Pun Thierry, Alecu Teodor Iulian, Chanel Guillaume, Kronegg Julien, Voloshynovskiy Sviatoslav
Computer Vision and Multimedia Laboratory, Computer Science Department, University of Geneva, CH-1211 Geneva, Switzerland.
IEEE Trans Neural Syst Rehabil Eng. 2006 Jun;14(2):210-3. doi: 10.1109/TNSRE.2006.875544.
This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.
本文介绍了瑞士日内瓦大学计算机视觉与多媒体实验室多模态交互小组在脑机交互（BCI）领域所开展的工作。这项工作的应用重点在于多模态交互，而非康复，即如何通过生理测量来增强传统交互。文中探讨了三个主要研究主题。第一个主题涉及从脑电图（EEG）中识别脑源活动这一更为普遍的问题。与传统的确定性方法不同，我们研究了基于迭代稳健随机的重建程序，对源和噪声统计进行建模，以克服当前技术的已知局限性。我们还开发了在电极放置和数量方面实现最佳脑电图（EEG）传感器系统设计的程序。第二个主题是从信息论的角度研究BCI协议和性能。为评估BCI能力，已对各种信息速率测量方法进行了比较。第三个研究主题涉及使用脑电图（EEG）和其他生理信号来评估用户的情绪状态。
