脑电图纹状体搏动频率的单周期单频可视化

J. LaRue
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引用次数: 1

摘要

基于纹状体跳动频率的神经科学概念,提出了一种新的方法,利用脑电波活动的脑电图,利用由单个单周期单频正弦波组成的滑动窗口来搜索频谱成分。由于高性能计算体系结构的出现,这种新方法现在是实用的。注意,这与传统的时频谱图方法不同,它使用一个恒定大小的滑动窗口来同时计算所有频率分量,它与小波方法不同,因为一组小波是围绕一个基本单位制定的,这个基本单位通常包括一个卷积斜坡上升和斜坡下降结构。所提出的单周期单频搜索方法在识别频率分量的存在方面更具体,因为它使用了一组由单周期正弦波组成的窗口,每个窗口的长度是频率和采样率的函数。这种方法的结果是通过相关系数矩阵的可视化来呈现概念化纹状体拍频成分的开关性质,其中行是单个频率,(在本文中呈现为1000 Hz),其中列表示每个检测到的纹状体拍频窗口的位置。
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Single-Period Single-Frequency (SPSF) Visualization of an EEG’s Striatal Beat Frequency
Motivated by the neuroscience concept of striatal beat frequency a new method is presented that takes an electroencephalogram of brain-wave activity and searches for spectral components using a sliding bank of windows consisting of individual singleperiod single-frequency sinusoids. This new approach is now practical due to the presence of high performance computing architectures. And note, this is a departure from legacy time-frequency spectrogram approaches which use one sliding window of constant size to calculate all frequency components simultaneously and it differs from the wavelet method because a suite of wavelets are formulated around one base unit and that unit usually includes a convolutional ramp up and ramp down structure. The proposed single-period singlefrequency search method is more tangible in identifying the existence, in time, of a frequency component due to its self-imposed constraint of using a bank of windows consisting of single period sinusoids, the length of each being a function of frequency and sampling rate. The result of this approach is a rendering of the on-off nature of the conceptualized striatal beat frequency components through a visualization of a matrix of correlation coefficients, where the rows are individual frequencies, (presented up to 1000 Hz in this paper), and where the columns indicate the position of each detected striatal beat frequency window of time.
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