… To design the training phase of neural network, which greatly influences the decoding
performance of NND, we need solve two problems. First, how to generate training samples. …

… neural network decoders When using recurrent neural networks (RNNs) for decoding, we …
Recurrent neural networks explicitly model temporal transitions across time with a persistent …

… codes, developing good and efficient decoders still remains a challenge. In our … decoders
based on feedforward neural networks. To demonstrate adaptability, we apply neural decoders …

… The ability to decode the activity of a neural network is a tremendous advantage both for …
interpretation of neuronal output, decoding allows the complex activity of a neural circuit to be …

… neural networks (DNN) has achieved significant accuracy improvements in speech recognition,
it is computationally expensive to deploy large-scale DNN in decoding … for fast decoding. …

… In Section 4, many parameters of the neural network based decoders are discussed. In
Section 5, we provide the results with the best neural network based decoder for the different …

… common spatial patterns (FBCSP), the established baseline decoding method referred to
throughout this study, and of convolutional neural networks (ConvNets). Next, we describe the …

… The neurons of HL and TL type are used in the neural net decoder described in this paper.
The TL-type neuron is assumed to operate within its linear range. Both neurons used here …

… neural activation patterns with intended user actions. Here, we introduce a new deep neural
network 16 decoding … offline results demonstrating that our decoder is highly accurate, …

… This time slice corresponds to the time that is needed for traversing the neural network
hierarchy. For all decoding analyses we again selected the sources that reached higher …