图 书 馆
皮质电图信号中手指屈曲的解剖约束解码Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals |
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| 课程网址: | http://videolectures.net/nips2011_ji_fingerflexion/ |
| 主讲教师: | Qiang Ji |
| 开课单位: | 伦斯勒理工学院 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these demonstrations usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target kinematic parameter. In this paper, we show that different types of anatomical constraints that govern finger flexion can be exploited in this context. Specifically, we incorporate these constraints in the construction, structure, and the probabilistic functions of a switched non-parametric dynamic system (SNDS) model. We then apply the resulting SNDS decoder to infer the flexion of individual fingers from the same ECoG dataset used in a recent study. Our results show that the application of the proposed model, which incorporates anatomical constraints, improves decoding performance compared to the results in the previous work. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation. |
| 关 键 词: | 皮质电图信号; 大脑; 脑电记录 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-28：yxd |
| 最后编审: | 2020-12-28：yxd |
| 阅读次数: | 39 |