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- W3023550533 abstract "In the research of brain-computer interfaces, the neural electrophysiological signals are commonly used for neural information decoding. However, due to the difficulty in recording the neural electrophysiological signals of mice, especially spike, a brain-computer interface is seldom used. In this study, the local field potential (LFP) signals of the primary motor cortex of mice are recorded, and the power spectral density of the LFP is calculated as the input features. The SVM classification algorithm is used to decode the neural information of mice during the lever-pressing movement. The obtained results illustrate that the predicted movement has an early time between the real movement. To this end, a time-delay SVM model is built to decode the binary motion signal for the only four-channel LFP signal of the mice. The result shows that the time-delay SVM decoding achieved high-accuracy performance." @default.
- W3023550533 created "2020-05-13" @default.
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- W3023550533 date "2019-11-01" @default.
- W3023550533 modified "2023-09-23" @default.
- W3023550533 title "Time-delay-based neural decoding using LFP signals in the primary motor cortex of mice" @default.
- W3023550533 doi "https://doi.org/10.1360/n112018-00180" @default.
- W3023550533 hasPublicationYear "2019" @default.
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