Deep learning models for EEG-based rapid serial visual presentation event classification

被引：0

作者：

Zhang, Fu-Quan ^{[1
,2
]}

Mao, Zi-Jing ^{[3
]}

Huang, Yu-Fei ^{[3
]}

Xu, Lin ^{[4
]}

Ding, Gang Yi ^{[5
]}

机构：

[1] Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China

[2] School of Software, Beijing Institute of Technology, Room A9-301, Juyuan, Jinshan, Jiyuan, Fuzhou,350008, China

[3] Department of Electronic Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio Arrow Oaks 126, San Antonio,TX,78249, United States

[4] Innovative Information Industry Research Institute, Fujian Normal University, Fuqing Brunch of Fujian Normal University, Campus County 1, Longjiang Road, Fuqing,Fuzhou,350300, China

[5] School of Software, Beijing Institute of Technology, Room 611, Software Building, 5 South Zhongguancun Street, Haidian, Beijing,100081, China

来源：

Journal of Information Hiding and Multimedia Signal Processing | 2018年 / 9卷 / 01期

关键词：

Convolution - Image enhancement - Brain - Electroencephalography - Learning algorithms;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We consider deep learning (DL) for event classification using electroencephalogram (EEG) measurements of brain activities. We proposed HDNN or hierarchical deep neural network, and CNN4EEG, a new convolution neural network (CNN). Both DL models are designed to improve the representation of spatial and local temporal correlations inherent in EEG data. These models were tested for image target prediction in a time-locked rapid serial visual presentation (RSVP) experiment. The performances were compared with the state-of-the-art RSVP classification algorithms including HDCA and XDAWN and with other popular machine learning algorithms. The results show that global spatial local temporal CNN (CNN4EEG) achieved a 13% improvement over the best competing non-DL algorithm and a 9% improvement over canonical CNN for image processing, and a 6% over deep neural network (DNN). Our results suggest that the unique design of temporal and spatial filters in CNN4EEG can improve the representation of EEG data, hence the prediction performance. © 2018, Ubiquitous International. All rights reserved.

引用

页码：177 / 187