at the first I'm going to develop a simple code that using GLM method(by matlab lassoglm function) to predict an upcoming seizure. i think it's the most simple kind of machine learning that I'll use following feature that exported from EEG data on it:

1. Spectrum and Shannon's entropy at six frequency bands: delta

(0.1-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (12-30Hz), low-gamma

(30-70Hz) and high gamma (70-180Hz).

2. Spectral edge power of 50% power up to 40Hz.

3. Shannon's entropy at dyadic frequency bands.

4. Spectrum correlation across channels at dyadic frequency bands.

5. Time-series correlation matrix and its eigenvalues.

6. Fractal dimensions.

7. Hjorth parameters: activity, mobility and complexity.

8. Statistical moments: skewness and kurtosis.

I'll use 1 minute window length without overlapping area, but it may be changed by more studies.

this features are used by winner team in kaggle challenge and I'll use them too, because at least we know there is a success experiment of them.

i will use either forest tree and... after completing this scope :)