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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: Blink event in EEG data

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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
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