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Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors

  • Image & Signal Processing
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Abstract

To control multiple degrees of freedom (MDoF) upper limb prostheses, pattern recognition (PR) of electromyogram (EMG) signals has been successfully applied. This technique requires amputees to provide sufficient EMG signals to decode their limb movement intentions (LMIs). However, amputees with neuromuscular disorder/high level amputation often cannot provide sufficient EMG control signals, and thus the applicability of the EMG-PR technique is limited especially to this category of amputees. As an alternative approach, electroencephalograph (EEG) signals recorded non-invasively from the brain have been utilized to decode the LMIs of humans. However, most of the existing EEG based limb movement decoding methods primarily focus on identifying limited classes of upper limb movements. In addition, investigation on EEG feature extraction methods for the decoding of multiple classes of LMIs has rarely been considered. Therefore, 32 EEG feature extraction methods (including 12 spectral domain descriptors (SDDs) and 20 time domain descriptors (TDDs)) were used to decode multiple classes of motor imagery patterns associated with different upper limb movements based on 64-channel EEG recordings. From the obtained experimental results, the best individual TDD achieved an accuracy of 67.05 ± 3.12% as against 87.03 ± 2.26% for the best SDD. By applying a linear feature combination technique, an optimal set of combined TDDs recorded an average accuracy of 90.68% while that of the SDDs achieved an accuracy of 99.55% which were significantly higher than those of the individual TDD and SDD at p < 0.05. Our findings suggest that optimal feature set combination would yield a relatively high decoding accuracy that may improve the clinical robustness of MDoF neuroprosthesis.

Trial registration: The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.

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Funding

The Research work was supported in part by the National Natural Science Foundation of China under Grants (#91420301, #61403367), the National High-Tech. Research and Development Program of China under Grant (#2015AA042303), the Natural Science Foundation of Guangdong Province under Grant (#2016A030313179), the Shenzhen Governmental Basic Research Grant (#JCYJ20160331185848286, #JCYJ20150401145529005), and the Shenzhen High-level Oversea Talent Program (Shenzhen Peacock Plan) Grant (#KQCX2015033117354152), and the Outstanding Youth Innovation Research Fund of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (#Y7G016). Lastly, I (Oluwarotimi Williams Samuel) sincerely appreciate the support of CAS-TWAS President’s Fellowship to pursue a Ph.D. degree at the University of Chinese Academy of Sciences, Beijing, China.

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Authors and Affiliations

  1. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Shenzhen, 518055, China

    Oluwarotimi Williams Samuel, Yanjuan Geng, Xiangxin Li & Guanglin Li

  2. Institute of Biomedical and Health Engineering, SIAT, Chinese Academy of Sciences (CAS), Shenzhen, 518055, China

    Oluwarotimi Williams Samuel, Yanjuan Geng, Xiangxin Li & Guanglin Li

  3. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China

    Oluwarotimi Williams Samuel & Xiangxin Li

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  1. Oluwarotimi Williams Samuel
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  2. Yanjuan Geng
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Correspondence to Guanglin Li.

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Samuel, O.W., Geng, Y., Li, X. et al. Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors. J Med Syst 41, 194 (2017). https://doi.org/10.1007/s10916-017-0843-z

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