The disclosures relates to systems and methods for using a trained alignment neural network along with a trained latent representation model to achieve accurate alignment between complex neural signals arising from co-variation across neuron populations over time and their intended motor control that can be invariant for a much longer period without supervised recalibrations. In one implementation, the method may include receiving neural data for a period of time from one or more sensors. The method may further include transforming the neural data to generate aligned variables using a trained alignment network. The method may also include processing the aligned variables through a trained latent model to determine a latent spatiotemporal representation of one or more brain state variables for the period of time and decoding the latent spatiotemporal representation into a brain state for the period of time.
Systems and Methods for Nonlinear Latent Spatiotemporal Representation Alignment Decoding for Brain-Computer Interfaces