Classifying sleep stages from only EEG I'm currently working on a project that requires me to classify sleep stages (Awake W, N1, N2, N3 and REM) based on only an EEG. Various algorithms and classifying standards (such as Rechtschaffen &amp; Kales or AASM) actually require EMG and EOG in order to distinguish from REM sleep and wakefulness, which I don't have. Additionally, most algorithms use techniques such as Wavelet analysis, ICA, Neural Networks and such, but I have only learned up to Fourier Transform. Which means that I have to detect and classify these stages and their components (detect sleep spindles in order to classify N2, for example) based on only frequency analysis.

Power spectra analysis is poor since low frequency components tend to have high powers so the program ends up over-classifying 30s epochs as deep sleep stages. 

I have a .txt file telling me exactly what's going on in every 30s epoch, so I have a priori knowledge regarding when is the subject awake or asleep (and which sleep stages the subject is in). 

I appreciate any insight into this problem.
The standard approach would be to


Choose your feature vectors.
Partition your data set into training and testing.
Calculate the feature vectors for all your data (both training and test).
Use the training data set to train your classification algorithm.
Apply the trained classification algorithm to the test data set and calculate your correct classifications and incorrect classifications to get your precision / recall.


The trick will be step 1: choosing your feature vectors, given that you've only got Fourier coefficients (and values derived from them).