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A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network
Chu YQ(褚亚奇)1,2,3; Zhao XG(赵新刚)1,2; Zou YJ(邹宜君)1,2,3; Xu WL(徐卫良)1,4; Han JD(韩建达)1,2; Zhao YW(赵忆文)1,2
Department机器人学研究室
Source PublicationFRONTIERS IN NEUROSCIENCE
ISSN1662-453X
2018
Volume12Pages:1-17
Indexed BySCI
WOS IDWOS:000445928200001
Contribution Rank1
Funding OrganizationNational Nature Science Foundation of China ; Chinese Academy of Sciences ; Liaoning Provincial Doctoral Starting Foundation of China
Keywordbrain-computer interface decoding scheme incomplete motor imagery EEG power spectral density deep belief network
AbstractHigh accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform,Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.
Language英语
WOS SubjectNeurosciences
WOS KeywordBRAIN-COMPUTER INTERFACES ; SENSORIMOTOR RHYTHMS ; COMPONENT ANALYSIS ; FEATURE-EXTRACTION ; CLASSIFICATION ; ELECTROENCEPHALOGRAM ; REHABILITATION ; ARTIFACTS ; ALGORITHM ; REMOVAL
WOS Research AreaNeurosciences & Neurology
Funding ProjectNational Nature Science Foundation of China[61503374] ; National Nature Science Foundation of China[61573340] ; Chinese Academy of Sciences[QYZDY-SSW-JSC005] ; Liaoning Provincial Doctoral Starting Foundation of China[201501032]
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/23353
Collection机器人学研究室
Corresponding AuthorZhao XG(赵新刚)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.2Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
3.3University of Chinese Academy of Sciences, Beijing, China
4.Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand
Recommended Citation
GB/T 7714
Chu YQ,Zhao XG,Zou YJ,et al. A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network[J]. FRONTIERS IN NEUROSCIENCE,2018,12:1-17.
APA Chu YQ,Zhao XG,Zou YJ,Xu WL,Han JD,&Zhao YW.(2018).A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network.FRONTIERS IN NEUROSCIENCE,12,1-17.
MLA Chu YQ,et al."A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network".FRONTIERS IN NEUROSCIENCE 12(2018):1-17.
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