Peters B O, Pfurtscheller G, Flyvbjerg H
John von Neumann Institute for Computing, Forschungszentrum Jülich, D-52425 Jülich, Germany.
IEEE Trans Biomed Eng. 2001 Jan;48(1):111-6. doi: 10.1109/10.900270.
Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. We present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.
在三名受试者的脑电图（EEG）中识别出了左或右手食指或右脚的运动意图。我们提出了一种多通道分类方法，该方法使用人工神经网络“委员会”来实现这一目的。该分类方法会自动在颅骨上找到与分类任务相关的空间区域。根据受试者的情况，基于持续一秒钟的单次脑电图，在委员会之前未见过的试验中，有75%-98%的试验能够正确识别预期运动。频率滤波并没有提高识别率。在实际运动期间分类效果最佳，但在所有受试者中，当他们被提示稍后要执行哪种运动时，就已经观察到分类成功率出现了第一个峰值。
