Early Visuomotor Representations Revealed From Evoked Local Field Potentials in Motor and Premotor Cortical Areas

Local field potentials (LFPs) recorded from primary motor cortex (MI) have been shown to be tuned to the direction of visually guided reaching movements, but MI LFPs have not been shown to be tuned to the direction of an upcoming movement during the delay period that precedes movement in an instructed-delay reaching task. Also, LFPs in dorsal premotor cortex (PMd) have not been investigated in this context. We therefore recorded LFPs from MI and PMd of monkeys ( Macaca mulatta) and investigated whether these LFPs were tuned to the direction of the upcoming movement during the delay period. In three frequency bands we identified LFP activity that was phase-locked to the onset of the instruction stimulus that specified the direction of the upcoming reach. The amplitude of this activity was often tuned to target direction with tuning widths that varied across different electrodes and frequency bands. Single-trial decoding of LFPs demonstrated that prediction of target direction from this activity was possible well before the actual movement is initiated. Decoding performance was significantly better in the slowest-frequency band compared with that in the other two higher-frequency bands. Although these results demonstrate that task-related information is available in the local field potentials, correlations among these signals recorded from a densely packed array of electrodes suggests that adequate decoding performance for neural prosthesis applications may be limited as the number of simultaneous electrode recordings is increased.

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

10.1101/015750 ◽

2015 ◽

Author(s):

Sergey D Stavisky ◽

Jonathan C Kao ◽

Paul Nuyujukian ◽

Stephen I Ryu ◽

Krishna V Shenoy

Keyword(s):

Local Field ◽

High Performance ◽

Premotor Cortex ◽

Local Field Potentials ◽

Low Frequency ◽

Control Signal ◽

Multielectrode Arrays ◽

Field Potentials ◽

Reaching Task ◽

Improved Performance

Objective. Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main Results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

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Local field potentials allow accurate decoding of muscle activity

Journal of Neurophysiology ◽

10.1152/jn.00832.2011 ◽

2012 ◽

Vol 108 (1) ◽

pp. 18-24 ◽

Cited By ~ 47

Author(s):

Robert D. Flint ◽

Christian Ethier ◽

Emily R. Oby ◽

Lee E. Miller ◽

Marc W. Slutzky

Keyword(s):

Upper Limb ◽

Local Field ◽

Muscle Activity ◽

Primary Motor Cortex ◽

Target Location ◽

Local Field Potentials ◽

Field Potentials ◽

Reach To Grasp ◽

Significant Information ◽

Complex Movement

Local field potentials (LFPs) in primary motor cortex include significant information about reach target location and upper limb movement kinematics. Some evidence suggests that they may be a more robust, longer-lasting signal than action potentials (spikes). Here we assess whether LFPs can also be used to decode upper limb muscle activity, a complex movement-related signal. We record electromyograms from both proximal and distal upper limb muscles from monkeys performing a variety of reach-to-grasp and isometric wrist force tasks. We show that LFPs can be used to decode activity from both proximal and distal muscles with performance rivaling that of spikes. Thus, motor cortical LFPs include information about more aspects of movement than has been previously demonstrated. This provides further evidence suggesting that LFPs could provide a highly informative, long-lasting signal source for neural prostheses.

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Force decoding using local field potentials in primary motor cortex: PLS or Kalman filter regression?

Physical and Engineering Sciences in Medicine ◽

10.1007/s13246-019-00833-7 ◽

2020 ◽

Vol 43 (1) ◽

pp. 175-186 ◽

Cited By ~ 2

Author(s):

Nargess Heydari Beni ◽

Reza Foodeh ◽

Vahid Shalchyan ◽

Mohammad Reza Daliri

Keyword(s):

Kalman Filter ◽

Motor Cortex ◽

Local Field ◽

Primary Motor Cortex ◽

Local Field Potentials ◽

Field Potentials

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Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

eNeuro ◽

10.1523/eneuro.0178-19.2019 ◽

2019 ◽

Vol 6 (3) ◽

pp. ENEURO.0178-19.2019 ◽

Cited By ~ 4

Author(s):

Junmo An ◽

Taruna Yadav ◽

John P. Hessburg ◽

Joseph T. Francis

Keyword(s):

Motor Cortex ◽

Local Field ◽

Primary Motor Cortex ◽

Local Field Potentials ◽

Field Potentials ◽

Field Coherence ◽

Spiking Activity

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

Journal of Neurophysiology ◽

10.1152/jn.00293.2014 ◽

2015 ◽

Vol 114 (3) ◽

pp. 1500-1512 ◽

Cited By ~ 29

Author(s):

Sagi Perel ◽

Patrick T. Sadtler ◽

Emily R. Oby ◽

Stephen I. Ryu ◽

Elizabeth C. Tyler-Kabara ◽

...

Keyword(s):

Motor Cortex ◽

Local Field ◽

Unit Activity ◽

Single Unit ◽

Primary Motor Cortex ◽

Spatial Scales ◽

Local Field Potentials ◽

Single Unit Activity ◽

Field Potentials ◽

Threshold Crossing

A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the “beta band” (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.

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