【摘要】 目的 :评价滑行窗口技术分析脑电诱发电位的能力。方法 :将具有一定宽度的时间窗口延时间轴滑行 ,计算该窗口内的脑电电位平均值 ,再与对照窗口进行统计比较 ,以检验诱发电位是否具有统计显著性。利用该方法分析随机产生的模拟数据 ,计算在指定单次检验阈值下 ,多次统计比较导致显著性差异点连续出现的几率 ,以确定可使整体α值小于 0 .0 5的cluster大小。为检验该方法的有效性 ,在 14名健康右利手志愿者右手中指给予痛或非痛电刺激 ,记录EEG信号并采用上述技术加以分析。结果 :在整体α值确定的前提下 ,作为显著性判据的cluster大小随单次检验阈值与窗宽的增加而增大。依据上述方法分析真实EEG数据 ,确定了体感与痛觉诱发电位波形中具有统计学意义的成分 ,以及两种波形之间的显著性差异。结论 :滑行窗口技术可有效地用于分析脑电诱发电位。更多还原

【Abstract】 Objective: To evaluate the efficiency of sliding window technique in extracting and analyzing somatosensory evoked potentials (SEP) from multichannel electroencephalogram (EEG) data. Methods: A time window of certain window size was moved along the time dimension of data sets. Values within the window were averaged for each trial, and then compared with a preset control window. The probability of randomly appeared significance resulting from repeated statistical comparison was calculated utilizing simulated EEG data sets. Cluster size (number of successive significant data points with given individual significance threshold) was determined to keep the general alpha value under 0.05. To test this procedure, multichannel EEG signals were recorded and analyzed from fourteen healthy right handed volunteers, with painful and non painful electrical stimuli delivered to the right middle fingers. Results: Cluster size increased in parallel with window size and individual statistical threshold. The major SEP components of real EEG data, as well as the difference between pain and non pain SEPs, were demonstrated to be significant with the sliding window method. Conclusion: Sliding window method is an effective tool for the analysis of SEP data.更多还原