基于香农熵和偏度信息测度的脑认知状态判别

J. Davis, Florian Schübeler, Sungchul Ji, R. Kozma
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引用次数: 3

摘要

非侵入性脑成像技术是监测人类参与者认知状态的常用工具。这项工作建立在我们之前使用HydroCel测地线传感器网络,256个电极密集阵列脑电图(EEG)的研究基础上。研究分析了每个EEG电极的时间功率谱密度的主导频率。实验包括三种模式:冥想、数学思维和(c)睁眼状态。本文对Shannon熵指数和Pearson偏度系数进行了分析,以检验它们对不同大脑状态进行分类的适合度。这些结果有助于开发一种全面的方法来理解大脑动力学。
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Discrimination Between Brain Cognitive States Using Shannon Entropy and Skewness Information Measure
Non-invasive brain imaging techniques are popular tools for monitoring the cognitive state of human participants. This work builds on our previous studies using the HydroCel Geodesic Sensor Net, 256 electrodes dense-array electro-encephalography (EEG). The studies analyze dominant frequencies of temporal power spectral densities for each of the EEG electrodes. The experiments involve three modalities: Meditation, Math Mind, and (c) Open Eyes condition. Here we perform an analysis of the Shannon entropy index and Pearson’s skewness coefficient in order to test their fitness to classify different brain states. The results help to develop a comprehensive methodology to understand brain dynamics.
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