An unsupervised eye blink artifact detection method for real-time electroencephalogram processing

Electroencephalogram (EEG) is easily contaminated by unwanted physiological artifacts, among which electrooculogram (EOG) artifacts due to eye blinking are known to be most dominant. The eye blink artifacts are reported to affect theta and alpha rhythms of frontal EEG signals, and hard to be accurately detected in an unsupervised way due to large individual variability. In this study, we propose a new method for detecting eye blink artifacts automatically in real time without using any labeled training data. The proposed method combined our previous method for detecting eye blink artifacts based on digital filters with an automatic thresholding algorithm. The proposed method was evaluated using EEG data acquired from 24 participants. Two conventional algorithms were implemented and their performances were compared with that of the proposed method. The main contributions of this study are (1) confirming that individual thresholding is necessary for artifact detection, (2) proposing a novel algorithm structure to detect blink artifacts in a real-time environment without any a priori knowledge, and (3) demonstrating that the length of training data can be minimized through the use of a real-time adaption procedure.