Localizing functional activity in the brain through time-frequency analysis and synthesis of the EEG

1996 ◽  
Vol 84 (9) ◽  
pp. 1302-1311 ◽  
Author(s):  
Mingui Sun ◽  
Shie Qian ◽  
Xiaopu Yan ◽  
S.B. Baumann ◽  
Xiang-Gen Xia ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Functional Activity ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis And Synthesis ◽  
The Brain

scholarly journals A better way to define and describe Morlet wavelets for time-frequency analysis

2018 ◽  
Author(s):  
Michael X Cohen
Keyword(s):  
Frequency Analysis ◽  
Time Series Data ◽  
Sine Wave ◽  
Stationary Time Series ◽  
Series Data ◽  
Time Frequency Analysis ◽  
Temporal Precision ◽  
Time Frequency ◽  
Number Of Cycles ◽  
The Brain

AbstractMorlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and frequency precision. It is typically defined as the “number of cycles,” but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and should facilitate proper analyses, reporting, and interpretation of results. MATLAB code is provided.

Sign in / Sign up

Export Citation Format

Share Document