Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition
MetadataShow full item record
© 2017, Springer Science+Business Media New York. Electrooculogram (EOG) is one of the major artifacts in the design of electroencephalogram (EEG)-based brain computer interfaces (BCIs). That removing EOG artifacts automatically while retaining more neural data will benefit for further feature extraction and classification. In order to remove EOG artifacts automatically as well as reserve more useful information from raw EEG, this paper proposes a novel blind source separation method called CCA-EEMD (canonical correlation analysis, ensemble empirical mode decomposition). Technically, the major steps of CCA-EEMD are as follows: Firstly, the multiple-channel original EEG signals are separated into several uncorrelated components using CCA. Then, the EOG component can be identified automatically by its kurtosis value. Next, the identified EOG component is decomposed into several intrinsic mode functions (IMFs) by EEMD. The IMFs uncorrelated to the EOG component are recognized and retained, and a new component will be constructed by the retained IMFs. Finally, the clean EEG signals are reconstructed. Keep in mind that the novelty of this paper is that the identified EOG component is not removed directly but used to extract neural EEG data, which would keep more effective information. Our tests with the data of seven subjects demonstrate that the proposed method has distinct advantages over other two commonly used methods in terms of average root mean square error [37.71 ± 0.14 (CCA-EEMD), 44.72 ± 0.13 (CCA), 49.59 ± 0.16 (ICA)], signal-to-noise ratio [3.59 ± 0.24 (CCA-EEMD), -6.53 ± 0.18(CCA), -8.43 ± 0.26 (ICA)] , and classification accuracy [0.88 ± 0.002 (CCA-EEMD), 0.79 ± 0.001 (CCA), 0.73 ± 0.002 (ICA)]. The proposed method can not only remove EOG artifacts automatically but also keep the integrity of EEG data to the maximum extent.
Showing items related by title, author, creator and subject.
Li, X.; Guan, Cuntai; Zhang, H.; Ang, K. (2017)© 2016 IEEE. Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain-computer interface (BCI) research. It is especially challenging when ...
Tan, Y.; Song, Y.; Liu, Xin; Wang, X.; Cheng, J. (2016)© 2017 Elsevier B.V.Offshore oil and gas platforms (OOGPs) usually have a lifetime of 30-40. years. An increasing number of OOGPs across the world will be retired and decommissioned in the coming decade. Therefore, a safe ...
Using the EZ-diffusion model to score a single-category implicit association test of physical activityRebar, Amanda; Ram, N.; Conroy, D. (2015)© 2014 Elsevier Ltd. Objective: The Single-Category Implicit Association Test (SC-IAT) has been used as a method for assessing automatic evaluations of physical activity, but measurement artifact or consciously-held ...