File:Architectures of neural decoding.jpg
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Architectures of neural decoding
Summary[edit]
| Description |
English: (A) Naive Bayes models were trained separately on LFP and electroencephalogram (EEG) features, and the predicted kinematics were obtained through decision fusion and posterior probability from Bayes classifiers. (B) The DNN, consisting of LSTM and a convolutional layer, used MWP features in multiscale to predict movement. (C) TCN was used as the decoder for predicting kinematics. (D) The LFP-based decoder used LSTM, as illustrated in the gray-shaded block. |
| Date | |
| Source | https://spj.science.org/doi/full/10.34133/cbsystems.0044 |
| Author | YUANRUI DONG, SHIRONG WANG, QIANG HUANG, RUNE W. BERG, GUANGHUI LI, AND JIPING HE |
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| current | 16:38, 19 December 2023 | | 660 × 296 (182 KB) | DoctorNaturopath (talk | contribs) | Uploaded a work by YUANRUI DONG, SHIRONG WANG, QIANG HUANG, RUNE W. BERG, GUANGHUI LI, AND JIPING HE from https://spj.science.org/doi/full/10.34133/cbsystems.0044 with UploadWizard |
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