Müller Klaus-Robert, Tangermann Michael, Dornhege Guido, Krauledat Matthias, Curio Gabriel, Blankertz Benjamin
Technical University Berlin, Str. des 17. Juni 135, 10623 Berlin, Germany.
J Neurosci Methods. 2008 Jan 15;167(1):82-90. doi: 10.1016/j.jneumeth.2007.09.022. Epub 2007 Sep 29.
Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain-computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.
在实时分析单试次脑电图（EEG）数据时，机器学习方法是补偿EEG高度变异性的绝佳选择。本文简要回顾了用于基于EEG的高效脑机接口（BCI）和心理状态监测应用的预处理和分类技术。更具体地说，本文概述了柏林脑机接口（BBCI），该接口只需对受试者进行最少的训练即可操作。此外，还讨论了基于新型BBCI的Hex-o-Spell文本输入系统的拼写，该系统的通信速度可达每分钟6 - 8个字母。最后展示了实时唤醒监测实验的结果。
