Robust Independent Component Analysis based EMG decomposition - A comparison study

High density surface Electromyography (HD-sEMG) provides a high fidelity measurement of the myoelectric activity that can be leveraged by EMG decomposition methods to estimate the motor neuron discharges. Independent Component Analysis (ICA) methods are used as basis for many EMG decomposition algorithms, for the estimation of motor unit action potential signals. Accurate source separation is a non-trivial task in EMG decomposition. While FastICA is widely used for this purpose, other methods with attractive characteristics, such as RobustICA, remain relatively unexplored. The purpose of the current work is to compare three different ICA-based EMG decomposition methods (FastICA, RobustICA and RobustICALCH) in terms of decomposition accuracy and computation time. The evaluation was performed on simulated data using a decomposition algorithm inspired by previous studies. Our results demonstrate that RobustICA outperforms the other methods in terms of number of correctly identified motor units, high decomposition accuracy, and low computation time, across different muscle contraction levels.