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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 this paper, by comparing the results and efficiency of EEG signal processing with FFT and wavelet algorithm, a fast and efficient β-wave real-time extraction technique is selected to evaluate the real-time health of laboratory 3D television EEG data processing Provide evidence. Five normal volunteers were selected to watch the EEG signals before and after the 3D TV and during the viewing. The EEG signals of the EEG signal were extracted respectively by FFT and wavelet packet transform, and the relative energy trends and the computational costs of the two methods were compared. The results show that: (1) The results of FFT and wavelet packet transform before and after watching 3D TV are consistent with the results obtained by extracting β band of EEG signal. (2) The trend of β-band of EEG signals obtained by the two methods during 3D TV viewing is the same. (3) The processing speed of FFT in the computational cost is faster than the wavelet packet transform. The results of FFT and wavelet algorithm are consistent in extracting EEG signal eigenfrequency and provide a fast processing method for subsequent processing of large quantities of experimental data.