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A New Computational Method for Single-Trial-EEG-Based BCI

Proposal of the Number of Electrodes
  • Shin’ichi Fukuzumi
  • Hiromi Yamaguchi
  • Kazufumi Tanaka
  • Toshimasa Yamazaki
  • Takahiro Yamanoi
  • Ken-ichi Kamijo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8521)

Abstract

In this paper, the categorization of single-trial EEG data recorded during the MI-related task, as another data reduction, will be attempted, because the categorical data would require less storage and computational time than continuous one. The categorization will be realized by equivalent current dipole source localization (ECDL). To analyze this, we used EEG data and visually evoked related potentials (v-ERP) led by 32 electrodes. From the result of single-trial v-ERP, only 6 electrode v-ERPs have a remarkable reaction. Therefore, from the view point of business, it is found that the minimum number of electrodes have been seven.

Keywords

EEG Brain Computer Interface equivalent current dipole source localization topography 

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References

  1. 1.
    Wolpow, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., Vaughan, T.: Brain-Computer Interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002)CrossRefGoogle Scholar
  2. 2.
    Townsend, G., Graimann, B., Pfurtscheller, G.: Continuous EEG classification during motor imagery-Simulation of an asynchronous BCI. IEEE Transaction of Neural System and Rehabilitation Engineering 12, 258–265 (2004)CrossRefGoogle Scholar
  3. 3.
    Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology 110(11), 1842–1857 (1999)CrossRefGoogle Scholar
  4. 4.
    Miller, K.J., Schalk, G., Fetz, E.E., den Nijs, M., Ojemann, J.G., Rao, R.P.N.: Cortical activity during motor execution, motor imagery, and imagery-based online feedback. PNAS 107, 4430–4435 (2010)CrossRefGoogle Scholar
  5. 5.
    Leocani, L., Toro, C., Manganotti, P., Zhuang, P., Hallett, M.: Event-related coherence and event-related desynchronization / synchronization in the 10 Hz and 20 Hz EEG during self-paced movements. Electroencephalography and Clinical Neurophysiology / Evoked Potentials Section 104(3), 199–206 (1997)CrossRefGoogle Scholar
  6. 6.
    Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 103(6), 642–651 (1997)CrossRefGoogle Scholar
  7. 7.
    Zhou, J., Yao, J., Deng, J., Dewald, J.P.A.: EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects. Computers in Biology and Medicine, 443–452 (2009)Google Scholar
  8. 8.
    Wang, D., Miao, D., Blohm, G.: Multi-class motor imagery EEG decoding for brain-computer interfaces. Frontier in Neuroscience 6, article 151, 1–13 (2012)Google Scholar
  9. 9.
    Hsu, W.Y.: EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features. Journal of Neuroscience and Methods 189, 295–302 (2010)CrossRefGoogle Scholar
  10. 10.
    Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: Toward enhanced P300 speller performance. Journal of Neuroscience Methods 167, 15–21 (2008)CrossRefGoogle Scholar
  11. 11.
    Sakamoto, Y., Aono, M.: Supervised Adaptive Downsampling for P300-Based Brain-Computer Interface. In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009 (2009)Google Scholar
  12. 12.
    Arvaneh, M., Guan, C.T., Ang, K.K., Quek, C.: Optimizing the channel selection and classification accuracy in EEG-based BCI source. IEEE Transaction of Biomedical Engineering 58, 1865–1873 (2011)CrossRefGoogle Scholar
  13. 13.
    Kamrunnahar, M., Dias, N.S., Schiff, S.J.: Optimization of Electrode Channels in Brain Computer Interfaces. In: Conference Proceedings of IEEE Engineering and Medical Biological Society, pp. 6477–6480 (2009)Google Scholar
