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

Details

• Alternative Title:
  J Neural Eng
• Personal Author:
  Stavisky, Sergey D. ; Kao, Jonathan C. ; Nuyujukian, Paul ; Ryu, Stephen I. ; Shenoy, Krishna V.
• Description:
  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.
  
  
• Subjects:
  Action Potentials Algorithms Animals Article Brain-Computer Interfaces Evoked Potentials, Motor Macaca Mulatta Male Motor Cortex Movement Reproducibility Of Results Sensitivity And Specificity

  More +
• Source:
  J Neural Eng. 12(3):036009.
• Pubmed ID:
  25946198
• Pubmed Central ID:
  PMC4457459
• Document Type:
  Journal Article
• Funding:
  8DP1HD075623/DP/NCCDPHP CDC HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; T32 MH020016/MH/NIMH NIH HHS/United States
• Volume:
  12
• Issue:
  3
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:7f39216abff4656c6f083cb9945485adc6883a9abfc1ca9e069c6591efc8ef41
• Download URL:
  https://stacks.cdc.gov/view/cdc/32210/cdc_32210_DS1.pdf
• File Type:
  [PDF - 926.62 KB ]