Zhang Haihong, Wang Chuanchu, Guan Cuntai
Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5067-70. doi: 10.1109/IEMBS.2007.4353479.
Asynchronous control is a critical issue in developing brain-computer interfaces for real-life applications, where the machine should be able to detect the occurrence of a mental command. In this paper we propose a computational approach for robust asynchronous control using the P300 signal, in a variant of oddball paradigm. First, we use Gaussian models in the support vector margin space to describe various types of EEG signals that are present in an asynchronous P300-based BCI. This allows us to derive a probability measure of control state given EEG observations. Second, we devise a recursive algorithm to detect and locate control states in ongoing EEG. Experimental results indicate that our system allows information transfer at approx. 20bit/min at low false alarm rate (1/min).
异步控制是开发用于实际应用的脑机接口时的一个关键问题，在这种应用中，机器应该能够检测到心理命令的发生。在本文中，我们提出了一种在异常球范式的一个变体中使用P300信号进行鲁棒异步控制的计算方法。首先，我们在支持向量边缘空间中使用高斯模型来描述基于异步P300的脑机接口中存在的各种类型的脑电信号。这使我们能够根据脑电观测得出控制状态的概率度量。其次，我们设计了一种递归算法来检测和定位正在进行的脑电中的控制状态。实验结果表明，我们的系统允许以约20比特/分钟的速率进行信息传输，且误报率较低（1/分钟）。
