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Energy Efficient Method for Motor Imagery Data Compression

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Information and Software Technologies (ICIST 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 538))

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Abstract

Electroencephalogram (EEG) is a popular method for measuring the electrical activity of the brain, and diagnose a variety of neurological conditions such as epileptic seizure. Furthermore, most Brain - Computer Interface systems provide modes of communication based on EEG, usually signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in low power wireless systems, where low computational complexity and high speed are required.

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References

  1. Cardenas-Barrera, J.L., Lorenzo-Ginori, J.V., Rodriguez-Valdivia, E.: A wavelet-packets based algorithm for EEG signal compression. Med. Inform. Internet Med. 1, 15–27 (2004)

    Article  Google Scholar 

  2. Casson, A., Yates, D., Smith, S., Duncan, J., Rodriguez-Villegas, E.: Wearable electroencephalography. Eng. Med. Biol. Mag. IEEE 29(3), 44–56 (2010)

    Article  Google Scholar 

  3. Chen, W.H., Smith, C.H., Fralick, S.: A fast computational algorithm for the discrete cosine transform. IEEE Trans. Commun. 25, 1004–1009 (1977)

    Article  Google Scholar 

  4. Daou, H., Labeau, F.: Pre-processing of multi-channel EEG for improved compression performance using SPIHT. In: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. pp. 2232–2235, August 2012

    Google Scholar 

  5. Dauwels, J., Srinivasan, K., Reddy, M., Cichocki, A.: Near-lossless multichannel EEG compression based on matrix and tensor decompositions. IEEE J. Biomed. Health Inform. 17(3), 708–714 (2013)

    Article  Google Scholar 

  6. Fira, M., Goras, L.: Biomedical signal compression based on basis pursuit. In: Proceedings of the 2009 International Conference on Hybrid Information Technology, ICHIT 2009, pp. 541–545. ACM, New York, NY, USA (2009)

    Google Scholar 

  7. Higgins, G., Faul, S., McEvoy, R., McGinley, B., Glavin, M., Marnane, W., Jones, E.: EEG compression using JPEG2000: how much loss is too much? In: 32nd Annual International Conference of the IEEE EMBS, pp. 614–617 (2010)

    Google Scholar 

  8. Higgins, G., McGinley, B., Faul, S., McEvoy, R., Glavin, M., Marnane, W., Jones, E.: The effects of lossy compression on diagnostically relevant seizure information in EEG signals. IEEE J. Biomed. Health Inform. 17(1), 121–127 (2013)

    Article  Google Scholar 

  9. Holewa, K., Nawrocka, A.: Emotiv EPOC neuroheadset in brain - computer interface. In: Control Conference (ICCC), 2014 15th International Carpathian, pp. 149–152 (2014)

    Google Scholar 

  10. Liang, J., Tran, T.D.: Fast multiplierless approximations of the DCT with the lifting scheme. IEEE Trans. Sig. Process. 49, 3032–3044 (2001)

    Article  Google Scholar 

  11. Loeffler, C., Lightenberg, A., Moschytz, G.S.: Practical fast 1-D DCT algorithms with 11 multiplications. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP 1989), pp. 988–991 (1989)

    Google Scholar 

  12. Madan, T., Agarwal, R., Swamy, M.: Compression of long-term EEG using power spectral density. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS 2004, vol. 1, pp. 180–183, September 2004

    Google Scholar 

  13. Martisius, I., Birvinskas, D., Jusas, V., Tamosevicius, Z.: A 2-D DCT hardware codec based on Loeffler algorithm. Electron. Electr. Eng. 7, 47–50 (2011)

    Google Scholar 

  14. Mulvenna, M., Carswell, W., McCullagh, P., Augusto, J., Zheng, H., Jeffers, P., Wang, H., Martin, S.: Visualization of data for ambient assisted living services. Commun. Mag. IEEE 49(1), 110–117 (2011)

    Article  Google Scholar 

  15. Poh, K.K., Marziliano, P.: Compressive sampling of EEG signals with finite rate of innovation. EURASIP J. Adv. Sig. Process. 2010(1), 183105 (2010). doi:10.1155/2010/183105

    Article  MATH  Google Scholar 

  16. Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lécuyer, A.: OpenViBE: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence: Teleoperators Virtual Environ. 19(1), 35–53 (2010)

    Article  Google Scholar 

  17. Salomon, D.: Data Compression. The Complete Reference, 3rd edn. Springer, New York (2004)

    MATH  Google Scholar 

  18. Schloegl, A., Lugger, K., Pfurtscheller, G.: Using adaptive autoregressive parameters for a brain-computer-interface experiment. In: Engineering in Medicine and Biology Society, Proceedings of the 19th Annual International Conference of the IEEE, vol. 4, pp. 1533–1535, October 1997

    Google Scholar 

  19. Sriraam, N.: Quality-on-demand compression of EEG signals for telemedicine applications using neural network predictors. Int. J. Telemedicine Appl. 2011, 13 (2011)

    Google Scholar 

  20. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain - computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Article  Google Scholar 

  21. Yi, W., Qiu, S., Qi, H., Zhang, L., Wan, B., Ming, D.: EEG feature comparison and classification of simple and compound limb motor imagery. J. Neuro Eng. Rehabil. 10(1), 106 (2013)

    Article  Google Scholar 

  22. Ylostalo, J.: Data compression methods for EEG. Technol. Health Care 7(4), 285–300 (1999)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Kaunas University of Technology, Studentu St. 50-210, Kaunas, Lithuania

    Darius Birvinskas

  2. Department of Software Engineering, Kaunas University of Technology, Studentu St. 50-404, Kaunas, Lithuania

    Vacius Jusas

Authors
  1. Darius Birvinskas
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  2. Vacius Jusas
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Correspondence to Darius Birvinskas .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damasevicius

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Birvinskas, D., Jusas, V. (2015). Energy Efficient Method for Motor Imagery Data Compression. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2015. Communications in Computer and Information Science, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-24770-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-24770-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24769-4

  • Online ISBN: 978-3-319-24770-0

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