FEATURE EXTRACTION FROM ELECTROENCEPHALOGRAM BY ADAPTIVE SEGMENTATION
FEATURE EXTRACTION FROM ELECTROENCEPHALOGRAM BY ADAPTIVE SEGMENTATION
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DOI：
10.1109/proc.1977.10543
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发表时间：
1977-01-01
影响因子：
20.6
通讯作者：
PRAETORIUS, HM
中科院分区：
文献类型：
--
作者：
BODENSTEIN, G;PRAETORIUS, HM
本文描述了一种针对脑电自动分析的模式识别系统的特征提取阶段。通过一种依赖于线性预测滤波的方法，基本模式——脑电图记录——被分割成称为片段和瞬态的“基本模式”。然后提取合适的特征，代表信号的功率谱和时间结构，最后组合成一个代表整个EEG的特征集。这种表示的质量可以通过比较原始信号与存储特征的模拟来评估。
This paper describes the feature extraction stage of a proposed pattern recognition system aimed at automatic EEG analysis. The basic pattern-the EEG record-is split into "elementary patterns" called segments and transients, by means of a method relying on linear predictive filtering. Appropriate features, representing power spectra and the time structure of the signal, are then extracted and finally combined into a feature set representing the EEG as a whole. The quality of this representation may be assessed by comparing the original signal with its simulation from the stored features.