Deep Recurrent Convolutional Neural Networks for Classifying P300 Bci Signals
نویسندگان
چکیده
We develop and test three deep-learning recurrent convolutional architectures for learning to recognize single trial EEG event related potentials for P300 brain-computer interfaces (BCI)s. One advantage of the neural network solution is that it provides a natural way to share a lower-level feature space between subjects while adapting the classifier that works on that feature space. We compare the deep neural networks with the standard methods for P300 BCI classification.
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