Evidence map›Paper›PMID 28177925›Full record

ArticleJournal of neural engineering2017

Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law.

Francis R Willett, Brian A Murphy, William D Memberg, Christine H Blabe, Chethan Pandarinath, Benjamin L Walter, Jennifer A Sweet, Jonathan P Miller, Jaimie M Henderson, Krishna V Shenoy and 3 more

Abstract readCase Reports
In one paragraph

Article in Journal of neural engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Trial
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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

13 authors.

Francis R WillettDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.
Brian A MurphyDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.
William D MembergDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.
Christine H BlabeDepartment of Neurosurgery, Stanford University, Stanford, CA, United States of America.
Chethan PandarinathDepartment of Neurosurgery, Stanford University, Stanford, CA, United States of America.
Benjamin L WalterLouis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, United States of America.
Jennifer A SweetLouis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, United States of America.
Jonathan P MillerLouis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, United States of America.
Jaimie M HendersonDepartment of Neurosurgery, Stanford University, Stanford, CA, United States of America.
Krishna V ShenoyDepartment of Electrical Engineering, Stanford University, Stanford, CA, United States of America.
Leigh R HochbergDepartment of Veterans Affairs Medical Center, Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence, RI, United States of America.
Robert F KirschDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.
A Bolu AjiboyeDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America.

Funding

Engaging new cognitive and motor signals to improve communication prosthesesR01DC014034 · NIDCD · STANFORD UNIVERSITY · PI JAIMIE M HENDERSON · 2015 to 2026
$8.2M
Restoring Communication with an Intracortical Neural Interface SystemR01DC009899 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI HOCHBERG, LEIGH R · 2009 to 2017
$7.0M
Integrated Neural Engineering and Rehabilitation Training ProgramT32EB004314 · NIBIB · CASE WESTERN RESERVE UNIVERSITY · PI Jeffrey R Capadona, Robert F. Kirsch · 2004 to 2026
$6.1M
Toward an Animal Model of Freely Moving HumansR01NS066311 · NINDS · STANFORD UNIVERSITY · PI BLACK, MICHAEL J, SHENOY, KRISHNA V · 2009 to 2012
$2.9M
Intracortical control of FES-restored arm and hand function in people with SCIR01HD077220 · NICHD · CASE WESTERN RESERVE UNIVERSITY · PI KIRSCH, ROBERT F. · 2014 to 2017
$1.7M
Influencing Children's Beliefs Through LanguageR03HD053403 · NICHD · UNIVERSITY OF VIRGINIA · PI KEEN, RACHEL ELIZABETH · 2007 to 2008
$150k
NIBIB NIH HHS T32 EB004314NICHD NIH HHS N01 HD053403NICHD NIH HHS R01 HD077220NIDCD NIH HHS R01 DC009899NIDCD NIH HHS R01 DC014034NINDS NIH HHS R01 NS066311RRD VA A6779-IRRD VA B6453-R
6 · The paper itself

Abstract

objectiveDo movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]). APPROACH: Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law. MAIN

resultsWe found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. SIGNIFICANCE: The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.

Indexed as

ArtifactsBrain-Computer InterfacesEvoked PotentialsModels, NeurologicalMovementPsychomotor PerformanceComputer SimulationElectroencephalographyFemaleHumansMaleMiddle AgedReproducibility of ResultsSensitivity and SpecificitySignal-To-Noise Ratio

Identifiers

PMID28177925
PMCPMC5371026

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Registered trials

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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.