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Channel and Class Dependent Time-Series Embedding Using Partial Mutual Information Improves Sensorimotor Rhythm Based Brain-Computer Interfaces

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Time Series Analysis, Modeling and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 47))

Abstract

Mutual information has been found to be a suitable measure of dependence among variables for input variable selection. For time-series prediction mutual information can quantify the average amount of information contained in the lagged measurements of a time series. Information quantities can be used for selecting the optimal time lag, τ, and embedding dimension, Δ, to optimize prediction accuracy. Times series modeling and prediction through traditional and computational intelligence techniques such as fuzzy and recurrent neural networks (FNNs and RNNs) have been promoted for EEG preprocessing and feature extraction to maximize signal separability to improve the performance of brain-computer interface (BCI) systems. This work shows that spatially disparate EEG channels have different optimal time embedding parameters which change and evolve depending on the class of motor imagery (movement imagination) being processed. To determine the optimal time embedding for each EEG channel (time-series) for each class an approach based on the estimation of partial mutual information (PMI) is employed. The PMI selected embedding parameters are used to embed the time series for each channel and class before self-organizing fuzzy neural network (SOFNN) based predictors are specialization to predict channel and class specific data in a prediction based signal processing framework, referred to as neural-time-seriesprediction- preprocessing (NTSPP). The results of eighteen subjects show that subject-, channel- and class-specific optimal time embedding parameter selection using PMI improves the NTSPP framework, increasing time-series separability. The chapter also shows how a range of traditional signal processing tools can be combined with multiple computational intelligence based approaches including the SOFNN and practical swarm optimization (PSO) to develop a more autonomous parameter optimization setup and ultimately a novel and more accurate BCI.

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References

  1. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. J. Clinical Neurophysiology 113, 767–791 (2002)

    Article  Google Scholar 

  2. Kubler, A., Kotchoubey, B., Kaiser, J., Wolpaw, J.R., Birbaumer, N.: Brain-Computer communication: unlocking the locked-in. Psychological Bulletin 127(3), 358–375 (2001)

    Article  Google Scholar 

  3. Pfurtscheller, G., Guger, C., Muller, G., Krausz, G., Neuper, C.: Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211–214 (2000)

    Article  Google Scholar 

  4. Coyle, D., Satti, A., Stow, J., McCreadie, K., Carroll, A., McElligott, J.: Operating a Brain Computer Interface: Able Bodied vs. Physically Impaired Performance. In: Proc. of the Recent Advances in Assistive Technology & Engineering Conference (2011)

    Google Scholar 

  5. Stow, J., Coyle, D., Carroll, A., Satti, A., McElligott, J.: Achievable Brain Computer Communication through Short Intensive Motor Imagery Training despite Long Term Spinal Cord Injury. In: Proc. of the Annual IICN Registrar’s Prize in Neuroscience (2011)

    Google Scholar 

  6. Coyle, D., Carroll, A., Stow, J., McCann, A., Ally, A., McElligott, J.: Enabling Control in the Minimally Conscious State in a Single Session with a Three Channel BCI. In: Proc. of the 1st International DECODER Workshop (2012)

    Google Scholar 

  7. Prasad, G., Herman, P., Coyle, D., McDonough, S., Crosbie, J.: Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J. Neuroeng. Rehab. 7(60), 1–17 (2011)

    MATH  Google Scholar 

  8. Coyle, D., Garcia, J., Satti, A., McGinnity, T.M.: EEG-based Continuous Control of a Game using a 3 Channel Motor Imagery BCI. In: IEEE Symposium Series on Computa-tional Intelligence, pp. 88–93 (2011)

    Google Scholar 

  9. Enzinger, C., Ropele, S., Fazekas, F., Loitfelder, M., Gorani, F., Seifert, T., Reiter, G., Neuper, C., Pfurtscheller, G., Muller-Putz, G.: Brain motor system function in a patient with complete spinal cord injury following extensive brain–computer interface training. Exp. Brain Res. 190, 215–223 (2008)

    Article  Google Scholar 

  10. Chatrian, G.E., Peterson, M., Lazarte, J.A.: The blocking of the rolandic wicket rhythm and some central changes related to movement. Electroencephalogr. Clin. Neurophysiol. 11, 497–510 (1959)

    Article  Google Scholar 

  11. Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 113(6), 642–651 (1997)

    Article  Google Scholar 

  12. Felzer, T., Freisleben, B.: Analyzing EEG signals using the probability estimated guarded neural classifier. IEEE Trans. on Neural Sys. and Rehab. Eng. 11(2), 361–371 (2003)

    Article  Google Scholar 

  13. Anderson, C., Sijercic, Z.: Classification of EEG signals from four subjects during five mental tasks. In: Proc of the Conference on Eng. Applications in Neural Networks (EANN 1996), pp. 407–414 (1996)

