COMBINING TEMPORAL AND FREQUENCY BASED PREDICTION FOR EEG SIGNALS

Padma Polash Paul, Howard Leung, David A. Peterson, Terrence J. Sejnowski, Howard Poizner

2010

Abstract

This paper presents a novel approach for electroencephalogram (EEG) signal prediction. It combines temporal and frequency based prediction to achieve a good final prediction result. Artificial neural networks are used as the predictive model for signals both in the temporal and frequency domain. In frequency based prediction, the amplitude and the phase of the frequency response are predicted separately. Experiments were conducted on the prediction of EEG data recorded from 13 subjects. Eight performance measures were used to evaluate the performance of our proposed method. Experiment results show that the proposed combined prediction method gives the overall best performance compared with the temporal based prediction alone and the frequency based prediction alone.

References

  1. Peterson, D.A.,, Elliott, C., Song, D.D., Makeig, S., Sejnowski, T.J., Poizner, H. Probabilistic reversal learning is impaired in Parkinson's disease, Neuroscience, July 20, 2009 [E-pub ahead of print].
  2. G., Morris, Alon N., David A., E., Vaadia1, H., Bergman, 2006. Midbrain dopamine neurons encode decisions for future action. Nature Neuroscience. Volume 9.
  3. H. Robbins, 1952. Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society.
  4. Robert A. Stêpieñ, 2002. Testing for non-linearity in EEG signal of healthy subjects. Acta Neurobiol. Exp.
  5. Ying-Jie Li, Yi-Sheng Zhu, and Fei-Yan Fan, 2005. Detecting the Determinism of EEG Time Series Using a Nonlinear Forecasting Method. IEEE Engineering in Medicine and Biology 27th Annual Conference. Shanghai, China.
  6. Florian Mormann, Ralph G. Andrzejak, Christian E. Elger and Klaus Lehnertz, 2007. Seizure prediction: the long and winding road. Brain, Published by Oxford University Press on behalf of the Guarantors of Brain.
  7. Damien Coyle1, Girijesh Prasad2 and Thomas M. McGinnity, 2004. Extracting Features for a BrainComputer Interface by Self-Organising Fuzzy Neural Network-based Time Series Prediction. Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA.
  8. Damien Coyle, Abdul Satti, Girijes Prasad, Thomas M. McGinnity, 2008. Neural Time-Series Prediction Preprocessing Meets Common Spatial Patterns in BrainComputer Interface. 30th Annual International Conference Vancouver, British Columbia, Canada, pp. 2626-2629.
  9. Nicholas I., Sapankevych, 2009. Time Series Prediction Using Support Vector Machines: A Survey. IEEE Computational Intelligence Magazine. pp.25-38
  10. Paul Cristea, V., mladenov, G., Tsenov, Rodica T., Simona P., 2008. Application of neural network, PCA and feature extraction for prediction of nucleotide sequence by using genomic signals. 9th Symposium on Neural Network Application in Electrical Engineering, NEURAL-2008.
  11. Qisong Chen, Xiaowei Chen, Yun Wu, 2008. The Combining Kernel Principal Component Analysis with Support Vector Machine for Time Series Prediction Model. 2nd International Symposium of Inteligent Information Technology. pp. 90-94
  12. K.J. Blinowska and M. Malinowski, 1991. Non-linear and linear forecasting of the EEG time series. Biological Cybernetics, Springer-Vcrlag.
  13. Coles, Michael G.H., Michael D. Rugg, 1996. "Eventrelated brain potentials: an introduction". Electrophysiology of Mind. Oxford Scholarship Online Monographs.
  14. Ou Bai, M., Nakamura, Akio Ikeda, Hiroshi Shibasaki, 2000. Nonlinear Markov Process Amplitude EEG Model for Nonlinear Coupling Interaction of Spontaneous EEG. IEEE Transaction on Biomedicine Engineering, VOL. 47, NO. 9.
  15. J.M.Gorriz, C.G.Puntonet, M. Salmeron, E.W. Lang, Time series prediction using ICA algorithms", Proceedings of the Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 226-230, 2003
  16. Figure 11: Prediction result of a longer segment (M=256) .
  17. Figure 12: Prediction result of a shorter segment (M=32) .
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Paper Citation


in Harvard Style

Polash Paul P., Leung H., A. Peterson D., J. Sejnowski T. and Poizner H. (2010). COMBINING TEMPORAL AND FREQUENCY BASED PREDICTION FOR EEG SIGNALS . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 29-36. DOI: 10.5220/0002696800290036


in Bibtex Style

@conference{biosignals10,
author={Padma Polash Paul and Howard Leung and David A. Peterson and Terrence J. Sejnowski and Howard Poizner},
title={COMBINING TEMPORAL AND FREQUENCY BASED PREDICTION FOR EEG SIGNALS},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={29-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002696800290036},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - COMBINING TEMPORAL AND FREQUENCY BASED PREDICTION FOR EEG SIGNALS
SN - 978-989-674-018-4
AU - Polash Paul P.
AU - Leung H.
AU - A. Peterson D.
AU - J. Sejnowski T.
AU - Poizner H.
PY - 2010
SP - 29
EP - 36
DO - 10.5220/0002696800290036