A Hybrid BCI Study: Temporal Optimization for EEG Single-trial Classification by Exploring Hemodynamics from the Simultaneously Measured NIRS Data

被引：0

作者：

Shu, Xiaokang ^{[1
]}

Yao, Lin ^{[1
]}

Sheng, Xinjun ^{[1
]}

Zhang, Dingguo ^{[1
]}

Zhu, Xiangyang ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai, Peoples R China

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS IEEE-ROBIO 2014 | 2014年

关键词：

BRAIN-COMPUTER-INTERFACE; FNIRS; SYSTEM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we introduced a new method to optimally select the time window for a single-trial classification problem in BCI system. As a hybrid-BCI, we combine EEG and NIRS signals to improve the performance of BCI system. Since there's a coupled relationship between EEG and NIRS, we try to define the activation state of subject's brain according to the changes of hemoglobin. We therefore defined the maximum point of HbO changes to be the time when the brain was fully activated. Then we chose the EEG data according to this critical time point with a 3 s window, which is almost within 6-9s according to the NIRS signal. With this selected time window, there is a significantly improvement of decoding accuracy from 69% to 79% compared to the original time window (1-12 s).

引用

页码：914 / 918

页数：5

共 37 条

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