A nonparametric method for the segmentation of the EEG
A nonparametric method for the segmentation of the EEG
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DOI：
10.1016/s0169-2607(98)00079-0
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发表时间：
1999-09-01
影响因子：
6.1
通讯作者：
Shishkin, SL
中科院分区：
文献类型：
--
作者：
Brodsky, BE;Darkhovsky, BS;Shishkin, SL
提出了一种基于非参数统计分析的脑电信号分割方法。选择非参数方法是因为它可以最大限度地减少对信号先验信息的需求。对于给定的虚警概率，该方法提供了几乎任何EEG特征的变化点（准平稳段的边界）检测。将该方法应用于12名受试者在闭眼和睁眼条件下的g通道自发脑电图记录，检测α节奏功率的快速波动。对虚警概率值进行初步调整后，在相同参数的无监督状态下对所有记录进行分析。每分钟可发现15 ~ 19个变化点和脑电通道。自动检测到的变化点与对α活动变化瞬间的视觉估计非常吻合。1999爱思唯尔科学爱尔兰有限公司版权所有。
A new method for segmentation of the EEG, based on a nonparametric statistical analysis, is proposed. A nonparametric approach was chosen because it minimises the need for a priori information about a signal. The method provides detection of change-points (quasi-stationary segments' boundaries) in almost any EEG characteristic for a given level of false alarm probability. The method was applied to g-channels spontaneous EEG recordings obtained from 12 subjects in eyes closed and eyes open conditions to detect rapid fluctuations of the alpha rhythm power. After preliminary adjustment of false alarm probability values al the recordings were analysed in unsupervised regime with the same parameters. From 15 to 1 19 change-points were found per minute and EEG channel. Automatically detected change-points were in good correspondence with visual estimation of the instants of change in alpha activity. (C) 1999 Elsevier Science ireland Ltd. All rights reserved.