Evidence map›Paper›PMID 32765212›Full record

ArticleFrontiers in neuroscience2020

Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces.

Mina Sadeghi Najafabadi, Longtu Chen, Kelsey Dutta, Ashley Norris, Bin Feng, Jan W H Schnupp, Nicole Rosskothen-Kuhl, Heather L Read, Monty A Escabí

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

9 authors.

Mina Sadeghi NajafabadiDepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States.
Longtu ChenDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.
Kelsey DuttaDepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States.
Ashley NorrisDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.
Bin FengDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.
Jan W H SchnuppDepartment of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong.
Nicole Rosskothen-KuhlDepartment of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong.
Heather L ReadDepartment of Biomedical Engineering, University of Connecticut, Storrs, CT, United States.
Monty A EscabíDepartment of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States.

Funding

The Role of Lumbar Splanchnic Innervations in Visceral Nociception and PainR01DK120824 · NIDDK · UNIVERSITY OF CONNECTICUT STORRS · PI FENG, BIN · 2019 to 2023
$2.0M
Mechanisms of DRG Stimulation to Modulate Visceral Afferent FunctionR03DK114546 · NIDDK · UNIVERSITY OF CONNECTICUT STORRS · PI FENG, BIN · 2018 to 2019
$160k
NIDDK NIH HHS R01 DK120824NIDDK NIH HHS R03 DK114546
6 · The paper itself

Abstract

Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including

Indexed as

artifact removalbrain machine interfacecochlear implantselectrical stimulationnerve fibersneural implantWiener filter

Identifiers

PMID32765212
PMCPMC7379342

What OpenQuestion holds

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LicenceCC BY
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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.