Features for blink detection in real-time single channel EEG I am looking to detect blink events in real-time single channel EEG. Classification of a moving window of samples to determine whether a blink artifact exists requires feature extraction (except when using deep learning, I am not experienced enough for this). What features would be useful to extract from a window of approx. 50-200 samples of time series data for detecting a blink event. The blink event can be easily seen in the below picture:
I would try a combination of the following:


A rolling-window variance to detect unusually large deflections
Bollinger bands to detect the spikes
Autocorrelation to detect the 'up down up' pattern