We propose a novel method that combines James-Stein regression for feature extraction, and deep neural network for decoding; we refer to the architecture as ...
Jan 27, 2020 · The theory of non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for ...
We propose a novel method that combines. James-Stein regression for feature extraction, and deep neu- ral network for decoding; we refer to the architecture as ...
May 4, 2020 · We propose a novel method that combines James-Stein regression for feature extraction, and deep neural network for decoding; we refer to the ...
This paper leverages the robustness of several important results in non-parametric regression to harness the potentials of deep learning in limited data setups.
We propose an end-to-end cross-subject neural decoding system that 1) uses Pinsker's theorem to extract relevant features from scarce neural activity data ...
Deep James-Stein Neural Networks for Brain-Computer Interfaces ...
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Paper Title, DEEP JAMES-STEIN NEURAL NETWORKS FOR BRAIN-COMPUTER INTERFACES ; Authors, Marko Angjelichinoski, Mohammadreza Soltani, Duke University, United ...
May 5, 2020 · Paper Title, DEEP JAMES-STEIN NEURAL NETWORKS FOR BRAIN-COMPUTER INTERFACES ; Authors, Marko Angjelichinoski, Mohammadreza Soltani, Duke ...
Nov 17, 2020 · The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected ...
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