Literature DB >> 28177925

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

Francis R Willett1, 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, Leigh R Hochberg, Robert F Kirsch, A Bolu Ajiboye.   

Abstract

OBJECTIVE: Do 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
RESULTS: We 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.

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Year:  2017        PMID: 28177925      PMCID: PMC5371026          DOI: 10.1088/1741-2552/aa5990

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  52 in total

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5.  A recurrent neural network for closed-loop intracortical brain-machine interface decoders.

Authors:  David Sussillo; Paul Nuyujukian; Joline M Fan; Jonathan C Kao; Sergey D Stavisky; Stephen Ryu; Krishna Shenoy
Journal:  J Neural Eng       Date:  2012-03-19       Impact factor: 5.379

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Authors:  Charles B Matlack; Howard Jay Chizeck; Chet T Moritz
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Authors:  Konstantinos P Michmizos; Hermano Igo Krebs
Journal:  Exp Brain Res       Date:  2013-11-23       Impact factor: 1.972

8.  Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity.

Authors:  Nathan A Fitzsimmons; Mikhail A Lebedev; Ian D Peikon; Miguel A L Nicolelis
Journal:  Front Integr Neurosci       Date:  2009-03-09

9.  Characterizing and predicting submovements during human three-dimensional arm reaches.

Authors:  James Y Liao; Robert F Kirsch
Journal:  PLoS One       Date:  2014-07-24       Impact factor: 3.240

10.  Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering.

Authors:  Maryam M Shanechi; Amy L Orsborn; Jose M Carmena
Journal:  PLoS Comput Biol       Date:  2016-04-01       Impact factor: 4.475

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  5 in total

1.  A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces.

Authors:  Francis R Willett; Brian A Murphy; Daniel R Young; William D Memberg; Christine H Blabe; Chethan Pandarinath; Brian Franco; Jad Saab; Benjamin L Walter; Jennifer A Sweet; Jonathan P Miller; Jaimie M Henderson; Krishna V Shenoy; John D Simeral; Beata Jarosiewicz; Leigh R Hochberg; Robert F Kirsch; Abidemi Bolu Ajiboye
Journal:  IEEE Trans Biomed Eng       Date:  2017-12-14       Impact factor: 4.538

Review 2.  The science and engineering behind sensitized brain-controlled bionic hands.

Authors:  Chethan Pandarinath; Sliman J Bensmaia
Journal:  Physiol Rev       Date:  2021-09-20       Impact factor: 37.312

3.  Power-saving design opportunities for wireless intracortical brain-computer interfaces.

Authors:  Nir Even-Chen; Dante G Muratore; Sergey D Stavisky; Leigh R Hochberg; Jaimie M Henderson; Boris Murmann; Krishna V Shenoy
Journal:  Nat Biomed Eng       Date:  2020-08-03       Impact factor: 25.671

4.  Signal processing methods for reducing artifacts in microelectrode brain recordings caused by functional electrical stimulation.

Authors:  D Young; F Willett; W D Memberg; B Murphy; B Walter; J Sweet; J Miller; L R Hochberg; R F Kirsch; A B Ajiboye
Journal:  J Neural Eng       Date:  2018-04       Impact factor: 5.379

5.  Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model.

Authors:  Francis R Willett; Daniel R Young; Brian A Murphy; William D Memberg; Christine H Blabe; Chethan Pandarinath; Sergey D Stavisky; Paymon Rezaii; Jad Saab; Benjamin L Walter; Jennifer A Sweet; Jonathan P Miller; Jaimie M Henderson; Krishna V Shenoy; John D Simeral; Beata Jarosiewicz; Leigh R Hochberg; Robert F Kirsch; A Bolu Ajiboye
Journal:  Sci Rep       Date:  2019-06-20       Impact factor: 4.379

  5 in total

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