  A major outstanding problem when interfacing with spinal motor neurons is how
to accurately compensate for non-stationary effects in the signal during source
separation routines, particularly when they cannot be estimated in advance.
This forces current systems to instead use undifferentiated bulk signal, which
limits the potential degrees of freedom for control. In this study we propose a
potential solution, using an unsupervised learning algorithm to blindly correct
for the effects of latent processes which drive the signal non-stationarities.
We implement this methodology within the theoretical framework of a quasilinear
version of independent component analysis (ICA). The proposed design,
HarmonICA, sidesteps the identifiability problems of nonlinear ICA, allowing
for equivalent predictability to linear ICA whilst retaining the ability to
learn complex nonlinear relationships between non-stationary latents and their
effects on the signal. We test HarmonICA on both invasive and non-invasive
recordings both simulated and real, demonstrating an ability to blindly
compensate for the non-stationary effects specific to each, and thus to
significantly enhance the quality of a source separation routine.
