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The Scientific World Journal
Volume 2014, Article ID 259121, 9 pages
http://dx.doi.org/10.1155/2014/259121
Research Article

Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Received 16 October 2013; Accepted 18 December 2013; Published 12 January 2014

Academic Editors: P. Bifulco and R. J. Ferrari

Copyright © 2014 Hong Zeng and Aiguo Song. 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.

Citations to this Article [7 citations]

The following is the list of published articles that have cited the current article.

  • Mansura Afifa Khan, Md. Rabiul Islam, and Md. Khademul Islam Molla, “Artifact suppression from electroencephalography signals using stationary subspace analysis,” 2016 19th International Conference on Computer and Information Technology (ICCIT), pp. 252–256, . View at Publisher · View at Google Scholar
  • Asrul Adam, Mohd Ibrahim Shapiai, Mohd Zaidi Mohd Tumari, Mohd Saberi Mohamad, and Marizan Mubin, “Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization,” The Scientific World Journal, vol. 2014, pp. 1–13, 2014. View at Publisher · View at Google Scholar
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  • Mario Guarascio, and Sadasivan Puthusserypady, “Automatic minimization of ocular artifacts from electroencephalogram: A novel approach by combining Complete EEMD with Adaptive Noise and Renyi's Entropy,” Biomedical Signal Processing and Control, vol. 36, pp. 63–75, 2017. View at Publisher · View at Google Scholar
  • Noor Al-Qazzaz, Sawal Hamid Bin Mohd Ali, Siti Ahmad, Mohd Islam, and Javier Escudero, “Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA?WT during Working Memory Tasks,” Sensors, vol. 17, no. 6, pp. 1326, 2017. View at Publisher · View at Google Scholar
  • Hong Ge Li, Rui Qi Song, and Jian Wei Liu, “Low-dimensional feature fusion strategy for overlapping neuron spike sorting,” Neurocomputing, 2017. View at Publisher · View at Google Scholar