Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces

2014 ◽  
Author(s):  
Sergey D. Stavisky ◽  
Jonathan C. Kao ◽  
Paul Nuyujukian ◽  
Stephen I. Ryu ◽  
Krishna V. Shenoy
Keyword(s):  
Local Field ◽  
Local Field Potentials ◽  
Low Frequency ◽  
Field Potentials ◽  
Brain Machine Interfaces

scholarly journals Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information

2008 ◽  
Vol 28 (22) ◽  
pp. 5696-5709 ◽  
Author(s):  
A. Belitski ◽  
A. Gretton ◽  
C. Magri ◽  
Y. Murayama ◽  
M. A. Montemurro ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Local Field ◽  
Visual Information ◽  
Local Field Potentials ◽  
Low Frequency ◽  
Field Potentials

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

2015 ◽  
Vol 12 (3) ◽  
pp. 036009 ◽  
Author(s):  
Sergey D Stavisky ◽  
Jonathan C Kao ◽  
Paul Nuyujukian ◽  
Stephen I Ryu ◽  
Krishna V Shenoy
Keyword(s):  
Local Field ◽  
Local Field Potentials ◽  
Low Frequency ◽  
Brain Machine Interface ◽  
Field Potentials ◽  
High Performing ◽  
Machine Interface

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

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.

scholarly journals Inferring Spike Trains From Local Field Potentials

2008 ◽  
Vol 99 (3) ◽  
pp. 1461-1476 ◽  
Author(s):  
Malte J. Rasch ◽  
Arthur Gretton ◽  
Yusuke Murayama ◽  
Wolfgang Maass ◽  
Nikos K. Logothetis
Keyword(s):  
Spontaneous Activity ◽  
Local Field ◽  
Local Field Potentials ◽  
Low Frequency ◽  
Spike Trains ◽  
Dorsal Lateral Geniculate Nucleus ◽  
Support Vector ◽  
Field Potentials ◽  
Data Set ◽  
Spiking Activity

We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation across electrodes, the low-frequency structure in spike trains (in the 100-ms range) can be inferred with reasonable accuracy, whereas exact spike positions are not reliably predicted. Two kinds of features of the LFP are exploited for prediction: the frequency power of bands in the high γ-range (40–90 Hz) and information contained in low-frequency oscillations (<10 Hz), where both phase and power modulations are informative. Information analysis revealed that both features code (mainly) independent aspects of the spike-to-LFP relationship, with the low-frequency LFP phase coding for temporally clustered spiking activity. Although both features and prediction quality are similar during seminatural movie stimuli and spontaneous activity, prediction performance during spontaneous activity degrades much more slowly with increasing electrode distance. The general trend of data obtained with anesthetized animals is qualitatively mirrored in that of a more limited data set recorded in V1 of non-anesthetized monkeys. In contrast to the cortical field potentials, thalamic LFPs (e.g., LFPs derived from recordings in the dorsal lateral geniculate nucleus) hold no useful information for predicting spiking activity.

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