    Google Scholar 

  14. Muller, K.-R., Anderson, C.W., Birch, G.E.: Linear and nonlinear methods for brain-computer interfaces. IEEE Trans. on Neural Systems and Rehab. Eng. 11(2), 165–169 (2003)

    Article  Google Scholar 

  15. Schlogl, A., Flotzinger, D., Pfurtscheller, G.: Adaptive autoregressive modelling used for single-trial EEG classification. Biomedizinische Technik, Band 42, 162–167 (1997)

    Article  Google Scholar 

  16. Forney, E., Anderson, C.W.: Classification of EEG during Imagined Mental Tasks by Forecasting with Elman Recurrent Neural Networks. In: Proceedings of the International Joint Conference on Neural Networks, pp. 2749–2755 (2011)

    Google Scholar 

  17. Pfurtscheller, G., Neuper, C., Schlogl, A., Lugger, K.: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Transactions on Rehabilitation Engineering 6(3), 316–324 (1998)

    Article  Google Scholar 

  18. Schloegl, A.: The electroencephalogram and the adaptive autoregressive model: theory and applications. Shaker Verlag, Aachen (2000)

    Google Scholar 

  19. Kohlmorgen, J., Müller, K.-R., Rittweger, J., Pawelzik, K.: Identification of non-stationary dynamics in physiological recordings. Biological Cybernetics 83(1), 73–84 (2000)

    Article  MATH  Google Scholar 

  20. Haselsteiner, E., Pfurtscheller, G.: Using Time-Dependent NNs for EEG classification. IEEE Trans. on Rehab. Eng. 8(4), 457–462 (2000)

    Article  Google Scholar 

  21. Coyle, D., Prasad, G., McGinnity, T.M.: A time-series prediction approach for feature extraction in a brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13(4), 461–467 (2005)

    Article  Google Scholar 

  22. Coyle, D.: Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces. IEEE Computational Intelligence Magazine 4(4), 47–59 (2009)

    Article  Google Scholar 

  23. Coyle, D., Prasad, G., McGinnity, T.M.: Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface. IEEE Transactions on Systems, Man and Cybernetics (Part B) 39(6), 1458–1471 (2009)

    Article  Google Scholar 

  24. Coyle, D., Prasad, G., McGinnity, T.M.: Improving the separability of multiple feature types for a brain-computer interface by neural time-series prediction preprocessing. Biomedical Signal Processing and Control, 196–204 (2010)

    Google Scholar 

  25. Sharma, A.: Seasonal to inter annual rainfall probabilistic forecasts for improved water supply management: part 1 – a strategy for system predictor identification. Journal of Hydrology 239, 232–239 (2000)

    Article  Google Scholar 

  26. May, R.J., Maier, H.R., Dandy, G.C., Gayani Fernando, T.M.K.: Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling and Software 23, 1312–1326 (2008)

    Article  Google Scholar 

  27. Blankertz, et al.: BCI Competition III and IV (2005), http://www.bbci.de/competition/

  28. Blankertz, et al.: The BCI competition. III: Validating alternative approaches to actual BCI problems. IEEE Trans. Neural. Syst. Rehabil. Eng. 14, 153–159 (2006)

    Article  Google Scholar 

  29. Schlogl, A., Lee, F., Birschof, H., Pfurtscheller, G.: Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J. of Neural Engineering 2, L.14–L.22 (2005)

    Google Scholar 

  30. Schlogl, et al.: BCI-Competition IV (Dataset 2A and 2B) (2008), http://www.bbci.de/competition/iv/desc_2b.pdf , http://www.bbci.de/competition/iv/desc_2a.pdf

  31. Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 473–482 (2007)

    Article  Google Scholar 

  32. Schlogl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., Pfurtscheller, G.: A fully automated correction method for EOG artifacts in EEG recordings. Clin. Neuro-Phys. 118(1), 98–104 (2007)

    Article  Google Scholar 

  33. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)

    Article  Google Scholar 

  34. Jang, J.S.R.: Neuro-Fuzzy & Soft Computing. Prentice-Hall (1997)

    Google Scholar 

  35. Leng, G.: Algorithmic Developments for Self-Organising Fuzzy Neural Networks. PhD Dissertation, University of Ulster (2003)

    Google Scholar 

  36. Prasad, G., McGinnity, T.M., Leng, G., Coyle, D.: On-line identification of self-organizing fuzzy neural networks for modelling time-varying complex systems. In: Plamen, et al. (eds.) Evolving Intelligent Systems, pp. 302–324. John Wiley, NY (2010)

    Google Scholar 

  37. Coyle, D., Prasad, G., McGinnity, T.M.: Faster Self-organising Fuzzy Neural Network Training and Improved Autonomy with Time-Delayed Synapses for Locally Recurrent Learning. In: Temel (ed.) System and Circuit Design for Biologically-Inspired Learning, pp. 156–183. IGI-Global (2010)

    Google Scholar 

  38. Ramouser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. on Rehab. Eng. 8(4), 441–446 (2000)

    Article  Google Scholar 

  39. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Müller, K.R.: Optimizing spatial filters for robust EEG Analysis. IEEE Signal Processing Magazine, 41–56 (2008)

