Andrey Zhdanov, Yehudit Hasson-Meir, Andrey Zhdanov, Yehudit Hasson-Meir, Talma Hendler, Nathan Intrator


We present an efficient and robust computational model for brain state interpretation from EEG single trials. This includes identification of the most relevant time points and electrodes that may be active and contribute to differentiation between the mental states investigated during the experiment. The model includes a regularized logistic regression classifier trained with cross-validation to find the optimal model and its regularization parameter. The proposed framework is generic and can be applied to different classification tasks. In this study we applied it to a classical visual task of distinction between faces and houses. The results show that the obtained single trial prediction is significantly better than chance. Moreover, correct choice of the regularization parameter significantly improves classification results. In addition, the obtained spatial-temporal information of brain activity can give an indication to correlated activity of regions of the brain (spatial) as well as temporal activity correlations between and within EEG electrodes. This spatial-temporal analysis can render a far more holistic interpretability for visual perception mechanism without any a priori bias on certain time periods or scalp locations.


  1. Allison, B. Z., Wolpaw, E. W., Wolpaw, J. R., 2007. Brain computer interface systems: progress and prospects. Expert review of medical devices, 4 (4), pp.463- 474(12).
  2. Bellman, R. E, 1961. Adaptive Control Processes. Princeton University Press, Princeton, NJ.
  3. Ben-Simon, E., Podlipsky, I., Arieli, A., Zhdanov, A., Hendler, T., 2008. Never resting brain: Simultaneous representation of two alpha related processes in humans. Plos One, 3 (12), e3984.
  4. Bentin, S., Allison, T., Puce, A., Perez, E., McCarthy, G., 1996. Electrophysiological studies of faces perception in humans. Journal of Cognitive Neuroscience, 8(6), pp. 551-565.
  5. Blankertz, B., Dornhege, G., Krauledat, M., Müller, K. R., Curio, G., 2007. The noninvasive Berlin braincomputer interface: fast acquisition of effective performance in untrained subjects. NeuroImage, 37(2), pp. 539-550.
  6. Christoforou, C., Sajda, P., Parra, L. C., 2008. Second order bilinear discriminant analysis for single trial EEG analysis. Advances in Neural Information Processing Systems, 20, pp. 313-320.
  7. Delorme, A., Makeig, S., Sejnowski, T., 2001. Automatic artifact rejection for EEG data using high-order statistics and independent component analysis. Proceedings of the 3rd International ICA Conference.
  8. Detre, G., Polyn, S. M., Moore, C., Natu, V., Singer, B., Cohen, J., Haxby, J. V., Norman, K. A., 2006. The Multi-Voxel Pattern Analysis (MVPA) Toolbox. Poster presented at the Annual Meeting of the Organization for Human Brain Mapping, Italy.
  9. Dornhege, G., Millán, J. del R., Hinterberger, T., McFarland, D., Müller, K.-R. (Eds.), 2007. Towards Brain-Computer Interfacing. MIT Press.
  10. Duda, R. O., Hart, P. E., Stork, D. G., 2001. Pattern Recognition 2nd edn (New York: Wiley-Interscience)
  11. Ekman, P., Friesen, W., 1976. Pictures of facial affect, Consulting Psychologists Press, Palo Alto, CA.
  12. Friedman, J., Hastie, T., Tibshirani, R., 2001. The elements of statistical learning. Springer.
  13. Geman, S., Bienenstock , E., 1992. Neural networks and the bias/variance dilemma. Neural Computation, 4 (1), pp. 1-58.
  14. Hosmer , D. W., Lemeshow, S., 1989. Applied logistic regression. New York: John Wiley, pp. 118-24.
  15. Jain, A. K., Duin, R.P.W.,Mao, J., 2000. Statistical pattern recognition: a review IEEE Trans. Pattern Anal. Mach. Intell. 22, pp.4-37
  16. Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., Ritter, H., 2004. BCI competition 2003-data set llb: support vector machines for the p300 speller paradigm IEEE Trans. Biomed. Eng. 51, pp.1073-6.
