Authors: Luke Bashford, Isabelle Anna Rosenthal, Spencer Kellis, David Bjanes, Kelsie Pejsa, Bingni W. Brunton and Richard A. Andersen
Publication: Journal of Neural Engineering
Date: August 12, 2024
Abstract
Objective A crucial goal in brain-machine interfacing is the long-term stability of neural decoding performance, ideally without regular retraining. Long-term stability has only been previously demonstrated in non-human primate experiments and only in primary sensorimotor cortices. Here we extend previous methods to determine long-term stability in humans by identifying and aligning low-dimensional structures in neural data. Approach Over a period of 1106 and 871 days respectively, two participants completed an imagined center-out reaching task. The longitudinal accuracy between all day pairs was assessed by latent subspace alignment using principal components analysis and canonical correlations analysis of multi-unit intracortical recordings in different brain regions (Brodmann Area 5, Anterior Intraparietal Area and the junction of the postcentral and intraparietal sulcus). Main Results We show the long-term stable representation of neural activity in subspaces of intracortical recordings from higher-order association areas in humans. Significance These results can be practically applied to significantly expand the longevity and generalizability of brain-computer interfaces.