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I am new to signal processing and despite googling about my question, I still couldn't find the solution.

Here is what I am struggling with:

I have an Electrocorticography (ECoG) dataset. This consists of recording brain activity from many channels. The data is stored in a matrix. Data in each row relates to the brain activity recorded from each channel. Brain activity data was recorded with 10kHz frequency (Therefore, the continues 10,000 elements of each row is the brain activity data of 1s)

Similar to many analysis such as, I need to extract powers of different frequency bands (1-8 hz, etc) from this data and run some regression anlaysis.

For example, as a sentence from a previous study: "A fast Fourier transforma-tion (FFT; EEGLAB v5.03) was performed for each 1-secondsignal to obtain the power of each of the 3 frequency bands(2–8, 8–25, and 80–150Hz) for each electrode"

However, I am still confused how should I apply the fft function of matlab to correctly obtain power of different frequency bands.

Considering I only have this matrix of dataset (each row: brain activity recorded from each channel) and also the fact that I am not very familiar with methods in signal processing, I appreciate if someone can kindly help me how should I do such an analysis.

I greatly appreciate your help.

Kadaj13
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    You might want to have a look at MATLAB's [`spectrogram`](https://www.mathworks.com/help/signal/ref/spectrogram.html) function, especially have a look at [this example](https://www.mathworks.com/help/signal/ref/spectrogram.html#bultpz_-2). And, **please**, get some introduction and help on EEG/ECoG in person from your supervisor or whatever before working on that kind of data! – HansHirse Feb 05 '20 at 07:27
  • Is the data segmented to trials (~1sec long) or is it continuous? You may want to do it as reviewer II suggested, import it to EEGLab. See here for example https://sccn.ucsd.edu/wiki/A01:_Importing_Continuous_and_Epoched_Data#Importing_sets_of_data_averages_into_EEGLAB – Yuval Harpaz Feb 05 '20 at 07:49

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