![]() ![]() The tutorial can be % found here: % % % % The data (provided by Mike) can be found here: % % % % His book, Analzyzing Neural Time Series Data, is an excellent resource for % anyone working on EEG analysis: % % % % Code rewritten by: Justin Brantley % email: justin dot a dot brantley at gmail dot com clc clear close all % Simulated data - three frequencies fs = 100 % sampling rate move_freq = % frequencies Tau = 10 % time constant time_vec = 0 : 1 / fs : Tau % time vector amplitude = % Amplitude of sinewave burst_time = % Generate matrix of Gaussian random noise: swave = 0.01. We will use the % dataset and wavelet method in the original tutorial. The purpose of this tutorial is to highlight the effects % of limits and scaling on visualization of time-freq data. ![]() We will also compute the sum of all of them to create a single time series with multiple frequencies. Let’s start by simulating some cosine waves, each with fixed frequency. The following tutorial is broken down into two main sections: (1) we will look at the time-frequency plots of some simulated data to see the effect of scaling, then (2) we will use some actual EEG data to further examine this in real data. I actually bought the hard copy myself and referenced it quite frequently when doing these types of analysis on EEG data. If you are interested in neural time series analysis, in particular EEG, EMG, or ECoG, I can’t recommend his book Analyzing Neural Time Series Data enough. Just a quick disclaimer before we begin: I am using large chunks of code adapted from a tutorial on time-frequency analysis by Mike X Cohen. However, this post is not really about how to use time-frequency analysis for EEG data as much as its just a great example of how using proper color scaling makes a difference in what we can see in the data. This approach is particularly useful in EEG analysis since we know that changes in certains bands correlate to changes in behavior. Time-frequency analyses are a useful class of methods that help us to resolve changes in time-varying frequency content in our timeseries data. Published: ApPart Three: EEG time-frequeny analysis Making Nice Figures (in MATLAB) - Part 3: EEG time-frequency analysis ![]()
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