Unsupervised neural decoding of signals recorded by thin-film electrode arrays implanted in muscles using autoencoding with a physiologically derived optimisation criterion

Abstract

We present an autoencoder that learns the discharge times of individual motor units (MU) by processing multichannel electromyogram (EMG) recordings obtained by electrode arrays implanted in muscles, thereby providing a neural interface with the spinal cord. To this end, after preprocessing of the EMG via convolutive sphering, the encoder is constrained to apply an orthogonal transformation to the spatial data and the optimisation criterion enforces a temporal sparsity constraint. These constraints are based on the theoretical modelling of EMG generation. This decomposition method was evaluated on both simulated and experimental signals. For simulated signals, with a ratio of 1.5 between observations and sources, the average detection accuracy was 94% for up to 60 sources for SNR 20 dB. Moreover, the detection accuracy did not significantly decrease when decreasing the SNR to values as low as 0 dB. Experimental signals were collected in humans with thin-film invasive electrodes implanted in the tibialis anterior muscle. The results showed an accuracy on experimental data >90%. Moreover, the proposed method outperformed a state-of-the-art blind source separation approach in terms of the number of reliably detected motor units. In conclusion, we have proposed the first fully unsupervised neural network approach to the problem of neural decoding of intramuscular EMG time series by translating the available theoretical knowledge on the signal properties into an autoencoding architecture tailored to the decomposition problem.