UC San Diego
Going Deeper with Recurrent Convolutional Neural Networks for ClassifyingP300 BCI Signals
- Author(s): Maddula, Ramesh Krishna
- Advisor(s): De Sa, Virginia
- et al.
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. The existing classifiers for P300 detection don't preserve the spatiotemporal
structure of the data, thereby losing local spatial and temporal patterns in the
data. The proposed models respect the spatial and temporal nature of the EEG signals.
A three-dimensional convolutional neural network (3D-CNN) is used in concert
with a two-dimensional convolutional neural network (2D-CNN) and LSTM to capture
the spatiotemporal patterns in the EEG signals. Moreover, a transfer learning based approach is applied while training the subjects. 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.