Classification of EEG signals during acupuncture based on Bayesian Linear Discriminant Analysis
In this paper, a standard experiment has been designed in order to research the EEG signals during acupuncture. In the experiment an experienced acupuncturist inserted a needle into the Tsu-San-Li of the volunteers right legs. Five different frequencies of acupuncture manipulation are used during one experiment. The EEG was recorded at 256 Hz sampling rate from 22 electrodes placed at the standard positions of the 10-20 international system. Bayesian Linear Discriminant Analysis is used as a classification of the acupuncture EEG signals. The classifier directly classed the EEG instead of the feature values of the EEG. For almost all the subjects the classification accuracies of 100％ are obtained. The simulation results show that Bayesian Linear Discriminant Analysis is an effective classifier to the acupuncture EEG signals. The results also show that the EEG signals have remarkable changes when acupuncture. The EEG identification algorithm can be applied -in intelligence acupunctures system.
EEG Acupuncture Tsu-San-Li Bayesian Linear Discriminant Analysis(BLDA)
Hongli Li Jiang Wang Haiyang Wang Yingyuan Chen Xile Wei Hongli Li
School of Electrical Engineering and Automation Tianjin University Tianjin, china School of Electrical Engineering and Automation Tianjin Polytechic University Tianjin China
国际会议
太原
英文
113-117
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）