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• W2071729112 startingPage "836" @default.
• W2071729112 abstract "Dexterous finger movements can be decoded from neuronal action potentials acquired from a nonhuman primate using a chronically implanted Utah Electrode Array. We have developed an algorithm that can, after training, detect and classify individual and combined finger movements without any a priori knowledge of the data, task, or behavior. The algorithm is based on changes in the firing rates of individual neurons that are tuned for one or more finger movement types. Nine different movement types, which consisted of individual flexions, individual extensions, and combined flexions of the thumb, index finger, and middle finger, were decoded. The algorithm performed reliably on data recorded continuously during movement tasks, including a no-movement state, with an overall average sensitivity and specificity that were both >;92%. These results demonstrate a viable algorithm for decoding dexterous finger movements under conditions similar to those required for a real-world neural prosthetic application." @default.
• W2071729112 created "2016-06-24" @default.
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• W2071729112 date "2012-11-01" @default.
• W2071729112 modified "2023-09-27" @default.
• W2071729112 title "Decoding Dexterous Finger Movements in a Neural Prosthesis Model Approaching Real-World Conditions" @default.
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• W2071729112 doi "https://doi.org/10.1109/tnsre.2012.2210910" @default.
• W2071729112 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22875261" @default.
• W2071729112 hasPublicationYear "2012" @default.
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