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

Sensors

Hong Zeng¹  Aiguo Song¹  Ruqiang Yan¹ 

[1] School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; E-Mails:

关键词: electroencephalographic (EEG) signals;    electro-oculographic (EOG) artifact;    stationary subspace analysis (SSA);    empirical model decomposition (EMD);    signal reconstruction;    artifact correction;   

DOI  :  10.3390/s131114839

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.