SIA OpenIR  > 机器人学研究室
A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching
Yin XX(尹旭贤); Xu BL(徐保磊); Jiang ZH(蒋长好); Fu YF(伏云发); Wang ZD(王志东); Li HY(李洪谊); Shi G(石刚)
作者部门机器人学研究室
关键词Eeg-fnirs HAnd Clenching Force And Speed Motor Imagery Joint Mutual Information (Jmi) Extreme Learning Machines (Elms)
发表期刊JOURNAL OF NEURAL ENGINEERING
ISSN1741-2560
2015
卷号12期号:3
收录类别SCI ; EI
EI收录号20152200888155
WOS记录号WOS:000354998600005
产权排序1
资助机构National Natural Science Foundation (NNSF) of China [61203368, 81470084, 61463024] ; National High Technology Research and Development Program of China (863 Program) [2012AA02A605]
摘要Objective. In order to increase the number of states classified by a brain-computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching. Approach. The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxyhemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs). Main results. In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG-fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% +/- 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature. Significance. Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.
语种英语
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
WOS类目Engineering, Biomedical ; Neurosciences
关键词[WOS]NEAR-INFRARED SPECTROSCOPY ; BRAIN-COMPUTER-INTERFACE ; EXTREME LEARNING-MACHINE ; THEORETIC FEATURE-SELECTION ; OPTICAL PATHLENGTH ; MENTAL TASKS ; CLASSIFICATION ; COMMUNICATION ; RECOGNITION ; MOVEMENTS
WOS研究方向Engineering ; Neurosciences & Neurology
引用统计
被引频次:28[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/16228
专题机器人学研究室
通讯作者Xu BL(徐保磊)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Key Laboratory of Motor and Brain Imaging, Capital Institute of Physical Education, Beijing, China
4.School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, China
5.Dept. of Advanced Robotics, Chiba Institute of Technology, Chiba, Japan
6.School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
推荐引用方式
GB/T 7714
Yin XX,Xu BL,Jiang ZH,et al. A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching[J]. JOURNAL OF NEURAL ENGINEERING,2015,12(3).
APA Yin XX.,Xu BL.,Jiang ZH.,Fu YF.,Wang ZD.,...&Shi G.(2015).A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching.JOURNAL OF NEURAL ENGINEERING,12(3).
MLA Yin XX,et al."A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching".JOURNAL OF NEURAL ENGINEERING 12.3(2015).
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
A hybrid BCI based o(1428KB)期刊论文作者接受稿开放获取ODC PDDL浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Yin XX(尹旭贤)]的文章
[Xu BL(徐保磊)]的文章
[Jiang ZH(蒋长好)]的文章
百度学术
百度学术中相似的文章
[Yin XX(尹旭贤)]的文章
[Xu BL(徐保磊)]的文章
[Jiang ZH(蒋长好)]的文章
必应学术
必应学术中相似的文章
[Yin XX(尹旭贤)]的文章
[Xu BL(徐保磊)]的文章
[Jiang ZH(蒋长好)]的文章
相关权益政策
暂无数据
收藏/分享
文件名: A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。