Hoffmann Ulrich, Vesin Jean-Marc, Ebrahimi Touradj, Diserens Karin
Ecole Polytechnique Fédérale de Lausanne, Signal Processing Institute, CH-1015 Lausanne, Switzerland.
J Neurosci Methods. 2008 Jan 15;167(1):115-25. doi: 10.1016/j.jneumeth.2007.03.005. Epub 2007 Mar 13.
A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.
脑机接口（BCI）是一种通信系统，它将大脑活动转化为计算机或其他设备的指令。换句话说，BCI允许用户仅通过大脑活动对周围环境施加影响，而无需使用外周神经和肌肉。在本文中，我们展示了一种脑机接口，它对残疾人和健全人都能实现高分类准确率和高比特率。该系统基于P300诱发电位，并在五名严重残疾人和四名健全人身上进行了测试。对于四名残疾受试者，分类准确率达到了100%。残疾受试者获得的比特率在10至25比特/分钟之间。测试了不同电极配置和机器学习算法对分类准确率的影响。还讨论了在基于P300的残疾受试者脑机接口系统中，对于获得良好分类准确率可能重要的其他因素。
