Predicting Intended Movement Direction Using EEG from Human Posterior Parietal Cortex

  • Yijun Wang
  • Scott Makeig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)

Abstract

The posterior parietal cortex (PPC) plays an important role in motor planning and execution. Here, we investigated whether noninvasive electroencephalographic (EEG) signals recorded from the human PPC can be used to decode intended movement direction. To this end, we recorded whole-head EEG with a delayed saccade-or-reach task and found direction-related modulation of event-related potentials (ERPs) in the PPC. Using parietal EEG components extracted by independent component analysis (ICA), we obtained an average accuracy of 80.25% on four subjects in binary single-trial EEG classification (left versusright). These results show that in the PPC, neuronal activity associated with different movement directions can be distinguished using EEG recording and might, thus, be used to drive a noninvasive brain-machine interface (BMI).

Keywords

posterior parietal cortex (PPC) electroencephalography (EEG) independent component analysis (ICA) brain-machine interface (BMI) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lebedev, M.A., Nicolelis, M.A.L.: Brain-Machine Interfaces: Past, Present and Future. Trends in Neurosciences 29(9), 536–546 (2006)CrossRefPubMedGoogle Scholar
  2. 2.
    Taylor, D.M., Tillery, S.I.H., Schwartz, A.B.: Direct Cortical Control of 3D Neuroprosthetic Devices. Science 296, 1829–1832 (2002)CrossRefPubMedGoogle Scholar
  3. 3.
    Nicolelis, M.A.L.: Actions from Thoughts. Nature 409, 403–440 (2001)CrossRefPubMedGoogle Scholar
  4. 4.
    Hochberg, L.R., Serruya, M.D., Friehs, G.M., Mukand, J.A., Saleh, M., Caplan, A.H., Branner, A., Chen, D., Penn, R.D., Donoghue, J.P.: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099), 164–171 (2006)CrossRefPubMedGoogle Scholar
  5. 5.
    Quiroga, R.Q., Snyder, L.H., Bastista, A.P., Andersen, R.A.: Movement Intention Is Better Predicted than Attention in the Posterior Parietal Cortex. J. Neurosci. 26(13), 3615–3620 (2006)CrossRefGoogle Scholar
  6. 6.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J., Vaughan, T.M.: Brain-Computer Interface Technology: A Review of the First International Meeting. IEEE Trans. Rehabil. Eng. 8, 164–173 (2000)CrossRefPubMedGoogle Scholar
  7. 7.
    Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-Computer Interfaces for Communication and Control. Clinical Neurophysiology 113(6), 767–791 (2002)CrossRefPubMedGoogle Scholar
  8. 8.
    Birbaumer, N.: Breaking the Silence: Brain-Computer Interfaces (BCI) for Communication and Motor Control. Psychophysiology 43, 517–532 (2006)CrossRefPubMedGoogle Scholar
  9. 9.
    Hammon, P.S., Makeig, S., Poizner, H., Todorov, E., de Sa, V.R.: Predicting Reaching Targets from Human EEG. IEEE Signal Processing Magazine 25(1), 69–77 (2008)CrossRefGoogle Scholar
  10. 10.
    Waldert, S., Preissl, H., Demandt, E., Braun, C., Birbaumer, N., Aertsen, A., Mehring, C.: Hand Movement Direction Decoded from MEG and EEG. J. Neurosci. 28(4), 1000–1008 (2008)CrossRefPubMedGoogle Scholar
  11. 11.
    Delorme, A., Makeig, S.: EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis. J. Neurosci. Meth. 134, 9–21 (2004)CrossRefGoogle Scholar
  12. 12.
    Makeig, S., Westerfield, M., Jung, T.P., Townsend, J., Courchesne, E., Sejnowski, T.J.: Dynamic Brain Sources of Visual Evoked Responses. Science 295, 690–694 (2002)CrossRefPubMedGoogle Scholar
  13. 13.
    Jung, T.P., Makeig, S., McKeown, M.J., Bell, A.J., Lee, T.W., Sejnowski, T.J.: Imaging Brain Dynamics Using Independent Component Analysis. Proc. IEEE 89, 1107–1122 (2001)CrossRefGoogle Scholar
  14. 14.
    James, C.J., Hesse, C.W.: Independent Component Analysis for Biomedical Signals. Physiol. Meas. 26, R15–R39 (2005)Google Scholar
  15. 15.
    Lee, T.W., Girolami, M., Sejnowski, T.J.: Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources. Neural Comput. 11(2), 417–441 (1999)CrossRefPubMedGoogle Scholar
  16. 16.
    Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F., Arnaldi, B.: A Review of Classification Algorithms for EEG-Based Brain-Computer Interfaces. J. Neural Eng. 4, R1–R13 (2007)Google Scholar
  17. 17.
    Müller, K.R., Krauledat, M., Dornhege, G., Curio, G., Blankertz, B.: Machine Learning Techniques for Brain-Computer Interfaces. Biomed. Tech. 49(1), 11–22 (2004)Google Scholar
  18. 18.
    Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., Ritter, H.: BCI Competition 2003-Data Set IIb: Support Vector Machines for the P300 Speller Paradigm. IEEE Trans. Biomed. Eng. 51(6), 1073–1076 (2004)CrossRefPubMedGoogle Scholar
  19. 19.
    Chang, C., Lin, C.: LIBSVM : a Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  20. 20.
    Calton, J.L., Dickinson, A.R., Snyder, L.H.: Non-Spatial, Motor-Specific Activation in Posterior Parietal Cortex. Nat. Neurosci. 5, 580–588 (2002)CrossRefPubMedGoogle Scholar
  21. 21.
    Thut, G., Nietzel, A., Brandt, S.A., Pascual-Leone, A.: Alpha-Band Electroencephalographic Activity over Occipital Cortex Indexes Visuospatial Attention Bias and Predicts Visual Target Detection. J. Neurosci. 26(37), 9494–9502 (2006)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yijun Wang
    • 1
  • Scott Makeig
    • 1
  1. 1.Swartz Center for Computational Neuroscience, Institute for Neural ComputationUniversity of CaliforniaSan DiegoUSA

Personalised recommendations