Machine learning for real-time single-trial EEG-analysis:: From brain-computer interfacing to mental state monitoring
Machine learning for real-time single-trial EEG-analysis:: From brain-computer interfacing to mental state monitoring
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DOI：
10.1016/j.jneumeth.2007.09.022
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发表时间：
2008-01-15
影响因子：
3
通讯作者：
Blankertz, Benjamin
中科院分区：
文献类型：
--
作者：
Mueller, Klaus-Robert;Tangermann, Michael;Blankertz, Benjamin
在实时分析单次试验数据时，机器学习方法是补偿EEG高变异性的绝佳选择。本文简要回顾了有效的基于脑电的脑机接口（BCI）和精神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），只需最少的学科培训即可操作。此外，拼写与新的BBCI为基础的十六进制拼写文本输入系统，获得每分钟6-8个字母的通信速度，进行了讨论。最后给出了一个实时唤醒监测实验的结果。(C)2007 Elsevier B. V.保留所有权利。
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. (C) 2007 Elsevier B.V. All rights reserved.