The EEG De-noising Research based on Wavelet and Hilbert Transform Method

摘要：

To remove the noises of EEG effectively, this paper makes the EEG De-noising research about Wavelet and Hilbert Transform. In HHT De-noising process, first, according to EEG own frequency characteristics, the EEG signals are made eight scales decomposition by using EMD algorithm, and obtain eight IMF component signals. Second, reconstruct the IMF component signals after filtering. Finally, get the EEG after De-noising. The experimental results show that HHT method can preferably eliminate the noises which mixed in the EEG. The De-noising effects of HHT and Wavelet Transform methods are compared by using the evaluation indexes. It finds that HHT method is superior to the traditional Wavelet Transform in the EEG De-noising, and its efficiency is higher.

关键词： EEG HHT EMD Wavelet transform De-noising

作者: Yuan Fei-long Luo Zhi-zeng

作者单位: Intelligent Control and Robot Research Institute Hangzhou Dianzi University Hangzhou, China

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 361-365

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）