Pfurtscheller G, Kalcher J, Neuper C, Flotzinger D, Pregenzer M
Ludwig Boltzmann-Institute of Medical Informatics and Neuroinformatics, University of Technology, Graz, Austria.
Electroencephalogr Clin Neurophysiol. 1996 Nov;99(5):416-25. doi: 10.1016/s0013-4694(96)95689-8.
EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: 'Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?' For this purpose the EEG during performance of hand movement was analyzed and classified on-line. A neural network in form of a learning vector quantizertion (LVQ) with an input dimension of 16 was trained to classify EEG patterns from two electrodes and two time windows. After two training sessions on 2 different days, 4 subjects showed a classification accuracy of 89-100%. For two subjects classification was not possible. These results show that in general movement specific EEG-patterns can be found, classified in real time and used to move a cursor on a monitor to the left or right. On-line EEG classification is necessary when the EEG is used as input signal to a brain computer interface (BCI). Such a BCI can be a help for handicapped people.
在6名正常受试者进行周期性左手或右手运动序列时记录了脑电图。通过视觉提示指示左手或右手。提出的问题是：“是否可以使用脑电图信号来控制光标，将显示器上的光标向左或向右移动？” 为此，对手部运动执行期间的脑电图进行了在线分析和分类。训练了一个输入维度为16的学习向量量化（LVQ）形式的神经网络，以对来自两个电极和两个时间窗口的脑电图模式进行分类。在不同的两天进行两次训练后，4名受试者的分类准确率达到89%-100%。对于两名受试者来说，无法进行分类。这些结果表明，一般来说，可以找到特定于运动的脑电图模式，实时进行分类，并用于将显示器上的光标向左或向右移动。当脑电图用作脑机接口（BCI）的输入信号时，在线脑电图分类是必要的。这样的脑机接口可以帮助残疾人。
