Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization.
Journal:
Journal of neural engineering
Published Date:
Oct 23, 2019
Abstract
OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process.