Sensors 2013, 13(11), 14839-14859; doi:10.3390/s131114839
Article

EOG Artifact Correction from EEG Recording Using Stationary Subspace Analysis and Empirical Mode Decomposition

1,* email, 1email, 1email and 2email
Received: 18 September 2013; in revised form: 21 October 2013 / Accepted: 29 October 2013 / Published: 1 November 2013
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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.
Abstract: Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked into the artifactual components, the adaptive signal decomposition technique EMD is applied to denoise the components. Finally, the artifact-only components are projected back to be subtracted from EEG signals to get the clean EEG data. The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Keywords: electroencephalographic (EEG) signals; electro-oculographic (EOG) artifact; stationary subspace analysis (SSA); empirical model decomposition (EMD); signal reconstruction; artifact correction
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MDPI and ACS Style

Zeng, H.; Song, A.; Yan, R.; Qin, H. EOG Artifact Correction from EEG Recording Using Stationary Subspace Analysis and Empirical Mode Decomposition. Sensors 2013, 13, 14839-14859.

AMA Style

Zeng H, Song A, Yan R, Qin H. EOG Artifact Correction from EEG Recording Using Stationary Subspace Analysis and Empirical Mode Decomposition. Sensors. 2013; 13(11):14839-14859.

Chicago/Turabian Style

Zeng, Hong; Song, Aiguo; Yan, Ruqiang; Qin, Hongyun. 2013. "EOG Artifact Correction from EEG Recording Using Stationary Subspace Analysis and Empirical Mode Decomposition." Sensors 13, no. 11: 14839-14859.

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