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

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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