HIGH PERFORMANCE DECODING OF BEHAVIORAL INFORMATION FROM MEAN BACKGROUND ACTIVITY IN EXTRACELLULAR NEURAL RECORDINGS

Year 2018, Volume: 3 Issue: 2, 23 - 27, 31.12.2018

Murat Okatan Mehmet Kocatürk

Abstract

We have previously shown that the standard deviation
of background activity in bandpass filtered extracellular neural recording
snippets is strongly modulated by behavior such that it can be used to decode
behavioral variables with up to 100% accuracy. Here we show that the mean
background activity is also strongly modulated by behavior and that it too can
be used to decode behavioral variables with up to 100% accuracy. To the best of
our knowledge, our method extracts the weakest signal that has ever been
extracted from extracellular neural recordings, which can still be used to decode
a behavioral variable with very high accuracy. Our results demonstrate that both
the standard deviation and the mean of the background activity can be exploited
in brain-machine interfaces. 

Keywords

Computational Neuroscience, Truncation Thresholds, Amplitude Thresholding, Brain-Computer Interfaces

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