图 书 馆
基于振荡脑电的脑机接口设计：信号处理等Oscillatory EEG-based BCI design: signal processing and more |
|
| 课程网址: | http://videolectures.net/bbci2014_lotte_signal_processing/ |
| 主讲教师: | Fabien Lotte |
| 开课单位: | INRIA研究机构 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  本讲座针对基于振荡 EEG 活动（例如运动意象）的脑机接口 (BCI) 设计提出了一个易于理解的介绍，特别是从信号处理的角度来看。特别是，它首先介绍了设计这种 BCI 的基本特征提取和分类工具。然后，讲座描述了空间滤波器的使用，包括简单的静态滤波器（例如拉普拉斯算子）和高级监督滤波器（例如通用空间模式和变体），以提高整个 BCI 的性能和鲁棒性。也将考虑一些监督时间过滤器。然后将替代 EEG 特征表示作为对基本特征的有希望的补充公开。这尤其包括测量 EEG 信号复杂性的功能，更重要的是，测量来自不同大脑区域的 EEG 信号如何同步的功能。本讲座将通过简要向听众展示设计基于振荡活动的 BCI 并不仅仅与信号处理有关。事实上，考虑用户以及如何训练他/她控制 BCI 也是 BCI 设计成功的关键。 |
| 课程简介: | This lecture proposes an accessible introduction to the design of Brain-Computer Interfaces (BCI) based on oscillatory EEG activity (e.g., motor imagery), notably from a signal processing point of view. In particular, it first presents the basic feature extraction and classification tools to design such a BCI. The lecture then describes the use of spatial filters, both simple static ones (e.g., Laplacian) as well as advanced supervised ones (e.g., Common Spatial Patterns and variants) to enhance the performance and the robustness of the whole BCI. A few supervised temporal filters will be considered as well. Alternative EEG features representation are then exposed as promising additions to basic features. This notably includes features measuring the EEG signals complexity, and more importantly, features measuring how EEG signals from different brain areas are synchronized. This lecture will ends by briefly showing the audience that designing oscillatory activity-based BCI is not all about signal processing. Indeed, considering the user and how to train him/her to control the BCI is also a key point for successful BCI design. |
| 关 键 词: | 基于振荡 EEG 活动; 信号处理; 脑机接口 (BCI) |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-11：liyy |
| 最后编审: | 2021-06-11：liyy |
| 阅读次数: | 53 |