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.

翻译：与脊髓运动神经元进行接口时，一个尚未解决的主要问题是如何在源分离过程中准确补偿信号的非平稳效应，尤其是在这些效应无法预先估计的情况下。这迫使现有系统转而使用未分化的整体信号，从而限制了潜在的控制自由度。本研究提出一种可能的解决方案，利用无监督学习算法对驱动信号非平稳性的潜在过程效应进行盲校正。我们将该方法实现在准线性独立成分分析（ICA）的理论框架内。所提出的设计HarmonICA规避了非线性ICA的可辨识性问题，在保持学习非平稳潜在变量与其信号效应间复杂非线性关系能力的同时，获得了与线性ICA相当的预测性能。我们在模拟和真实的侵入式与非侵入式记录数据上测试HarmonICA，证明其能够针对各类记录特异性非平稳效应进行盲补偿，从而显著提升源分离过程的质量。