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Articles

A Simple but Efficient EEG Data Compression Algorithm for Neuromorphic Applications

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References

  • D. L. Schomer and F. L. da Silva, Nidermeyer’s Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 5th ed. Philadelphia, PA: Lippincott Williams and Wilkins, 2005.
  • J. Jinu, G. Titus, and S. Purushothaman, “EEG based automatic detection of drowsy state,” in Artificial Intelligence and Evolutionary Algorithms in Engineering Systems, Vol. 324, L. Padma Suresh, Ed. Nagercoil: Springer, 2014, pp. 65–72.
  • V. Mihajlovic, B. Grundlehner, R. Vullers, and J. Penders, “Wearable, wireless EEG solutions in daily life applications: what are we missing?” IEEE J. Biomed. Health Inform., Vol. 19, pp. 6–21, Jan. 2015. doi: 10.1109/JBHI.2014.2328317
  • S. Sumit, Jayadeva, and M. Suri, “Recent trends in neuromorphic engineering,” Big Data Analytics, Vol. 1.1, pp. 1–19, 2016. doi:doi: 10.1186/s41044-016-0001-5
  • S. Sumit and Jayadeva, “High performance EEG signal classification using classifiability and the twin SVM,” Appl. Soft Comput., Vol. 30, pp. 305–18, 2015. doi: 10.1016/j.asoc.2015.01.018
  • A. Khalid, M. Massudi, M. Saleh, and M. Amr, “Ensemble classifier for epileptic seizure detection for imperfect EEG data,” Sci. World J., Vol. 2015, pp. 1–15, 2015.
  • Y. Zhang, B. Liu, X. Ji, and D. Huang, “Classification of EEG signals based on autoregressive model and wavelet packet decomposition,” Neural Process. Lett., Vol. 45, pp. 365–78, 2017. doi:doi: 10.1007/s11063-016-9530-1
  • T. Sarker, S. Paul, A. Rayhan, I. Zabir, and C. Shahnaz, “Bispectral higher order statistics and time-frequency domain features for arithmetic task classification from EEG signals,” in Proc. IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR 2017), Dhaka, Ii, 2016.
  • K. Subin and G. Titus, “Karhunen-loeve transform for sleep spindle detection,” in Proc. IEEE 3rd International Conference on Devices, Circuits and Systems (ICDCS 2016), Coimbatore, India, 2016.
  • G. Antoniol and P. Tonella, “EEG data compression techniques,” IEEE Trans. Biomed. Eng., Vol. 44, pp. 105–14, Feb. 1997. doi:doi: 10.1109/10.552239
  • N. Sriraam and C. Eswaran, “Context based error modeling for lossless compression of EEG signals using neural networks,” J. Med. Syst., Vol. 30, pp. 439–48, 2006. doi:doi: 10.1007/s10916-006-9025-0
  • N. Memon, X. Kong, and J. Cinkler, “Context based lossless and near lossless compression of EEG signals,” IEEE Trans. Biomed. Eng., Vol. 3, pp. 231–8, Mar. 1999. doi:doi: 10.1109/4233.788586
  • N. Sriraam, “Correlation dimension based lossless compression of EEG signals,” Biomed. Signal Process. Control, Vol. 7, no. 4, pp. 379–88, 2012. doi: 10.1016/j.bspc.2011.06.007
  • Y. Wongsawat, S. Oraintara, and K. R. Rao, “Integer sub optimal Karhunen- Loeve transform for multi-channel lossless EEG compression,” in Proc. IEEE 14th European Signal Process. Conf. (EUSIPCO “06), Florence, Italy, 2006.
  • A. N. Akansu, W. A. Serdijn, and I. W. Selesnick, “Emerging applications of wavelets: A review,” Phys. Commun., Vol. 3, pp. 1–18, 2010. doi: 10.1016/j.phycom.2009.07.001
  • K. Srinivasan, J. Dauwels, and M. R. Reddy, “A two-dimensional approach to lossless EEG compression,” Biomed. Signal Process. Control, Vol. 6, pp. 387–94, 2011. doi:doi: 10.1016/j.bspc.2011.01.004
  • J. L. C. Barrera, J. V. L. Ginori, and E. R. Valdivia, “A wavelet packet based algorithm for EEG signal compression,” Inf. Health Social Care, Vol. 29, pp. 15–27, 2004.
  • I. Jolliffe, Ed., Principal Component Analysis, 2nd ed., Vol. 61, ser. Statistics. New York: Springer, 2002.
  • P. Comon, “Independent component analysis: A new concept?,” Signal Process., Vol. 36, pp. 287–314, 1994. doi:doi: 10.1016/0165-1684(94)90029-9
  • Z. Anusha, J. Jinu, and T. Geevarghese, “Automatic EEG artifact removal by independent component analysis using critical EEG rhythms,” in 2013 IEEE International Conference Control Communication and Computing (ICCC). IEEE, 2013, pp. 364–7.
  • S. Aviyente, “Compressed sensing framework for EEG compression,” in Proc. IEEE Workshop on Statist. Signal Process., 2007, pp. 181–4.
