ON THE EFFECTIVENESS OF ICA BASED EYE ARTIFACT REMOVAL FROM EEG WINDOWS OF DIFFERENT LENGTHS

Foad Ghaderi, Elsa Andrea Kirchner

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References

  1. [1] S. Sanei, Adaptive Processing of Brain Signals, Wiley, 2013.
  2. [2] F. Ghaderi, K. Nazarpour, J.G. McWhirter, and S. Sanei, “Removal of ballistocardiogram artifacts using the cyclostationary source extraction method,” IEEE Trans Biomed Eng, vol. 57, no. 11, pp. 2667–2676, 2010.
  3. [3] F. Ghaderi, S. K. Kim, and E. A. Kirchner, “Effects of eye artifact removal methods on single trial P300 detection, a comparative study,” J NEUROSCI METH, vol. 221, no. 0, pp. 41–47, 2014.
  4. [4] H. Hyv¨arinen, J. Karhunen, and E. Oja, Independent Component Analysis, Wiley-Interscience, 2001.
  5. [5] A. Mognon, J Jovicich, L Bruzzone, and M Buiatti, “ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features,” Psychophysiology, vol. 48, no. 2, pp. 229–240, 2011.
  6. [6] S. Halder, M. Bensch, J. Mellinger, M. Bogdan, A. Kübler, N. Birbaumer, and W. Rosenstiel, “Online artifact removal for brain-computer interfaces using support vector machines and blind source separation,” Intell. Neuroscience, vol. 2007, pp. 8:1–8:9, Apr. 2007.
  7. [7] J. Gao, C. Zheng, and P. Wang, “Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis,” Clin EEG Neurosci, vol. 41, no. 1, pp. 53–59, 2010.
  8. [8] F. Matsusaki, T. Ikuno, Y. Katayama, and K. Iramina, “Online artifact removal in EEG signals,” in World Congress on Medical Physics and Biomedical Engineering May 2631, 2012, Beijing, China, Mian Long, Ed., vol. 39 of IFMBE Proceedings, pp. 352–355. Springer Berlin Heidelberg, 2013.
  9. [9] J. S¨arel¨a and R. Vigário, “Overlearning in marginal distribution-based ICA: Analysis and solutions,” J. Mach. Learn. Res., vol. 4, no. 7-8, pp. 1447–1469, Oct. 2004.
  10. [10] A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004.
  11. [11] J. Onton and S. Makeig, “Information-based modeling of event-related brain dynamics,” in Event-Related Dynamics of Brain Oscillations, Christa Neuper and Wolfgang Klimesch, Eds., vol. 159 of Progress in Brain Research, pp. 99 – 120. Elsevier, 2006.
  12. [12] A. Hyv¨arinen, J. S¨arel¨a, and R. Vigário, “Bumps and Spikes: Artefacts Generated by Independent Component Analysis with Insufficient Sample Size,” in Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA’99), 1999, pp. 425–429.
  13. [13] G. Korats, S. Cam, R. Ranta, and M. Hamid, “Applying ICA in EEG: Choice of the window length and of the decorrelation method,” in Biomedical Engineering Systems and Technologies, vol. 357 of Communications in Computer and Information Science, pp. 269–286. Springer Berlin Heidelberg, 2013.
  14. [14] A. J. Bell and T. J. Sejnowski, “An informationmaximization approach to blind separation and blind deconvolution,” Neural Computation, vol. 7, no. 6, pp. 1129– 1159, 1995.
  15. [15] C. Chih-Chung and L. Chih-Jen, “LIBSVM: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 27:1–27:27, 2011.
  16. [16] M. M. Krell, S. Straube, A. Seeland, H. Wöhrle, J. Teiwes, J. H. Metzen, E. A. Kirchner, and F. Kirchner, “pySPACE a signal processing and classification environment in Python,” Frontiers in Neuroinformatics, vol. 7, no. 40, Dec 2013.
  17. [17] S. Straube and M. M. Krell, “How to evaluate an agent’s behaviour to infrequent events? – reliable performance estimation insensitive to class distribution,” Frontiers in Computational Neuroscience, vol. 8, no. 43, 2014.

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