EEG信号β波信息分离的FFT与小波处理效果分析

The Comparison of the Extraction of β Wave from EEG Between FFT and Wavelet Transform

本文通过比较快速傅里叶变换(FFT)与小波算法进行脑电(EEG)信号处理的结果和效率,选择一种快速高效的β波实时提取技术,为实验室3D电视实时健康评估EEG数据处理提供依据。选择5名正常志愿者观看3D电视前后以及观看过程中的EEG信号,分别利用FFT法和小波包变换提取EEG信号β波段的特征波,比较相对能量的变化趋势以及两种方法的计算成本。结果显示:(1)观看3D电视前后FFT和小波包变换提取EEG信号β波段得到的对比结果一致。(2)观看3D电视过程中两种方法得到的EEG信号β波段的变化趋势一致。(3)FFT在计算成本方面的处理速度比小波包变换更快。FFT和小波算法在提取EEG信号特征波方面结果是一致的,为后续处理大批量的实验数据提供了一种快速处理的方法。

In order to choose a fast and efficient real-time method in β wave information extraction, we compared the result and the efficiency of the information separation of both fast Fourier transform （FFT） and wavelet transform of EEG beta band in the present paper. Our work provides the basis for the EEG data come from the real-time health assessment of 3DTV. We took the EEGs of 5 healthy volunteers before, after and during the process of watching 3DTV and meanwhile recorded the results. The trends of the relative energy and the time cost of two methods were compared by using both the FFT and wavelet packet transform （WPT） which was to extract the feature of EEG wave. It demonstrated that （1） Results of the two methods were consistent in the trends of watching 3DTV; （2） Resuits of the differences in two methods were consistent before and after watching 3DTV; （3） FFT took less time than the wavelet transform in the same case. It is concluded that the results of both FFT and Wavelet transform are consistent in feature extraction of EEG, and a fast method to work with the large quantities of EEG data obtained in the experiments can be offered in the future.