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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)
Department机器人学研究室
Source PublicationJOURNAL OF NEURAL ENGINEERING
ISSN1741-2560
2015
Volume12Issue:3
Indexed BySCI ; EI
EI Accession number20152200888155
WOS IDWOS:000354998600005
Contribution Rank1
Funding OrganizationNational Natural Science Foundation (NNSF) of China [61203368, 81470084, 61463024] ; National High Technology Research and Development Program of China (863 Program) [2012AA02A605]
KeywordEeg-fnirs HAnd Clenching Force And Speed Motor Imagery Joint Mutual Information (Jmi) Extreme Learning Machines (Elms)
AbstractObjective. 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.
Language英语
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
WOS SubjectEngineering, Biomedical ; Neurosciences
WOS KeywordNEAR-INFRARED SPECTROSCOPY ; BRAIN-COMPUTER-INTERFACE ; EXTREME LEARNING-MACHINE ; THEORETIC FEATURE-SELECTION ; OPTICAL PATHLENGTH ; MENTAL TASKS ; CLASSIFICATION ; COMMUNICATION ; RECOGNITION ; MOVEMENTS
WOS Research AreaEngineering ; Neurosciences & Neurology
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/16228
Collection机器人学研究室
Corresponding AuthorXu BL(徐保磊)
Affiliation1.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
Recommended Citation
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).
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