I have some 64 channel EEG data sampled at 256Hz and I'm trying to conduct a time frequency analysis for each channel and plot a spectrogram. The data is stored in a NumPy 3d array, where one of the dimensions has length 256, each element containing a microvolt reading over all sampled time points (total length is 1 second for each channel of data). To be clear: my 3D array is 64256913 (electrode * voltages * trial). The trial is just a single trial of an experiment. So what I want to do is take a single electrode, from a single trial, and the entire 1D voltage vector and create a time-frequency spectrogram. So I want to create a spectrogram plot from data[0,:,0] for example. For each electrode, I want a plot where the y axis is frequency, x-axis is time, and colour/intensity is power I have tried using this in python: from matplotlib.pyplot import specgram #data = np.random.rand(256) specgram(data, NFFT=256, Fs=256) This gives me something that looks like this: Right off the bat, this looks incorrect to me because the axis ranges are incorrect Furthermore, when I run the same code for all EEG channels, over all of my data, I end up with the exact same plot (even though I have verified that the data is different for each) I'm pretty new to signal processing, is there somewhere that I went wrong in either how my data is laid out or how I used my function? Continue reading...
