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Journal ArticleDOI

A 15.2-ENOB 5-kHz BW 4.5- $\mu$ W Chopped CT $\Delta\Sigma$ -ADC for Artifact-Tolerant Neural Recording Front Ends

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TLDR
A low-power continuous-time delta-sigma analog to digital converter (ADC) is presented, which along with an capacitively-coupled chopper instrumentation amplifier (CCIA) realizes a front end that can digitize neural signals from 1 Hz to 5 kHz in the presence of 200-mV<sub>pp</sub> differential artifacts and 700-m V common-mode (CM) artifacts.
Abstract
Implantable closed-loop neural stimulation is desirable for clinical translation and basic neuroscience research. Neural stimulation generates large artifacts at the recording sites, which saturate existing recording front ends. This paper presents a low-power continuous-time delta-sigma analog to digital converter (ADC), which along with an 8 $\times $ gain capacitively-coupled chopper instrumentation amplifier (CCIA), realizes a front end that can digitize neural signals from 1 Hz to 5 kHz in the presence of 200-mVpp differential artifacts and 700-mVpp common-mode (CM) artifacts. A modified loop-filter is used in the ADC along with new linearization techniques to significantly reduce power consumption. Fabricated in 40-nm CMOS, the ADC occupies an area of 0.053 mm2, consumes 4.5 $\mu \text{W}$ from a 1.2-V supply, has an input impedance of 20 $\text{M}\Omega $ and bandwidth (BW) of 5 kHz, and achieves a peak signal to noise and distortion ratio (SNDR) of 93.5 dB for a 1.77- $\text{V}_{\mathrm {pp}}$ differential input at 1 kHz. The ADC’s figure of merit (FOM) (using SNDR) is 184 dB, which is 6 dB higher than the state of the art in high-resolution ADCs. The complete front end occupies an area of 0.113 mm2, consumes 7.3 $\mu \text{W}$ from a 1.2-V supply, has a dc input impedance of 1.5 $\text{G}\Omega $ , input-referred noise of 6.35 $\mu \text{V}_{\mathrm {rms}}$ in 1 Hz–5 kHz, and total harmonic distortion of −81 dB for a 200-mVpp input at 1 kHz, and is immune to 700-mVpp CM interference. Compared to front ends intended for closed-loop neural recording, this paper improves the linear input range by 2 $\times $ , the signal BW by 10 $\times $ , the dynamic range by 12.6 dB, the FOM by 12.4 dB and remains immune to large CM interference while maintaining comparable power, area, and noise performance.

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Journal ArticleDOI

Plugging Electronics Into Minds: Recent Trends and Advances in Neural Interface Microsystems

TL;DR: Despite rapid advancements in our electronics industry, connecting our minds to machines (e.g., robots and computers) through brain-machine interface (BMI) technologies remains an unfulfilled human ambition.
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A 340 nW/Channel 110 dB PSRR Neural Recording Analog Front-End Using Replica-Biasing LNA, Level-Shifter Assisted PGA, and Averaged LFP Servo Loop in 65 nm CMOS

TL;DR: This paper presents an 8-channel energy-efficient analog front-end (AFE) for neural recording, with improvements in power supply rejection ratio (PSRR) and dynamic range, and an averaged local field potential (A-LFP) servo loop is introduced to extend the dynamic range without consuming too much extra power and chip area.
Journal ArticleDOI

Energy-Efficient Integrated Circuit Solutions Toward Miniaturized Closed-Loop Neural Interface Systems.

TL;DR: In this paper, the authors review recent approaches toward the miniaturized closed-loop neural interface system with integrated circuit (IC) techniques and present hardware design challenges such as neural activity recording with severe stimulation artifact, real-time stimulation artifact removal, and energy-efficient wireless power delivery.
Journal ArticleDOI

A 2.53 NEF 8-bit 10 kS/s 0.5 μm CMOS Neural Recording Read-Out Circuit with High Linearity for Neuromodulation Implants

Nishat T. Tasneem, +1 more
- 03 Mar 2021 - 
TL;DR: A power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver.
Journal ArticleDOI

Recording Strategies for High Channel Count, Densely Spaced Microelectrode Arrays.

TL;DR: In this article, a review of different implementation alternatives reported for time-division multiplexing neural recording systems, analyze their advantages and drawbacks, and suggest strategies for improving performance, as well as strategies to improve performance.
References
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Journal ArticleDOI

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

TL;DR: Initial results for a tetraplegic human using a pilot NMP suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.
Journal ArticleDOI

A low-power low-noise CMOS amplifier for neural recording applications

TL;DR: In this article, a low-noise low-power biosignal amplifiers capable of amplifying signals in the millihertz-to-kilohertz range while rejecting large dc offsets generated at the electrode-tissue interface is presented.
Journal ArticleDOI

Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease

TL;DR: High-frequency deep brain stimulation of the subthalamic nucleus is a powerful method that is currently unchallenged in the management of Parkinson's disease, but its long-term effects must be thoroughly assessed.
Journal ArticleDOI

A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System

TL;DR: A prototype integrated circuit for wireless neural recording from a 100-channel microelectrode array was developed and a two-chip system was used to record neural signals from a Utah Electrode Array in cat cortex and transmit the digitized signals wirelessly to a receiver.
Journal ArticleDOI

Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics

TL;DR: New technology to engineer the tissue-electrode interface, electrode design, and extraction algorithms to transform the recorded signal to movement will help translate exciting laboratory demonstrations to patient practice in the near future.
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