Int J Neural Syst - Extraction of neural control commands using myoelectric pattern recognition: a novel application in adults with cerebral palsy.

Tópicos

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Resumo

This study investigates an electromyogram (EMG)-based neural interface toward hand rehabilitation for patients with cerebral palsy (CP). Forty-eight channels of surface EMG signals were recorded from the forearm of eight adult subjects with CP, while they tried to perform six different hand grasp patterns. A series of myoelectric pattern recognition analyses were performed to identify the movement intention of each subject with different EMG feature sets and classifiers. Our results indicate that across all subjects high accuracies (average overall classification accuracy > 98%) can be achieved in classification of six different hand movements, suggesting that there is substantial motor control information contained in paretic muscles of the CP subjects. Furthermore, with a feature selection analysis, it was found that a small number of ranked EMG features can maintain high classification accuracies comparable to those obtained using all the EMG features (average overall classification accuracy > 96% with 16 selected EMG features). The findings of the study suggest that myoelectric pattern recognition may be a useful control strategy for promoting hand rehabilitation in CP patients.

Resumo Limpo

studi investig electromyogram emgbas neural interfac toward hand rehabilit patient cerebr palsi cp fortyeight channel surfac emg signal record forearm eight adult subject cp tri perform six differ hand grasp pattern seri myoelectr pattern recognit analys perform identifi movement intent subject differ emg featur set classifi result indic across subject high accuraci averag overal classif accuraci can achiev classif six differ hand movement suggest substanti motor control inform contain paret muscl cp subject furthermor featur select analysi found small number rank emg featur can maintain high classif accuraci compar obtain use emg featur averag overal classif accuraci select emg featur find studi suggest myoelectr pattern recognit may use control strategi promot hand rehabilit cp patient

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