Towards Control of EEG-based Robotic Arm using Deep Learning via Stacked Sparse Autoencoder*
文献类型：会议论文
| 作者 | Zeyang Xia; Guanglin Li; Lin Wang; Oluwagbenga Paul Idowu; Peng Fang; Xiangxin Li |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Kuala Lumpur,Malaysia |
| 英文摘要 | Recently, an EEG-based robotic arm control has offered a key solution to the problem of high level amputation or severe neuromuscular damage. Several attempt to use EMG-based pattern recognition (PR) failed due to insufficient myoelectric signals from the residual limb to perform the control functions. The EEG activity recorded from the human scalp is used to control the movement of a robotic arm. This can either be achieved when the arm is attached to or separated from the amputee stump by interfacing the brain directly to the robotic arm through the brain-machine interface (BMI). To build an intelligent robotic (or prosthetics) system that can manipulate objects seamlessly with multiple degrees of freedom (DoF), it requires a robust learning algorithm which is able to learn how to control the robotic arm on its own when interacting with the environment. However, the conventional machine learning approach of using hand crafted features to design a robotcontroller that can perform multiple task is not a feasible option.Therefore, we proposed a robust learning control which is based on unsupervised learning algorithm of deep autoencoder. We applied stacked autoencoder to generate our features, and softmax layer was used to classify five different motor imagery tasks.The proposed method produced an overall accuracy of 98.9% across the four amputees recruited for the experiments.Our algorithm shows a better performance when compared with the state-of-the art classifiers. Thus, our results demonstrates the possibility of providing better control performance for EEG-based prosthesis |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14459]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Zeyang Xia,Guanglin Li,Lin Wang,et al. Towards Control of EEG-based Robotic Arm using Deep Learning via Stacked Sparse Autoencoder*[C]. 见:. Kuala Lumpur,Malaysia. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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