Gravity Transform for Input Conditioning in Brain Machine Interfaces

Gravity transform measures cooperative neural activity being utilized for the analysis of neural assemblies. In this paper we verify the applicability of the gravity transform to specify components of neural assemblies, which could be combined, leading ultimately to a reduction of the input dimensionality in brain-machine interface models. Our analysis was performed on data collected from rats performing a lever pressing task. We compare the results from the gravity transform analysis with the assignment obtained through a sensitivity analysis applied on a linear optimal filter

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