I have to use feedforward neural network for 2-class classification problem. I have EEG data (of 1s, recorded at the sampling frequency of 1200) for two mental states in the following format: Number of EEG channels x Number of samples/points x Number of iterations = 22x1200x200; As the awake and asleep mental state was recorded for 100 times each.
I'm confused that how i should prepare the input signal for feedforward neural network.?
In most of the literature the input layer is equal to the number of channels, but in the examples of NN database they follow the following format:
Input signal= samples x iterations
Targets= number of classes x iterations
Based on what i inferred from the examples i prepared my data in the following format:
Input Signal= 26400x 200
Targets= 2x 200
Although i'm getting acceptable results, but i am not sure if this the right way to it.?