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Sensors 2016, 16(2), 241; doi:10.3390/s16020241

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil Geumjeong-gu, Busan 609-735, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Patricia A. Broderick
Received: 28 November 2015 / Revised: 5 February 2016 / Accepted: 14 February 2016 / Published: 19 February 2016
(This article belongs to the Section Physical Sensors)
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Abstract

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data. View Full-Text
Keywords: electroencephalogram; eye tracker; ocular artifacts; independent component analysis; auto-regressive exogenous model; affine projection algorithm; composite multi-scale entropy; median absolute deviation electroencephalogram; eye tracker; ocular artifacts; independent component analysis; auto-regressive exogenous model; affine projection algorithm; composite multi-scale entropy; median absolute deviation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mannan, M.M.N.; Kim, S.; Jeong, M.Y.; Kamran, M.A. Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal. Sensors 2016, 16, 241.

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