A computationally efficient order statistics based outlier detection technique for EEG signals

Detecting artifacts in EEG data produced by muscle activity, eye blinks and electrical noise is a common and important problem in EEG applications. We present a novel outlier detection method based on order statistics. We propose a 2 step procedure comprising of detecting noisy EEG channels followed by detection of noisy epochs in the outlier channels. The performance of our method is tested systematically using simulated and real EEG data. Our technique produces significant improvement in detecting EEG artifacts over state-of-the-art outlier detection technique used in EEG applications. The proposed method can serve as a general outlier detection tool for different types of noisy signals.