A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network
文献类型：期刊论文
| 作者 | Zou YJ(邹宜君)2,3,4 ; Zhao XG(赵新刚)2,3; Zhao YW(赵忆文)2,3; Xu WL(徐卫良)1,2; Han JD(韩建达)2,3; Chu YQ(褚亚奇)2,3,4
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| 刊名 | FRONTIERS IN NEUROSCIENCE
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| 出版日期 | 2018 |
| 卷号 | 12页码:1-17 |
| 关键词 | brain-computer interface decoding scheme incomplete motor imagery EEG power spectral density deep belief network |
| ISSN号 | 1662-453X |
| 产权排序 | 1 |
| 英文摘要 | High 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. |
| WOS关键词 | BRAIN-COMPUTER INTERFACES ; SENSORIMOTOR RHYTHMS ; COMPONENT ANALYSIS ; FEATURE-EXTRACTION ; CLASSIFICATION ; ELECTROENCEPHALOGRAM ; REHABILITATION ; ARTIFACTS ; ALGORITHM ; REMOVAL |
| 资助项目 | National 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] |
| WOS研究方向 | Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000445928200001 |
| 资助机构 | National Nature Science Foundation of China ; Chinese Academy of Sciences ; Liaoning Provincial Doctoral Starting Foundation of China |
| 源URL | [http://ir.sia.cn/handle/173321/23353]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Zhao XG(赵新刚) |
| 作者单位 | 1.Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 3.2Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 4.3University of Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Zou YJ,Zhao XG,Zhao YW,et al. A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network[J]. FRONTIERS IN NEUROSCIENCE,2018,12:1-17. |
| APA | Zou YJ,Zhao XG,Zhao YW,Xu WL,Han JD,&Chu YQ.(2018).A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network.FRONTIERS IN NEUROSCIENCE,12,1-17. |
| MLA | Zou YJ,et al."A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network".FRONTIERS IN NEUROSCIENCE 12(2018):1-17. |
入库方式： OAI收割
来源：沈阳自动化研究所
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