    Google Scholar 

  40. Satti, A., Coyle, D., Prasad, G.: Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface. In: Proc. of the 2009 IEEE Systems, Man and Cybernetics Conference, pp. 1731–1735 (2009)

    Google Scholar 

  41. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings IEEE International Conference on Neural Networks, vol. 1, pp. 1942–1948 (1995)

    Google Scholar 

  42. Herman, P., Prasad, G., McGinnity, T.M., Coyle, D.: Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 16(4), 317–326 (2008)

    Article  Google Scholar 

  43. Coyle, D., Prasad, G., McGinnity, T.M.: A time-frequency approach to feature extraction for a brain-computer interface with a comparative analysis of performance measures. EURASIP JASP, Trends in Brain-Computer Interfaces (special issue) 19, 3141–3151 (2005)

    Google Scholar 

  44. Coyle, D., Prasad, G., McGinnity, T.M., Herman, P.: Estimating the predictability of EEG recorded over the motor cortex using information theoretic functionals. In: Proceedings of the 2nd International Brain-Computer Interface Workshop and Training Course, Biomedizinische Technik, pp. 43–44 (2004)

    Google Scholar 

  45. Fraser, A.M.: Information and Entropy in Strange Attractors. IEEE Trans. on Info. Theory. 35(2), 245–262 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  46. Palus, M., Pecen, L., Pivka, D.: Estimating predictability: The redundancy and surrogate data method. Neural Network World 5(4), 537–552 (1995)

    Google Scholar 

  47. Palus, M.: Testing for nonlinearity using redundancies: Quantitative and qualitative aspects. Physica D, 186–205 (1995)

    Google Scholar 

  48. Williams, G.P.: Chaos Theory Tamed. Taylor and Francis, London (1997)

    MATH  Google Scholar 

  49. Shannon, C.E., Weaver, W.: The mathematical theory of communication. University of Illinois Press (1963)

    Google Scholar 

  50. Scott, D.W.: Multivariate Density Estimation: Theory, Practice and Visualisation. John Wiley and Sons, New York (1992)

    Book  Google Scholar 

  51. Chow, T.W.S., Huang, D.: Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information. IEEE Transactions on Neural Networks 16(1), 213–224 (2005)

    Article  MathSciNet  Google Scholar 

  52. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)

    MATH  Google Scholar 

  53. Davies, L., Gather, U.: The identification of multiple outliers. Journal of the American Statistical Association 88(423), 782–792 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  54. Zar, J.H.: Biostatistical Analysis, 4th edn., pp. 255–259. Upper Saddle River, New-Jersey (1999)

    Google Scholar 

  55. Greene, J., D’Oliveira, M.: Learning to use statistical tests in psychology. Open University Press (1982)

    Google Scholar 

  56. Satti, A., Guan, C., Coyle, D., Prasad, G.: A covariate shift minimisation method to alleviate non-stationarity effects for an adaptive brain-computer interface. In: 20th International Conference Pattern Recognition, August 23-26, pp. 105–108 (2010)

    Google Scholar 

  57. Krusienski, D.J., Grosse-Wentrup, M., Galan, F., Coyle, D., Miller, K.J., Forney, E., Anderson, C.W.: Critical Issues in Brain Computer Interface Research. Journal of Neural Engineering 8, 025002 (8pp) (2011)

    Google Scholar 

  58. Coyle, D., McGinnity, T.M., Prasad, G.: A multi-class brain-computer interface with SOFNN-based prediction preprocessing. In: IEEE World Congress on Computational Intelligence, pp. 3695–3702 (2008)

    Google Scholar 

  59. Coyle, D., Prasad, G., McGinnity, T.M.: Improving information transfer rates of a brain-computer interface by self-organising fuzzy neural network-based multi-step-ahead time-series prediction. In: Proceedings of the 3rd IEEE Systems, Man and Cybernetics (UK&RI Chapter) Conference, pp. 230–235 (2004)

    Google Scholar 

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  1. Intelligent Systems Research Centre, University of Ulster, Derry, BT48 7JL, UK

    Damien Coyle

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  1. Damien Coyle
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  1. , Electrical & Computer Engineering, University of Alberta, 9107 - 116 Street, 2nd Floor, ECERF, Edmonton, T6G 2V4, Canada

    Witold Pedrycz

  2. , Graduate Institute of Educational Measur, National Taichung University of Educatio, Administration Building A110, Taichung, Taiwan R.O.C.

    Shyi-Ming Chen

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Coyle, D. (2013). Channel and Class Dependent Time-Series Embedding Using Partial Mutual Information Improves Sensorimotor Rhythm Based Brain-Computer Interfaces. In: Pedrycz, W., Chen, SM. (eds) Time Series Analysis, Modeling and Applications. Intelligent Systems Reference Library, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33439-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-33439-9_12

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