脑电信号的增量模式识别

Kam Swee Ng, Hyung-Jeong Yang, Sun-Hee Kim, Jong-Mun Jeong
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引用次数: 0

摘要

基于脑电图的脑机接口为人脑与计算机之间的通信提供了一条新的途径。它可用于残障或残疾用户通过计算机界面与人进行交互。它还可以用于控制人体的肌肉运动。在本文中,我们证明了通过增量方法可以从脑电信号中提取有意义的信息。我们逐步应用主成分分析来识别由实际和想象肢体运动组成的一系列EEG数据中的模式。我们的实验已经证明,这种方法很有前途,特别是在时间序列数据中,因为它是渐进的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incremental Pattern Recognition on EEG Signal
EEG based brain computer interface has provided a new communication pathway between the human brain and the computer. It can be used for handicap or disabled users to interact with human using the computer interface. It can also be used in controlling human's muscles movement. In this paper, we show that meaningful information can be extracted from EEG signal through incremental approach. We applied principal component analysis incrementally which recognizes patterns in the series of EEG data that consists of actual and imaginary limb movements. Our experiments have proven that the approach is promising especially in time series data because it works incrementally.
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