  17. Kohavi, R., John, G., 1997. Wrappers for feature subset selection. Artificial Intelligence, 97 (1-2), pp. 273-324.
  18. Lal, T., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B., 2004. Support vector channel selection in BCI. IEEE Trans. Biomed. Eng., 51(6), pp. 1003-1010.
  19. Laufs, H., Krakow. K., Sterzer. P., Eger. E., Beyerle. A., Salek-Haddadi. A., Kleinschmidt. A., 2003. Electroencephalographic signatures of attentional and cogntive default modes in spontaneous brain activity fluctuations at rest. Proceedings of the National Academy of Sciences, U.S.A., 100, 11053-11058.
  20. Lehmann, D., Skrandies, W., 1980. Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalogr Clin Neurophysiol, 48 (6), pp. 609-621.
  21. Lehmann, D., Ozaki, H. and Pal, I. 1987. EEG alpha map series: brain microstates by space-oriented adaptive segmentation. Electroenceph. clin. Neurophysiol., 67 (3), pp. 271-288.
  22. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B., 2007. A review of classifcation algorithms for eeg-based brain-computer interfaces. Journal of Neural Engineering, 4 (2), pp. R1.R13.
  23. Lundqvist, D., Flykt, A., Ohman, A.,1998. The Karolinska Directed Emotional Faces (KDEF), Department of Neurosciences, Karolinska Hospital, Stockholm, UK.
  24. Minka, T., 2003. A Comparison of Numerical Optimizers for Logistic Regression. technical report, Dept. of Statistics, Carnegie Mellon University.
  25. Muller, T., Ball, T., Kristeva-Feige, R., Mergner, T., Timmer, J., 2000. Selecting relevant electrode positions for classification tasks based on the electroencephalogram. Medical and Biological Engineering and Computing, 38(1), pp. 62-67.
  26. Murray, M. Brunet, M., Brunet, D., Michel, C. 2008. Topographic ERP analyses: step-by-step tutorial review. Brain Topography, 20 (4), 249-269.
  27. Palaniappan, R., Raveendran, P., Omatu, S., 2002. VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics. IEEE Transactions on Neural Networks, 13(2), pp. 486-491.
  28. Sadeh, B., Zhdanov, A., Podlipsky, I., Hendler, T., Yovel, G., 2008. The validity of the face-selective ERP N170 component during simultaneous recording with functional MRI. Neuroimage, 42 (2), pp.778-786.
  29. Schröder, M., Bogdan, M., Rosenstiel, W., Hinterberger, T., Birbaumer, N., 2003. Automated EEG Feature Selection for Brain Computer Interfaces, Proceedings of 1st International IEEE EMBS Conference on Neural Engineering, Capri Island, Italy.
  30. Tomioka, R., Aihara, K., Müller, K. R., 2007. Logistic regression for single trial eeg classification. In: Schölkopf, B., Platt, J., Hoffman, T. (Eds.), Advances in Neural Information Processing Systems 19. MIT Press, Cambridge, MA, pp. 1377-1384.
  31. Tomioka, R., Müller, K. R., 2010. A regularized discriminative framework for EEG analysis with application to brain-computer interface. Neuroimage. 49 (1), pp.415-32.
  32. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., Vaughan, T. M., 2002. Braincomputer interfaces for communication and control Clin. Neurophysiol. 113 (6), pp. 767-91.
  33. Zhdanov, A., Hendler, T., Ungerleider, L., Intrator, N., 2007. Inferring functional brain States using temporal evolution of regularized classifiers. Comput. Intell. Neurosci, p. 52609.

Paper Citation

in Harvard Style

Zhdanov A., Hasson-Meir Y., Hasson-Meir Y., Zhdanov A., Hendler T. and Intrator N. (2011). INFERENCE OF BRAIN MENTAL STATES FROM SPATIO-TEMPORAL ANALYSIS OF EEG SINGLE TRIALS . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 59-66. DOI: 10.5220/0003159800590066

in Bibtex Style

author={Andrey Zhdanov and Yehudit Hasson-Meir and Yehudit Hasson-Meir and Andrey Zhdanov and Talma Hendler and Nathan Intrator},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
SN - 978-989-8425-35-5
AU - Zhdanov A.
AU - Hasson-Meir Y.
AU - Hasson-Meir Y.
AU - Zhdanov A.
AU - Hendler T.
AU - Intrator N.
PY - 2011
SP - 59
EP - 66
DO - 10.5220/0003159800590066