Literature DB >> 22733013

Accurate decoding of reaching movements from field potentials in the absence of spikes.

Robert D Flint1, Eric W Lindberg, Luke R Jordan, Lee E Miller, Marc W Slutzky.   

Abstract

The recent explosion of interest in brain-machine interfaces (BMIs) has spurred research into choosing the optimal input signal source for a desired application. The signals with highest bandwidth--single neuron action potentials or spikes--typically are difficult to record for more than a few years after implantation of intracortical electrodes. Fortunately, field potentials recorded within the cortex (local field potentials, LFPs), at its surface (electrocorticograms, ECoG) and at the dural surface (epidural, EFPs) have also been shown to contain significant information about movement. However, the relative performance of these signals has not yet been directly compared. Furthermore, while it is widely postulated, it has not yet been demonstrated that these field potential signals are more durable than spike recordings. The aim of this study was to address both of these questions. We assessed the offline decoding performance of EFPs, LFPs and spikes, recorded sequentially, in primary motor cortex (M1) in terms of their ability to decode the target of reaching movements, as well as the endpoint trajectory. We also examined the decoding performance of LFPs on electrodes that are not recording spikes, compared with the performance when they did record spikes. Spikes were still present on some of the other electrodes throughout this study. We showed that LFPs performed nearly as well as spikes in decoding velocity, and slightly worse in decoding position and in target classification. EFP performance was slightly inferior to that reported for ECoG in humans. We also provided evidence demonstrating that movement-related information in the LFP remains high regardless of the ability to record spikes concurrently on the same electrodes. This is the first study to provide evidence that LFPs retain information about movement in the absence of spikes on the same electrodes. These results suggest that LFPs may indeed remain informative after spike recordings are lost, thereby providing a robust, accurate signal source for BMIs.

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Year:  2012        PMID: 22733013      PMCID: PMC3429374          DOI: 10.1088/1741-2560/9/4/046006

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


  61 in total

1.  Local field potentials allow accurate decoding of muscle activity.

Authors:  Robert D Flint; Christian Ethier; Emily R Oby; Lee E Miller; Marc W Slutzky
Journal:  J Neurophysiol       Date:  2012-04-11       Impact factor: 2.714

2.  Cortical local field potential encodes movement intentions in the posterior parietal cortex.

Authors:  Hansjörg Scherberger; Murray R Jarvis; Richard A Andersen
Journal:  Neuron       Date:  2005-04-21       Impact factor: 17.173

Review 3.  Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks.

Authors:  Marlene Bartos; Imre Vida; Peter Jonas
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4.  Early visuomotor representations revealed from evoked local field potentials in motor and premotor cortical areas.

Authors:  John G O'Leary; Nicholas G Hatsopoulos
Journal:  J Neurophysiol       Date:  2006-05-31       Impact factor: 2.714

5.  Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements.

Authors:  J P Donoghue; J N Sanes; N G Hatsopoulos; G Gaál
Journal:  J Neurophysiol       Date:  1998-01       Impact factor: 2.714

6.  Nonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex.

Authors:  Charles M Gaona; Mohit Sharma; Zachary V Freudenburg; Jonathan D Breshears; David T Bundy; Jarod Roland; Dennis L Barbour; Gerwin Schalk; Eric C Leuthardt
Journal:  J Neurosci       Date:  2011-02-09       Impact factor: 6.167

7.  Instant neural control of a movement signal.

Authors:  Mijail D Serruya; Nicholas G Hatsopoulos; Liam Paninski; Matthew R Fellows; John P Donoghue
Journal:  Nature       Date:  2002-03-14       Impact factor: 49.962

8.  Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans.

Authors:  Jeremy R Manning; Joshua Jacobs; Itzhak Fried; Michael J Kahana
Journal:  J Neurosci       Date:  2009-10-28       Impact factor: 6.167

9.  Cortical representation of ipsilateral arm movements in monkey and man.

Authors:  Karunesh Ganguly; Lavi Secundo; Gireeja Ranade; Amy Orsborn; Edward F Chang; Dragan F Dimitrov; Jonathan D Wallis; Nicholas M Barbaro; Robert T Knight; Jose M Carmena
Journal:  J Neurosci       Date:  2009-10-14       Impact factor: 6.167

10.  Emergence of a stable cortical map for neuroprosthetic control.

Authors:  Karunesh Ganguly; Jose M Carmena
Journal:  PLoS Biol       Date:  2009-07-21       Impact factor: 8.029

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  65 in total

1.  Evaluation of local field potential signals in decoding of visual attention.

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2.  A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.

Authors:  Samuel R Nason; Alex K Vaskov; Matthew S Willsey; Elissa J Welle; Hyochan An; Philip P Vu; Autumn J Bullard; Chrono S Nu; Jonathan C Kao; Krishna V Shenoy; Taekwang Jang; Hun-Seok Kim; David Blaauw; Parag G Patil; Cynthia A Chestek
Journal:  Nat Biomed Eng       Date:  2020-07-27       Impact factor: 25.671

3.  Characterization of the effects of the human dura on macro- and micro-electrocorticographic recordings.

Authors:  David T Bundy; Erik Zellmer; Charles M Gaona; Mohit Sharma; Nicholas Szrama; Carl Hacker; Zachary V Freudenburg; Amy Daitch; Daniel W Moran; Eric C Leuthardt
Journal:  J Neural Eng       Date:  2014-02       Impact factor: 5.379

4.  Decoding three-dimensional reaching movements using electrocorticographic signals in humans.

Authors:  David T Bundy; Mrinal Pahwa; Nicholas Szrama; Eric C Leuthardt
Journal:  J Neural Eng       Date:  2016-02-23       Impact factor: 5.379

5.  A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes.

Authors:  Sergey D Stavisky; Jonathan C Kao; Paul Nuyujukian; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neural Eng       Date:  2015-05-06       Impact factor: 5.379

6.  Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics.

Authors:  Sagi Perel; Patrick T Sadtler; Emily R Oby; Stephen I Ryu; Elizabeth C Tyler-Kabara; Aaron P Batista; Steven M Chase
Journal:  J Neurophysiol       Date:  2015-07-01       Impact factor: 2.714

Review 7.  Physiological properties of brain-machine interface input signals.

Authors:  Marc W Slutzky; Robert D Flint
Journal:  J Neurophysiol       Date:  2017-06-14       Impact factor: 2.714

8.  Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

Authors:  A R Marathe; D M Taylor
Journal:  J Neural Eng       Date:  2013-04-23       Impact factor: 5.379

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

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10.  Multi-View Broad Learning System for Primate Oculomotor Decision Decoding.

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Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-06-18       Impact factor: 3.802

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