Motor Imagery Classification of Upper Limb Movements Based on Spectral Domain Features of EEG Patterns
文献类型：会议论文
| 作者 | Oluwarotimi Williams Samuel; Xiangxin Li; Yanjuan Geng; Peng Fang; Shixiong Chen; Guanglin Li |
| 出版日期 | 2017 |
| 会议日期 | 2017 |
| 会议地点 | JeJu Island, S. Korea, 11-15 July 2017 |
| 英文摘要 | Surface electromyography pattern recognition methods have been widely applied to decode limb movement intentions for prosthesis control. These methods generally require amputees to provide sufficient myoelectric signals from their residual limb muscles. Previous studies have shown that amputees with high level amputation or neuromuscular disorder usually do not have sufficient residual limb muscles to provide enough myoelectric signals for accurate identification of movements. Electroencephalography (EEG), another bioelectric signal associated with limb movements has also been proposed and used for decoding the limb motion intents of such amputees. With an attempt to improve the performance of EEG-based method in identifying motion classes, four spectral domain features of EEG were proposed in this study. Motor imagery patterns associated with five different classes of imagined upper limb movements were distinctively decoded based on the four features extracted from 64-channel EEG recordings in four transhumeral amputees. Experimental results show that an average accuracy of 97.81% was achieved across all the subjects and limb movement classes. By applying an iterative search channel selection method, an accuracy of around 95.00% was realized with about 20-channels of EEG. Thus, the proposed method might be potential for providing accurate control input for neuroprosthesis. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/12169]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2017 |
| 推荐引用方式 GB/T 7714 | Oluwarotimi Williams Samuel,Xiangxin Li,Yanjuan Geng,et al. Motor Imagery Classification of Upper Limb Movements Based on Spectral Domain Features of EEG Patterns[C]. 见:. JeJu Island, S. Korea, 11-15 July 2017. 2017. |
入库方式： OAI收割
来源：深圳先进技术研究院
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