Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors.
文献类型：期刊论文
| 作者 | Samuel, Oluwarotimi Williams ; Geng, Yanjuan ; Li, Xiangxin ; Li, Guanglin |
| 刊名 | JOURNAL OF MEDICAL SYSTEMS
 |
| 出版日期 | 2017 |
| 文献子类 | 期刊论文 |
| 英文摘要 | To control multiple degrees of freedom (MDoF) upper limb prostheses, pattern recognition (PR) ofelectromyogram (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 limbmovement 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 spectraldomain descriptors (SDDs) and 20 time domain descriptors (TDDs)) were used to decode multipleclasses 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 ofthe 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. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11948]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | JOURNAL OF MEDICAL SYSTEMS |
| 推荐引用方式 GB/T 7714 | Samuel, Oluwarotimi Williams , Geng, Yanjuan , Li, Xiangxin ,et al. Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors.[J]. JOURNAL OF MEDICAL SYSTEMS,2017. |
| APA | Samuel, Oluwarotimi Williams , Geng, Yanjuan , Li, Xiangxin ,& Li, Guanglin.(2017).Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors..JOURNAL OF MEDICAL SYSTEMS. |
| MLA | Samuel, Oluwarotimi Williams ,et al."Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors.".JOURNAL OF MEDICAL SYSTEMS (2017). |
入库方式： OAI收割
来源：深圳先进技术研究院
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