First of all here is an example of a params file with multiple inputs:
Each of the lines starting with u' is another field, so just add another section like that for each input field you want to include (starting with the clipInput = True down until w = 21).
Here is the run.py file I use to run it:
From the raw EEG data you showed that looks like a single metric. It's always ideal to keep it to a single metric if possible, and at least worth trying to begin with. From the look of that data, if you feed that metric right into NuPIC it would find that area you boxed in red as anomalous.
If you have enough data from the subject you're trying to detect seizures in I don't think you'd have to turn off learning. The algorithm learns continuously, so when the EEG values are near 0 as in the beginning it'll learn that, then when it spikes up it will adjust to that and stop finding it as anomalous, and then when it returns to calm near 0 after it will adjust and get used to that again.
I would guess that the majority of the time the data will look calm like it does in the beginning, since people aren't having seizures most of the time. If that's the case NuPIC will be used to calmness and surprised by the seizure spikes. I'm just saying that you may have success with a simpler approach of one metric and learning continuously (without turning off training). If you don't have enough data of the calmer non-seizure times that's when I'd recommend turning off training after the normal times, to ensure it'll be surprised by the seizure spikey times.
These are just my basic intuitions and could not be correct, hope it helps.