Foad Ghaderi, Elsa Kirchner


  1. [1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland,G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,”Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002.
  2. [2] J. Polich, “Updating P300: an integrative theory ofP3a and P3b,” Clinical Neurophysiology, vol. 118,no. 10, pp. 2128–48, Oct 2007.
  3. [3] E. A. Kirchner, J. H. Metzen, T. Duchrow, S. K.Kim, and F. Kirchner, “Assisting telemanipulationoperators via real-time brain reading,” in Proc.Mach. Learning in Real-time Applicat. Workshop2009, V Lohweg and O Niggemann, Eds., Paderborn,Germany, 2009.
  4. [4] A. Bashashati, M. Fatourechi, R. K. Ward, and G. E.Birch, “A survey of signal processing algorithms inbrain-computer interfaces based on electrical brainsignals,” Journal of Neural Engineering, vol. 4, no.2, pp. R32–R57, 2007.54
  5. [5] M. Arvaneh, Cuntai Guan, Kai Keng Ang, and ChaiQuek, “Optimizing the channel selection and classi-fication accuracy in eeg-based bci,” Biomedical En-gineering, IEEE Transactions on, vol. 58, no. 6, pp.1865–1873, june 2011.
  6. [6] H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller,“Optimal spatial filtering of single trial EEG duringimagined hand movement,” Rehabilitation Engineer-ing, IEEE Transactions on, vol. 8, no. 4, pp. 441–446,dec 2000.
  7. [7] U. Hoffmann, J.-M. Vesin, and T. Ebrahimi, “Spa-tial filters for the classification of event-related po-tentials,” in Proc the 14th European Symposium onArticial Neural Networks (ESANN), April 2006, pp.26–28.
  8. [8] B. Rivet, A. Souloumiac, V. Attina, and G. Gibert,“xDAWN algorithm to enhance evoked potentials:Application to brain-computer interface,” Biomedi-cal Engineering, IEEE Transactions on, vol. 56, no.8, pp. 2035–2043, aug. 2009.
  9. [9] A. Onishi, A. H. Phan, K. Matsuoka, and A. Cichocki,“Tensor classification for P300-based brain computerinterface,” in Proc. IEEE Int. Conf. Acoustics, Speech,and Signal Processing (ICASSSP), 2012, pp. 581–584.
  10. [10] A. Ivannikov, I. Kalyakin, J. H¨am¨al¨ainen, P. H.T.Lepp¨anen, T. Ristaniemi, H.Lyytinen, andT. K¨arkk¨ainen, “ERP denoising in multichannelEEG data using contrasts between signal and noisesubspaces,” Journal of Neuroscience Methods, vol.180, no. 2, pp. 340–351, 2009.
  11. [11] Saeid Sanei and J. A. Chambers, EEG Signal Pro-cessing, Wiley-Interscience, Sept. 2007.
  12. [12] L. K. Saul and J. B. Allen, “Periodic component anal-ysis: An eigenvalue method for representing periodicstructure in speech.,” in NIPS’00, 2000, pp. 807–813.
  13. [13] J. H. Metzen, S. K. Kim, and E. A. Kirchner, “Min-imizing calibration time for brain reading,” inProceedings of the 33rd international conferenceon Pattern recognition, Berlin, Heidelberg, 2011,DAGM’11, pp. 366–375, Springer-Verlag.
  14. [14] Robert West, “The temporal dynamics of prospectivememory: a review of the ERP and prospective mem-ory literature,” Neuropsychologia, vol. 49, no. 8, pp.2233–2245, Jul 2011.
  15. [15] D. R. Velez, B. C. White, A. A. Motsinger, W. S.Bush, M. D. Ritchie, S. M. Williams, and J. H. Moore,“A balanced accuracy function for epistasis modelingin imbalanced datasets using multifactor dimension-ality reduction,” Genetic Epidemiology, vol. 31, no.4, pp. 306–315, 2007.

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