Training on variable length data - EEG data classification
I'm a student working on a project involving using EEG data to perform lie detection. I will be working with raw EEG data from 2 channels and will record the EEG data during the duration that the subject is replying to the question. Thus, the data will be a 2-by-variable length array stored in a csv file, which holds the sensor readings from each of the two sensors. For example, it would look something like this:

Time (ms) | Sensor 1 | Sensor 2|
--------------------------------
10        | 100.2    | -324.5  |
20        | 123.5    | -125.8  |
30        | 265.6    | -274.9  |
40        | 121.6    | -234.3  |
             ....
2750      | 100.2    | -746.2  |


I want to predict, based on this data, whether the subject is lying or telling the truth (thus, binary classification.) I was planning on simply treating this as structured data and training based on that. However, on second thought, that wouldn't work at all because of a few reasons:


The order in which the data is organized matters as it is continuous time data.
The length of the data is variable as, again, it's time data and the time it takes for the subject to lie/tell the truth is inconsistent.
I don't know how to deal with there being multiple channels of data.


How would I go about setting up a training model for this type of data? I think this is a "time series classification" problem, but I'm not sure. Any kind of help would be greatly appreciated. Thank you in advance!

After doing some more research, I decided to use an LSTM network with the Keras framework running on top of TensorFlow. LSTMs deal with time series data and the Keras layer allows for multiple feature time series data to be fed into the network, so if anyone is having a similar problem as mine, then LSTMs or RNNs are the way to go.