  14. 14.
    Sannelli, C., Dickhausa, T., Halderc, S., Hammerc, E., Mullera, K., Blankertz, B.: On optimal channel configurations for SMR based braincomputer interfaces. Brain Topography, 186–193 (2010)Google Scholar
  15. 15.
    Qin, L., Ding, L., He, B.: Motor Imagery Classification by Means of Source Analysis for Brain Computer Interface Applications. Journal of Neural Engineering, 135–141 (2004)Google Scholar
  16. 16.
    Kamousi, B., Liu, Z., He, B.: Classificationi of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. IEEE Transaction of Neural System and Rehabilitation Engineering 13(2), 166–171 (2005)CrossRefGoogle Scholar
  17. 17.
    Congedo, M., Lotte, F., Lecuyer, A.: Classification of movement intention by spatially filtered electromagnetic inverse solution. Physics in Medicine and Biology 51, 1971–1989 (2006)CrossRefGoogle Scholar
  18. 18.
    Noirhomme, Q., Kitney, R.L., Macq, B.: Single-trial EEG source reconstruction for brain-computer interface. IEEE Transaction of Biomedical Engineering 55, 1592–1601 (2008)CrossRefGoogle Scholar
  19. 19.
    Makeig, S., Bell, A.J., Jung, T.P., Sejnowski, T.J.: Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 145–151 (1996)Google Scholar
  20. 20.
    Delorme, A., Sejnowski, T.J., Maikeig, S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34, 1443–1449 (2007)CrossRefGoogle Scholar
  21. 21.
    Hoffman, S., Falkenstein, M.: The correction of eye blink artefacts in the EEG: a comparison of two prominent methods. PLoS One 3 (2008)Google Scholar
  22. 22.
    Xu, N., Gao, X., Hong, B., Miao, X., Gao, S., Yang, F.: BCI competition 2003-Data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications. IEEE Transaction of Biometdical Engieeing 51, 1067–1072 (2004)CrossRefGoogle Scholar
  23. 23.
    Wang, Y., Wang, Y.T., Jung, T.P.: Translation of EEG spatial filters from resting to motor imagery using independent component analysis. PLoS One 7, e37665 (2012)Google Scholar
  24. 24.
    Kamijo, K., Kiyuna, T., Takaki, Y., Kenmochi, A., Tanigawa, T., Yamazaki, T.: Integrated approach of an artificial neural network and numerical analysis to multiple equivalent current dipole source localization. Frontier Medical and Biological Engineering 10(4), 285–301 (2001)CrossRefGoogle Scholar
  25. 25.
    Kamijo, K., Kawashima, R., Yamazaki, T., Kiyuna, T., Takaki, Y.: An event-related functional magnetic resonance imaging study of movement imagery. Transaction of the Japanese Society for Medical and Biological Engineering 42, 16–21 (2004)Google Scholar
  26. 26.
    Oldfield, R.C.: The assessment and analysis of handedness: the Edinburgh Inventory. Neuropsychologia 9, 97–113 (1971)CrossRefGoogle Scholar
  27. 27.
    Soufflet, L., Toussaint, M., Luthringer, R., Gresser, J., Minot, R., Macher, J.P.: A statistical evaluation of the main interpolation methods applied to 3-dimensional EEG mapping. Electroenceph. Clin. Neurophysiol. 79, 393–402 (1991)CrossRefGoogle Scholar
  28. 28.
    Kamijo, K., Yamazaki, T., Kiyuna, T., Takaki, Y., Kuroiwa, Y.: Brain Topgraphy 14(4), 279–292 (2002)Google Scholar
  29. 29.
    Yamamoto, K., Yamazaki, T., Kamijo, K., Yamanoi, T., Fukuzumi, S.: Ailent speech BCI: Learning and decoding algorithms using single-trial EEGs and speech signals. In: Proceedings of BPES 2011 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shin’ichi Fukuzumi
    • 1
  • Hiromi Yamaguchi
    • 2
  • Kazufumi Tanaka
    • 2
  • Toshimasa Yamazaki
    • 2
  • Takahiro Yamanoi
    • 3
  • Ken-ichi Kamijo
    • 1
  1. 1.NEC CorporationKawasakiJapan
  2. 2.Kyushu Institute of TechnologyIizukaJapan
  3. 3.Hokkai gakuen UniversitySapporoJapan

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