  Objective. We consider the cross-subject decoding problem from local field
potential (LFP) signals, where training data collected from the prefrontal
cortex (PFC) of a source subject is used to decode intended motor actions in a
destination subject. Approach. We propose a novel supervised transfer learning
technique, referred to as data centering, which is used to adapt the feature
space of the source to the feature space of the destination. The key
ingredients of data centering are the transfer functions used to model the
deterministic component of the relationship between the source and destination
feature spaces. We propose an efficient data-driven estimation approach for
linear transfer functions that uses the first and second order moments of the
class-conditional distributions. Main result. We apply our data centering
technique with linear transfer functions for cross-subject decoding of eye
movement intentions in an experiment where two macaque monkeys perform
memory-guided visual saccades to one of eight target locations. The results
show peak cross-subject decoding performance of $80\%$, which marks a
substantial improvement over random choice decoder. In addition to this, data
centering also outperforms standard sampling-based methods in setups with
imbalanced training data. Significance. The analyses presented herein
demonstrate that the proposed data centering is a viable novel technique for
reliable LFP-based cross-subject brain-computer interfacing and neural
prostheses.
