Scherer Reinhold, Müller-Putz Gernot R, Pfurtscheller Gert
Institute for Knowledge Discovery, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria.
Int Rev Neurobiol. 2009;86:119-31. doi: 10.1016/S0074-7742(09)86009-1.
"Graz brain-computer interface (BCI)" transforms changes in oscillatory electroencephalogram (EEG) activity into control signals for external devices and feedback. Steady-state evoked potentials (SSEPs) and event-related desynchronization (ERD) are employed to encode user messages. User-specific setup and training are important issues for robust and reliable classification. Furthermore, in order to implement small and thus affordable systems, focus is put on the minimization of the number of EEG sensors. The system also supports the self-paced operation mode, that is, users have on-demand access to the system at any time and can autonomously initiate communication. Flexibility, usability, and practicality are essential to increase user acceptance. Here, we illustrate the possibilities offered by now from EEG-based communication. Results of several studies with able-bodied and disabled individuals performed inside the laboratory and in real-world environments are presented; their characteristics are shown and open issues are mentioned. The applications include the control of neuroprostheses and spelling devices, the interaction with Virtual Reality, and the operation of off-the-shelf software such as Google Earth.
“格拉茨脑机接口（BCI）”将振荡脑电图（EEG）活动的变化转化为外部设备的控制信号和反馈。稳态诱发电位（SSEPs）和事件相关去同步化（ERD）被用于编码用户信息。针对健壮且可靠的分类，用户特定的设置和训练是重要问题。此外，为了实现小型且经济实惠的系统，重点在于将EEG传感器的数量减到最少。该系统还支持自定进度的操作模式，即用户可随时按需访问系统并能自主发起通信。灵活性、可用性和实用性对于提高用户接受度至关重要。在此，我们阐述了当前基于EEG通信所提供的可能性。展示了在实验室内部和现实环境中针对健全人和残疾人开展的多项研究结果；呈现了它们的特点并提及了未解决的问题。应用包括神经假体和拼写设备的控制、与虚拟现实的交互以及诸如谷歌地球等现成软件的操作。
