HarmonICA: Neural non-stationarity correction and source separation for motor neuron interfaces
This addresses a major bottleneck in motor neuron interfaces by enabling more accurate control, though it appears incremental as it builds on existing ICA frameworks.
The study tackled the problem of compensating for non-stationary effects in spinal motor neuron signals during source separation, proposing HarmonICA, an unsupervised learning algorithm that corrects for these effects and enhances source separation quality in both simulated and real recordings.
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.