I am a computer science student and I'm doing something for a psychology professor.
We have EEG data from an experiment where a person was shown 140 images for 2 seconds each. We placed 64 electrodes on the scalp so we have 64 channels of continuous data.
We want to correlate each node with every other node so that we can graph it using a chord diagram.
Since my professor is abroad, I am having trouble with the directions he gave me to manipulate the data to get the correlations.
"Once you are able to read the matrix of channels I suggest subtracting the mean signal from each, filtering to remove noise above 30 Hz."
My question is how to remove the noise above 30 Hz? For example, data for 1 electrode for 10 milliseconds looks like this (measured in uV):
[ 31172.50, 31173.53, 31174.80, 31177.34, 31173.73, 31172.85, 31172.75, 31172.70, 31174.95, 31178.95]
The python script I am using also gives this data:
sampling rate: 1000.0 Hz time: 0.0 s to 1883.15 s
Can anyone point me in the right direction what steps to take to remove noise above 30 Hz? And also, is that a good way to compute the correlations between the electrodes?