A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine

被引：43

作者：

Li, Jingcong ^{[1
]}

Yu, Zhu Liang ^{[1
]}

Gu, Zhenghui ^{[1
]}

Wu, Wei ^{[1
]}

Li, Yuanqing ^{[1
]}

Jin, Lianwen ^{[2
]}

机构：

[1] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China

[2] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING | 2018年 / 26卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Event-related potential (ERP); deep learning; neural network; temporal feature; spatial filter; MENTAL PROSTHESIS; BRAIN; P300;

D O I：

10.1109/TNSRE.2018.2803066

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usually rely on ensemble averaging technique for reliable detection, which may obliterate subtle but important information in each trial of ERP signals. Inspired by deep learning methods, we propose a novel hybrid network termed ERP-NET. With hybrid deep structure, the proposed network is able to learn complex spatial and temporal patterns from single-trial ERP signals. To verify the effectiveness of ERP-NET, we carried out a few ERP detection experiments that the proposed model achieved cutting-edge performance. The experimental results demonstrate that the patterns learned by the ERP-NET are discriminative ERP components in which the ERP signals are properly characterized. More importantly, as an effective approach to single-trial analysis, ERP-NET is able to discover new ERP patterns which are significant to neuroscience study as well as BCI applications. Therefore, the proposed ERP-NET is a promising tool for the research on ERP signals.

引用

页码：563 / 572

页数：10

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