Open Access
MATEC Web Conf.
Volume 140, 2017
2017 International Conference on Emerging Electronic Solutions for IoT (ICEESI 2017)
Article Number 01028
Number of page(s) 7
Published online 11 December 2017
  1. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain–computer interfaces for communication and control,” Clin. Neurophysiol., vol. 113, no. 6, pp. 767–791, Jun. 2002. [CrossRef] [PubMed] [Google Scholar]
  2. J. D. R. Millán, R. Rupp, G. R. Müller-Putz, R. Murray-Smith, C. Giugliemma, M. Tangermann, C. Vidaurre, F. Cincotti, a Kübler, R. Leeb, C. Neuper, K.-R. Müller, and D. Mattia, “Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges,” Front. Neurosci., vol. 4, no. September, pp. 1–15, Jan. 2010. [Google Scholar]
  3. K. K. Ang, J. Yu, and C. Guan, “Extracting effective features from high density nirs-based BCI for assessing numerical cognition,” 2012 IEEE Int. Conf. Acoust. Speech Signal Process, pp. 2233–2236, Mar. 2012. [Google Scholar]
  4. M. J. Alhaddad, “Common Average Reference (CAR) Improves P300 Speller,”International Journal of Engineering and Technology, vol. 2, no. 3, 2012. [Google Scholar]
  5. X. Lei, P. Yang, P. Xu, T. Liu, and D. Yao, “Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface,” Journal of Electronic Science and Technology of China, vol. 7, no. 1, pp. 17–21, 2009. [Google Scholar]
  6. C. Sannelli, C. Vidaurre, K. Müller, and B. Blankertz, “Common Spatial Pattern Patches - an Optimized Filter Ensemble for Adaptive BCIs,” International Journal of Bioelectromagnetism, vol. 13, no. 3, pp. 161–162, 2011. [Google Scholar]
  7. T. Solis-Escalante, G. Müller-Putz, and G. Pfurtscheller, “Overt foot movement detection in one single Laplacian EEG derivation,” J. Neurosci. Methods, vol. 175, no. 1, pp. 148–53, Oct. 2008. [CrossRef] [Google Scholar]
  8. J. Lu, D. J. McFarland, and J. R. Wolpaw, “Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.,” J. Neural Eng., vol. 10, no. 1, p. 016002, Feb. 2013. [CrossRef] [Google Scholar]
  9. Teplan, M, “Fundamentals of EEG Measurement,” Measurement Science Review, 2(2),1-11,2002. [Google Scholar]
  10. D. J. McFarland, L. M. McCane, S. V. David, and J. R. Wolpaw, “Spatial filter selection for EEG-based communication,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 386–394, Sep. 1997. [CrossRef] [Google Scholar]
  11. G. Pfurtscheller and F. H. Lopes da Silva “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clin. Neurophysiol., vol. 110, no. 11, pp. 1842–57,1999. [CrossRef] [Google Scholar]
  12. Delorme, A. &Makeig, S“EEGlab: An Open Source Toolbox For Analysis Of Single-Trial EEG Dynamics Including Independent Component Analysis,” Journal Of Neuroscience Methods, 134,9-21,2004. [CrossRef] [Google Scholar]
  13. M. Ferdjallah and R. E. Barr. “On the Unit Circle for the Removal of Power line Noise from Biomedical Signals,” vol. 41, no. 6, pp. 529–536, 1994. [Google Scholar]
  14. V. B. Deepa& P. Thangaraj. “A study on classification of EEG Data using the Filters,” vol. 2, no. 4, pp. 94–96, 2011 [Google Scholar]
  15. H. Lakany and B. a Conway, “Classification of Wrist Movements using EEG-based Wavelets Features.,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 5, pp. 5404–5407, 2005. [Google Scholar]
  16. Lakany, H. and Conway, B.A. “Comparing EEG patterns of actual and imaginary wrist movements –A machine learning approach,” Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05, 5 . ICGST, Cairo, Egypt, pp. 124-127, 2005. [Google Scholar]
  17. I. Dokare and N. Kant, “Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery,” vol. 10, no. 1, pp. 19–23, 2014. [Google Scholar]
  18. C. Liu, H. Wang, and Z. Lu, “EEG Classification for Multiclass Motor Imagery BCI,” Chinese Control and Decision Conference, pp. 4450–4453, 2013. [Google Scholar]
  19. S. Bhattacharyya, A. Khasnobish, S. Chatterjee, A. Konar, and D. N. Tibarewala, “Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data,” Int. Conf. Syst. Med. Biol. ICSMB 2010 - Proc., no. December, pp. 126–131, 2010. [CrossRef] [Google Scholar]
  20. O. Carrera-león, J. M. Ramirez, V. Alarcon-aquino, M. Baker, D. D. Croz-baron, and P. Gomez-gil, “A Motor Imagery BCI Experiment using Wavelet Analysis and Spatial Patterns Feature Extraction,” pp. 18–20. [Google Scholar]
  21. C. Elkan, “Evaluating Classifiers”, University of San Diego, California, retrieved [01-05-2013] from pdf [Google Scholar]
  22. R. Palaniappan, “Biological Signal Analysis,” Ventus Publishing, Denmark, 2010. [Google Scholar]
  23. H. Hwang, K. Kwon, and C. Im, “Neurofeedback-based motor imagery training for brain – computer interface (BCI),” vol. 179, pp. 150–156, 2009. [Google Scholar]
  24. S. C. Ng and P. Raveendran, “Comparison of different Montages on to {EEG} classification,” 3rd Kuala Lumpur Int. Conf. Biomed. Eng. 2006, vol. 15, pp. 365–368, 2007. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.