Features for blink detection in real-time single channel EEG
The EEG signature of eye blinks is typically visible for about 200ms of data. When you want to move a window of 50 to 200 samples, I assume that your sampling frequency is 1000Hz (you should specify this in your next question).

A classical feature for eye blink detection is the peak-to-peak amplitude, which is the absolute difference of the maximum amplitude and the minimum amplitude of the EEG within your window. Another feature could be the variance within your EEG window.

Pseudocode:

peak_to_peak_amp = abs(window.max() - window.min())
variance = var(window)


Note that both of these features are rather unspecific to eye blinks and will have a hard time to distinguish blinks from other peaks in the data. For this reason, the methods are often combined with filtering of the data and converting the amplitudes in microvolts to z-scores.

For reading I suggest chapter 6 in the canonical book by Steven Luck: An Introduction to the Event-Related Potential Technique