  • F. C. Morabito et al., “Enhanced compressibility of EEG signal in Alzheimer’s disease patients,” IEEE Sensors J., Vol. 13, pp. 3255–62, Sep. 2013. doi: 10.1109/JSEN.2013.2263794
  • A. Said, “Arithmetic coding,” in Lossless Compression Handbook, K. Sayood, Ed. New York: Academic Press, 2003, pp. 447–55.
  • A. Said and W. A. Pearlman, “A new fast and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video Technol., Vol. 6, pp. 243–50, Jun. 1996. doi:doi: 10.1109/76.499834
  • I. Capurro, F. Lecumberry, Á. Martín, I. Ramírez, E. Rovira, and G. Seroussi, “Low complexity, multichannel, lossless and near lossless EEG compression,” in Proc. IEEE 22nd European Signal Process. Conf. (EUSIPCO “14), Florence, Italy, Sep. 1–5, 2006, pp. 2040–44.
  • N. Sriraam, “Context based near lossless compression of EEG signals using neural network predictors,” Int. J. Electron. Commun. (AE), Vol. 63, pp. 311–20, 2009. doi:doi: 10.1016/j.aeue.2008.01.012
  • J. Dauwels, K. Srinivasan, M. R. Reddy, and A. Cichocki, “Near lossless multichannel EEG compression based on matrix and tensor decompositions,” IEEE J. Biomed. Health. Inf., Vol. 17, pp. 708–14, 2013. doi: 10.1109/TITB.2012.2230012
  • K. Srinivasan, J. Dauwels, and M. R. Reddy, “Multichannel EEG compression: Wavelet based image and volumetric coding approach,” IEEE J. Biomed. Health Inform., Vol. 17, pp. 113–20, Jan. 2013. doi:doi: 10.1109/TITB.2012.2194298
  • L. Lin, Y. Meng, J. P. Chen, and Z. B. Li, “Multichannel EEG compression based on ICA and SPIHT,” Biomed. Signal Process. Control, Vol. 20, pp. 45–51, 2015. doi:doi: 10.1016/j.bspc.2015.04.001
  • D. Birvinskas, V. Jusas, I. Martisius, and R. Damasevicius, “Fast DCT algorithms for EEG data compression in embedded systems,” Computer Science and Inform. Syst., Vol. 12, pp. 49–62, Jan. 2015. doi:doi: 10.2298/CSIS140101083B
  • A. F. B. Hejrati and F. A. Mohammadi, “Efficient lossless multichannel EEG compression based on channel clustering,” Biomed. Signal Process. Control, Vol. 31, pp. 295–300, 2017. doi:doi: 10.1016/j.bspc.2016.08.024
  • G. Titus and M. S. Sudhakar, “A simple and efficient algorithm operating with linear time for MCEEG data compression,” Australas. Phys. Eng. Sci. Med., Vol. 40, no. 3, pp. 759–68, 2017. doi:doi: 10.1007/s13246-017-0575-x
  • M. L. Overton, Numerical computing with IEEE floating point arithmetic. Philadelphia, PA: Siam, 2001.
  • D. Goldberg, “What every computer scientist should know about floating point arithmetic,” ACM Comput. Surv., Vol. 23, no. 1, pp. 5–48, 1991. doi:doi: 10.1145/103162.103163
  • S. Gal, “An accurate elementary mathematical library for the IEEE floating point standard,” ACM Trans. Math. Software, Vol. 17, no. 1, pp. 26–45, 1991. doi: 10.1145/103147.103151
  • V. Watson, “Run Length Encoding,” May 10 2002, US Patent App. 10/143,542.
  • D. Lemire and L. Boytsov, “Decoding Billions of Integers Per Second Through Vectorization,” 2012, arXiv:1209.2137v6.
  • M. Tangermann et al., “Review of the BCI competition IV,” Front. Neurosci., Vol. 6, p. 55, Jul. 2012.
  • B. Blankertz, G. Curio, and K. R. Mller, “Classifying single trial EEG: Towards brain computer interfacing,” in Advances in Neural Inf. Proc. Systems 14 (NIPS 01), Vol. 14, S. B. T. G. Diettrich and Z. Ghahramani, Eds., 2002, pp. 1–8.
  • B. Blankertz et al., “The BCI competition III: Validating alternative approaches to actual BCI problems,” IEEE Trans. Neural Sys. Rehab. Eng., Vol. 14, pp. 153–9, 2006. doi:doi: 10.1109/TNSRE.2006.875642
  • M. Naeem, C. Brunner, R. Leeb, B. Graimann, and G. Pfurtscheller, “Separability of four class motor imagery data using independent components analysis,” J. Neural Eng., Vol. 3, pp. 208–16, Jun. 2006. doi: 10.1088/1741-2560/3/3/003
  • A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation, Vol. 101, pp. E215–20, Jun. 2000.
  • C. B. Prieto, M. B. Velasco, J. Crdenas Barrera, and F. C. Roldn, “Analysis of tractable distortion metrics for EEG compression applications,” Physiol. Meas., Vol. 33, pp. 1237–47, Jun. 2012. doi: 10.1088/0967-3334/33/7/1237

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