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Peripheral neural signals can be used to estimate movement-specific muscle activation patterns for the purpose of human-machine interfacing (HMI). The available HMI solutions, however, provide limited movement decoding accuracy that often results in inadequate device control, especially in the dynamic tasks context, and require extensive algorithm training that is highly subject-specific. Here, we show that dexterous movements can be identified with high accuracy using a physiology-derived and information-theoretically optimised feature space that targets the spatio-temporal properties of the spiking activity of spinal motor neurons (neural features), decomposed from the interference myoelectric signal. Moreover, we show that the movement decoding accuracy based on these neural features is not influenced by the muscle activation level, reaching overall >98% in the full range of forces investigated and from processing intervals as short as 30-ms. Finally, we show that the high accuracy in individual finger movement recognition can be achieved without user-specific models. These results are the first to show a highly accurate discrimination of dexterous movement tasks in a wide range of muscle activation levels from near-real time processing intervals, with minimal subject-specific training, and thus are promising for the translation of HMI to daily use.

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Toward Universal Neural Interfaces for Daily Use: Decoding the Neural Drive to Muscles Generalises Highly Accurate Finger Task Identification Across Humans