Emiliano Noce1, Alberto Dellacasa Bellingegni2, Anna Lisa Ciancio2, Rinaldo Sacchetti3, Angelo Davalli3, Eugenio Guglielmelli2, Loredana Zollo2. 1. Unit of Biomedical Robotics and Biomicrosystems, Università Campus Biomedico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy. Electronic address: e.noce@unicampus.it. 2. Unit of Biomedical Robotics and Biomicrosystems, Università Campus Biomedico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy. 3. INAIL Centro Protesi, via Rabuina 14, 40054 Vigorso di Budrio, Bologna, Italy.
Abstract
BACKGROUND: This paper proposes a new approach for neural control of hand prostheses, grounded on pattern recognition applied to the envelope of neural signals (eENG). NEW METHOD: The ENG envelope was computed by taking into account the amplitude and the occurrence of the spike in the neural recording. A pattern recognition algorithm applied on muscular signals was defined as a reference and a comparative analysis with traditionally adopted Spike Sorting Algorithms (SSA) for neural signals has been carried out. Method validation was divided in two parts: firstly, neural signals recorded from one amputee subject through intraneural electrodes were offline analyzed to discriminate between the two performed gestures; secondly, algorithm performance decay with the increase of the number of classes was studied through synthetic data. RESULTS: An accuracy of 98.26% with real data was reached with the pattern recognition applied to eENG. SSA reached an accuracy of 70%. Increasing the number of classes worsens the accuracy of this algorithm. Additionally, computational time for the pattern recognition applied to eENG is very low (32.6 μs for each sample in the data window analyzed). COMPARISON WITH EXISTING METHOD: The eENG was proved to be more reliable in decoding the user intention than the SSA algorithm and it is computationally efficient. CONCLUSIONS: It was demonstrated that it is possible to apply the well-known techniques of EMG pattern recognition to a conveniently processed neural signal and can pave the way to the application of neural gesture decoding in upper limb prosthetics.
BACKGROUND: This paper proposes a new approach for neural control of hand prostheses, grounded on pattern recognition applied to the envelope of neural signals (eENG). NEW METHOD: The ENG envelope was computed by taking into account the amplitude and the occurrence of the spike in the neural recording. A pattern recognition algorithm applied on muscular signals was defined as a reference and a comparative analysis with traditionally adopted Spike Sorting Algorithms (SSA) for neural signals has been carried out. Method validation was divided in two parts: firstly, neural signals recorded from one amputee subject through intraneural electrodes were offline analyzed to discriminate between the two performed gestures; secondly, algorithm performance decay with the increase of the number of classes was studied through synthetic data. RESULTS: An accuracy of 98.26% with real data was reached with the pattern recognition applied to eENG. SSA reached an accuracy of 70%. Increasing the number of classes worsens the accuracy of this algorithm. Additionally, computational time for the pattern recognition applied to eENG is very low (32.6 μs for each sample in the data window analyzed). COMPARISON WITH EXISTING METHOD: The eENG was proved to be more reliable in decoding the user intention than the SSA algorithm and it is computationally efficient. CONCLUSIONS: It was demonstrated that it is possible to apply the well-known techniques of EMG pattern recognition to a conveniently processed neural signal and can pave the way to the application of neural gesture decoding in upper limb prosthetics.
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Authors: Dario Farina; Ivan Vujaklija; Rickard Brånemark; Anthony M J Bull; Hans Dietl; Bernhard Graimann; Levi J Hargrove; Klaus-Peter Hoffmann; He Helen Huang; Thorvaldur Ingvarsson; Hilmar Bragi Janusson; Kristleifur Kristjánsson; Todd Kuiken; Silvestro Micera; Thomas Stieglitz; Agnes Sturma; Dustin Tyler; Richard F Ff Weir; Oskar C Aszmann Journal: Nat Biomed Eng Date: 2021-05-31 Impact factor: 25.671