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A Comparative Study of Different Feature Extraction Methods for Motor Imagery EEG Decoding within the Same Upper Extremity
Chu YQ(褚亚奇)1,2,3; Zhao XG(赵新刚)1,2; Zou YJ(邹宜君)1,2,3; Zhang, He5; Xu WL(徐卫良)1,4; Zhao YW(赵忆文)1,2
Department机器人学研究室
Conference Name2018 Chinese Automation Congress (CAC)
Conference DateNovember 30 - December 2, 2018
Conference PlaceXi'an
Source Publication2018 Chinese Automation Congress (CAC)
PublisherIEEE
Publication PlaceNew York
2018
Pages330-335
Indexed ByEI ; CPCI(ISTP)
EI Accession number20191106633390
WOS IDWOS:000459239500063
Contribution Rank1
ISBN978-1-7281-1312-8
Keywordbrain-computer interfaces motor imagery EEG same upper extremity feature extraction common spatial patterns
AbstractCompared to other electroencephalogram (EEG) modalities, motor imagery (MI) based brain-computer interfaces (BCIs) can provide more natural and intuitive communication between human intentions and external machines. However, this type of BCI depends heavily on effective signal processing to discriminate EEG patterns corresponding to various MI tasks, especially feature extraction procedures. In this study, a comparison of different feature extraction methods was conducted for EEG classification of imaginary movements within the same upper extremity. Unlike traditional MI tasks (left/right hand), six imaginary movements from the same unilateral upper extremity were proposed and evaluated, including elbow flexion/extension, forearm supination/pronation, and hand grasp/open. To tackle the classification challenge of MI tasks within the same limb, four types of feature extraction methods were implemented and compared in combination with support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, such as wavelet transformation, power spectrum, autoregressive model, common spatial patterns (CSP) and variants of filter-bank CSP (FBCSP), regularized CSP (RCSP). The overall accuracies of the CSP were significant higher than other three types of feature extraction on a dataset collected from 8 individuals, particularly the SVM with FBCSP had the best performance with an average accuracy of 71.78%. These decoding results of MI tasks during single upper extremity are encouraging and promising in the context of more natural MI-BCI for controlling assisted devices, such as a neuroprosthetic or robotic arm for motor disabled individuals with highly impaired upper extremity.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23838
Collection机器人学研究室
Corresponding AuthorChu YQ(褚亚奇)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, Liaoning, 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences (CAS)
3.University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China
4.Department of Mechanical Engineering, University of Auckland, Auckland, 1142, New Zealand
5.Department of Orthopedics, Xinqiao Hospital, Third Military Medical University, Chongqing, 400038, China
Recommended Citation
GB/T 7714
Chu YQ,Zhao XG,Zou YJ,et al. A Comparative Study of Different Feature Extraction Methods for Motor Imagery EEG Decoding within the Same Upper Extremity[C]. New York:IEEE,2018:330-335.
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