I’m working on a project applying HTM to EEG data for seizure prediction, which is actually detection of the stage just before the seizure (i.e. pre-seizure or not), so not next step prediction, but binary classification.
The EEG dataset consists of 16 electrode channel readings in 10 minute recordings.
My questions are:
Can I split a 10 minute recording into epochs (say 30 second segments), extract features on these segments, keeping them sequential and use those features as input to HTM instead of the raw data? I was thinking this would involve using ‘reset’ at the beginning of a new 10 minute recording.
If this is a valid approach, I’m assuming a rolling window mechanism on the epochs would confuse the TM learning?
What is the effect of having more input fields? If I were to extract cross-channel features, making the number of input fields much larger, would that be a problem?