Visualizing extracted feature by deep learning in P300 discrimination task

Abstract

P300 speller is a system that allows users to input words using electroencephalogram (EEG). A component called P300 is used to interpret the EEG in P300 speller. In order to make a high performance P300 speller, it is essential to discriminate P300 from nonP300 precisely and automatically. In this study, deep learning (DL) is used to discriminate P300. The experimental result shows that DL was possible to discriminate P300 in EEG data, especially in the higher level layer. Furthermore, this study refers to the extracted feature by DL. We can see that DL learns feature from the waveforms correctly to discriminate P300 from others.