Detection of eye blink artifacts from single prefrontal channel electroencephalogram. Issue 124 (February 2016)

• Record Type: Journal Article
• Authors: Chang, Won-Du; Cha, Ho-Seung; Kim, Kiwoong; Im, Chang-Hwan
• Is Part Of: Computer methods and programs in biomedicine. Issue 124(2016)
• Page Start: 19
• Page End: 30
• Publication Date: 2016-02
• Subjects: Electroencephalogram (EEG) -- Electrooculogram (EOG) -- Artifact detection -- Eye blink; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.10.011
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed. Overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased. An online experiment showed that our method is useful for headband-type wearable EEG applications. A MATLAB package of our library and sample data is open for free download. Abstract: Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain–computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.