Evidence map›Paper›PMID 37163408›Full record

ArticleIEEE transactions on biomedical circuits and systems2023

A Time-Domain Readout Technique for Neural Interfaces Based on VCO-Timestamping.

Fernando Cardes, Ebrahim Azizi, Andreas Hierlemann

Open access · greenAbstract read
In one paragraph

Article in IEEE transactions on biomedical circuits and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. A 4-Channel 0.23The ... Midwest Symposium on Circuits and Systems conference proceedings : MWSCAS. Midwest Symposium on Circuits and Systems · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Fernando Cardes
Ebrahim Azizi
Andreas Hierlemann
ETH Zurich · CH

Funding

European Research Council 694829Swiss National Science Foundation 188910
6 · The paper itself

Abstract

CMOS neural interfaces are aimed at studying the electrical activity of neurons and may help to restore lost functions of the nervous system in the future. The central function of most neural interfaces is the detection of extracellular electrical potentials by means of numerous microelectrodes positioned in close vicinity to the neurons. Modern neural interfaces require compact low-power, low-noise readout circuits, capable of recording from thousands of electrodes simultaneously without excessive area consumption and heat dissipation. In this article, we propose a novel readout technique for neural interfaces. The readout is based on a voltage-controlled oscillator (VCO), the frequency of which is modulated by the input voltage. The novelty of this work lies in the postprocessing of the VCO output, which is based on generating digital timestamps that contain temporal information about the oscillation. This method is potentially advantageous, because it requires mostly digital circuitry, which is more scalable than analog circuitry. Furthermore, most of the digital circuitry required for VCO-timestamping can be shared among several VCOs, rendering the architecture efficient for multi-channel architectures. This article introduces the VCO-timestamping concept, including theoretical derivations and simulations, and presents measurements of a prototype fabricated in 0.18-μm CMOS technology. The measured input-referred noise in the 300 Hz-5 kHz band was 5.7 μV

Indexed as

Amplifiers, ElectronicNeuronsAction PotentialsMicroelectrodesTechnology

Identifiers

PMID37163408
PMCPMC7614778
OpenAlexW4376131162

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.