A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 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)
刊名	JOURNAL OF NEURAL ENGINEERING
	2015
卷号	12 期号:3
关键词	EEG-fNIRS hand clenching force and speed motor imagery joint mutual information (JMI) extreme learning machines (ELMs)
ISSN号	1741-2560
通讯作者	徐保磊
产权排序	1
中文摘要	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.
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
类目[WOS]	Engineering, Biomedical ; Neurosciences
研究领域[WOS]	Engineering ; Neurosciences & Neurology
关键词[WOS]	NEAR-INFRARED SPECTROSCOPY ; BRAIN-COMPUTER-INTERFACE ; EXTREME LEARNING-MACHINE ; THEORETIC FEATURE-SELECTION ; OPTICAL PATHLENGTH ; MENTAL TASKS ; CLASSIFICATION ; COMMUNICATION ; RECOGNITION ; MOVEMENTS
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000354998600005
内容类型	期刊论文
源URL	[http://ir.sia.ac.cn/handle/173321/16228]
专题	沈阳自动化研究所_机器人学研究室
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).