Abstract
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.
Highlights
A fundamental role of the human nervous system is to generate goal-oriented behaviour
Since the neural drive was described by several features, we calculated the mutual information (MI) and Poisson probability density function (PDF) to minimise redundancy of the neural feature space
GENERALISATION ACROSS INDIVIDUALS Having demonstrated that the neural approach provides a successful multi-phase-and-degree of freedom (DOF) classification at all phases of the isometric muscle contraction, we addressed the question whether the neural drive decoded with the EMG decomposition can be used as a universal movement code across humans without user-specific training, and if so, how well does it generalise in comparison with the global EMG features
Summary
A fundamental role of the human nervous system is to generate goal-oriented behaviour. Behaviour, such as walking, eye-gazing or grasping, is expressed in the form of a controlled movement [1], [2]. Movement-decoding paradigms have the potential to replace the currently available, indirect interfacing that makes use of a ‘medium’ device, such as a switch or a keyboard, in order to perform an operation, and introduce direct machine control, with which the user can operate a device using different features and levels of muscle excitation [11]–[13]. The direct interfacing can change the way we use smart devices recreationally, but it can bring numerous clinical perks, such as novel rehabilitation strategies and improved neuroprosthetic control
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