Decoding P300 Variability using Convolutional Neural Networks
Amelia J. Solon, Vernon J. Lawhern, Jonathan Touryan, Jonathan R. McDaniel, Anthony J. Ries, Stephen M. Gordon
The paper trains a convolutional neural network to decode P300 evoked responses from EEG data and shows it is sensitive to experimental variability and improves signal-to-noise ratio.
Frontiers in Human Neuroscience · Jun 14, 2019Peer-reviewedNeuroscience