of neuroprosthetic hands. Yet few studies materialized use deep learning in clinical settings due its high computational requirements. Methods: Recent advancements edge computing devices bring potential alleviate this problem. Here we present implementation a hand with embedded control. The decoder is designed based on recurrent network (RNN) architecture deployed...

This paper models in vivo neural signals and noise for extracellular spike detection. Although the recorded data approximately follow Gaussian distribution, they clearly deviate from white due to neuronal synchronization sparse distribution of energy. Our study predicts coexistence two components embedded dynamics, one exponential form (noise) other power (neural spikes). The prediction has been confirmed experiments sequences hippocampus, cortex surface, spinal cord; both acute long-term...

A high-performance, wide dynamic range, fully-integrated neural interface is one key component for many advanced bidirectional neuromodulation technologies. In this paper, to complement the previously proposed frequency-shaping amplifier (FSA) and high-precision electrical microstimulator, we will present a proof-of-concept design of data acquisition (DAQ) system that includes 15-bit, low-power Delta-Sigma analog-to-digital converter (ADC) real-time spike processor based on exponential...

In extracellular neural recording experiments, detecting spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. this paper, we report 16-channel spike detection chip based on customized method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features prediction of by applying...

This paper presents a low-noise, wireless neural recorder that has frequency dependent amplification to remove electrode offset and attenuate motion artifacts. The 2.5 GQ 50 MQ input impedance at 20 Hz 1 kHz for recording local field potentials extracellular spikes, respectively. To reduce the input-referred noise, we propose low-noise frontend design with multiple novel noise suppression techniques. power consumption, have integrated an exponential component polynomial spike processor...

This paper reports a novel neurotechnology (Neuronix) and its validation through experiments. It is miniature system-on-chip (SoC) that allows recording with simultaneous electrical microstimulation. function has not been demonstrated before enables precise, closed-loop neuromodulation. Neuronix represents recent advancement in brain technology applies to both animal research clinical applications.

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained different parts of nervous system. However, networks are often computationally complex and not feasible work in real-time. Here we investigate approaches' advantages disadvantages enhance learning-based decoding paradigm's efficiency inform its future implementation Our data recorded amputee's residual peripheral nerves. While primary analysis offline, nerve cut using a...

This paper presents the noise optimization of a novel switched-capacitor (SC) based neural interface architecture, and its circuit demonstration in 0.13 [Formula: see text] CMOS process. To reduce thermal folding ratio, suppress kT/C noise, several techniques are developed proposed architecture. First, one parasitic capacitance suppression scheme is to block charge transfer from capacitors amplifier output. Second, recording path-splitting input sampling stage selectively record local field...

This paper presents a frequency-shaping (FS) neural recording interface that can inherently reject electrode offset, 5-10 times increase input impedance, 4.5-bit extend system dynamic range (DR), and provide much more tolerance to motion artifacts 50/60 Hz power noise interferences. It is supposed be suitable for long-term brain-machine-interface (BMI) experiments. To achieve the mentioned performance above, proposed architecture adopts an auto-zero offset calibration avoid saturation,...

A high-resolution neurostimulator is the essential component of many bidirectional neural interfaces. In practice, effective resolution fully integrated designs often hindered by transistor mismatch, especially in submicrometer CMOS processes. this article, we present a new circuit technique called redundant crossfire (RXF) to address challenge. It derived from our sensing (RS) framework, which aims at engineering information redundancy into system architecture enhance its resolution. RXF...

This brief presents a new system architecture for neural recording to allow higher density and more tolerance interface degeneration artifacts. Compared with its conventional counterpart, the proposed has frequency-dependent gain stage that inherently rejects dc offset attenuates low-frequency interferences. In digital domain, frequency compensation is used restore signals "seen" by an electrode. Powered switched-capacitor design, can lead major improvements on performance metrics, including...

This paper presents a fully-integrated stimulator chip for electrical microstimulation. The device is designed in high-voltage process that allows up to 20V power supply and 19V output voltage compliance. A broad range of current-mode stimulation waveforms patterns can be generated, including symmetrical/asymmetrical, biphasic/monophasic, pulse train stimuli. current amplitude, width, rate are adjustable from 0.5μA 2mA, 100μs 4ms, 0.1Hz 200Hz, respectively. Two complementary charge-balancing...

Recorded neural data are frequently corrupted by large amplitude artifacts that triggered a variety of sources, such as subject movements, organ motions, electromagnetic interferences and discharges at the electrode surface. To prevent system from saturating electronics malfunctioning due to these artifacts, wide dynamic range for acquisition is demanded, which quite challenging achieve would require excessive circuit area power implementation. In this paper, we present high performance...

Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular, and nervous systems, yet its driven mechanism has not been fully comprehended. Until recently, only understanding redundancy mean to attain fault tolerance, which reflected design man-made systems. On contrary, our previous work on redundant sensing (RS) demonstrated an example where can be engineered solely for enhancing accuracy precision. The was inspired by binocular...

This paper presents a high-resolution, area- and power-efficient successive approximate register (SAR) analog-to-digital converter (ADC) for high precision nerve recording. The design features new "half-split" feedback digital-to-analog (DAC) capacitor array with integrated digital calibrations, which allow automatic estimation calibration of mismatches. As result, the SAR ADC can be substantially improved given constraints on circuits area power consumption. has been fabricated in 0.13μm...

This paper, power optimization of two high performance ΔΣ modulators for portable measurement applications is presented. One modulator a single-loop single-bit topology which achieves an 89.8dB peak SNDR and consumes 20μW with 1.5V supply. Here, new efficient current mirror Class-AB OTA introduced to reduce the power. The other adopts both multi-bit technique switched-opamp (SO) realize ultra-low target. Its total consumption only 9μW at 1.8V supply, reaches 80.5dB. Especially, fully-clocked...

A 9μW 88dB DR switched-opamp (SO) ΔΣ modulator is implemented in a low cost 0.35μm CMOS process. To evaluate the effects of finite voltage gain and 1/f noise clearly, two high efficient methods are introduced. And new fully switched-off SO with 50% power saving double Figure-of-Merit (FOM) over traditional type proposed to reduce total power. Besides, improve performance, novel resonator idea applicable technique adopted realize coefficient 1/100 75% 70% area reduction conventional design....

A theoretical analysis has been carried out to compare the tunneling processes in a double-quantum-well three-barrier (DQW-TB) system and single-quantum-well double-barrier (SQW-DB) system. Based on general WKB formula, it is shown that symmetric DQW-TB with transparency-matched barriers far superior SQW-DB number of aspects, including peak current, peak-to-valley ratio, speed limit.