Deep learning based on Batch Normalization for P300 signal detection

Volume: 275, Pages: 288 - 297
Published: Jan 31, 2018
Abstract
Detecting P300 signals from electroencephalography (EEG) is the key to establishing a P300 speller, which is a type of braincomputer interface (BCI) system based on the oddball paradigm that allows users to type messages simply by controlling eye-gazes. The convolutional neural network (CNN) is an approach that has achieved good P300 detection performances. However, the standard CNN may be prone to overfitting and the convergence may be slow. To...
Paper Details
Title
Deep learning based on Batch Normalization for P300 signal detection
Published Date
Jan 31, 2018
Volume
275
Pages
288 - 297
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