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Denoising and compression of intracortical signals with a modified MDL criterion

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Abstract

Intracortical signals are usually affected by high levels of noise [0 dB signal-to-noise ratio (SNR) is not uncommon] often due to magnetic or electrical coupling between surrounding sources and the recording system. Apart from hindering effective exploitation of the information content in the signals, noise also influences the bandwidth needed to transmit them, which is a problem especially when a large number of channels are to be recorded. In this paper, we propose a novel technique for joint denoising and compression of intracortical signals based on the minimum description length principle. This method was tested on both simulated and experimental signals, and the results showed that the proposed technique achieves improvements in SNR and compression ratios greater than alternative denoising/compression methods.

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Abbreviations

SNR:

Signal-to-noise ratio

BCI:

Brain–computer interface

DWT:

Discrete wavelet transform

DWPT:

Discrete wavelet packet transform

EZW:

Embedded zero tree wavelet

EZWP:

Embedded zero tree wavelet packets

MDL:

Minimum description length

NML:

Normalized maximum likelihood

SNML:

Sub-band-based NML

MNML:

Mixture-based NML

References

  1. Brechet L, Lucas MF, Doncarli C, Farina D (2007) Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Trans Biomed Eng 54:2186–2192

    Article  PubMed  Google Scholar 

  2. Chae MS, Yang Z, Yuce MR, Hoang L, Liu W (2009) A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter. Neural Syst Rehab Eng IEEE Trans 17:312–321

    Article  Google Scholar 

  3. Chae M, Liu W, Yang Z, Chen T, Kim J, Sivaprakasam M, Yuce M (2008) A 128-channel 6mW wireless neural recording IC with on-the-fly spike sorting and UWB transmitter. In: anonymous solid-state circuits conference, 2008. ISSCC 2008. Digest of technical papers. IEEE international, IEEE, pp 146–603

  4. Chang SG, Bin Yu, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. Image Process IEEE Trans 9:1532–1546

    Article  CAS  Google Scholar 

  5. Chen T, Ma T, Chen Y, Chen L (2012) Low power and high accuracy spike sorting microprocessor with on-line interpolation and re-alignment in 90 nm CMOS process. In: anonymous engineering in medicine and biology society (EMBC), 2012 annual international conference of the IEEE, IEEE, pp 4485–4488

  6. Cho J, Paiva AR, Kim S, Sanchez JC, Príncipe JC (2007) Self-organizing maps with dynamic learning for signal reconstruction. Neural Netw 20:274–284

    Article  PubMed  Google Scholar 

  7. Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. Inf Theory IEEE Trans 38:713–718

    Article  Google Scholar 

  8. Craciun S, Cheney D, Gugel K, Sanchez JC, Principe JC (2011) Wireless transmission of neural signals using entropy and mutual information compression. Neural Syst Rehab Eng IEEE Trans 19:35–44

    Article  Google Scholar 

  9. Donoghue JP (2008) Bridging the brain to the world: a perspective on neural interface systems. Neuron 60:511–521

    Article  CAS  PubMed  Google Scholar 

  10. Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90:1200–1224

    Article  Google Scholar 

  11. Donoho DL, Johnstone IM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81:425–455

    Article  Google Scholar 

  12. Grünwald PD (2005) Minimum description length tutorial. In: Grünwald PD, Myung IJ, Pitt MA (eds) Advances in minimum description length: theory and applications. MIT Press, Cambridge, p 23

    Google Scholar 

  13. Harrison RR, Watkins PT, Kier RJ, Lovejoy RO, Black DJ, Greger B, Solzbacher F (2007) A low-power integrated circuit for a wireless 100-electrode neural recording system. Solid-State Circuits IEEE J 42:123–133

    Article  Google Scholar 

  14. Hatsopoulos NG, Donoghue JP (2009) The science of neural interface systems. Annu Rev Neurosci 32:249–266

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Holleman J, Mishra A, Diorio C, Otis B (2008) A micro-power neural spike detector and feature extractor in. 13 μm CMOS. In: anonymous custom integrated circuits conference. CICC 2008. IEEE, IEEE, pp 333–336

  16. Kamboh AM, Raetz M, Oweiss KG, Mason A (2007) Area-power efficient VLSI implementation of multichannel DWT for data compression in implantable neuroprosthetics. Biomed Circuits Syst IEEE Trans 1:128–135

    Article  CAS  Google Scholar 

  17. Karkare V, Gibson S, Markovic D (2011) A 130- W, 64-Channel Neural Spike-Sorting DSP Chip. Solid-State Circuits IEEE J 46:1214–1222

    Article  Google Scholar 

  18. Kipke DR, Shain W, Buzsaki G, Fetz E, Henderson JM, Hetke JF, Schalk G (2008) Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. J Neurosci 28:11830–11838

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480

    Article  Google Scholar 

  20. Krim H, Schick IC (1999) Minimax description length for signal denoising and optimized representation. Inf Theory IEEE Trans 45:898–908

    Article  Google Scholar 

  21. Krim H, Tucker D, Mallat S, Donoho D (1999) On denoising and best signal representation. Inf Theory IEEE Trans 45:2225–2238

    Article  Google Scholar 

  22. Manduca A (1995) Compressing images with wavelet/subband coding. Eng Med Biol Mag IEEE 14:639–646

    Article  Google Scholar 

  23. Neafsey E (1990) The complete ratunculus: output organization of layer V of the cerebral cortex. In: Kolb B, Tees RC (eds) The cerebral cortex of the rat. MIT Press, Cambridge, pp 197–212

    Google Scholar 

  24. Nielsen M, Kamavuako EN, Andersen MM, Lucas MF, Farina D (2006) Optimal wavelets for biomedical signal compression. Med Biol Eng Comput 44:561–568

    Article  PubMed  Google Scholar 

  25. Oweiss KG (2006) A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces. Biomed Eng IEEE Trans 53:1364–1377

    Article  Google Scholar 

  26. Oweiss KG, Mason A, Suhail Y, Kamboh AM, Thomson KE (2007) A scalable wavelet transform VLSI architecture for real-time signal processing in high-density intra-cortical implants. Circuits Syst I Regul P IEEE Trans 54:1266–1278

    Article  Google Scholar 

  27. Paiva AR, Principe JC, Sanchez JC (2005) Compression of spike data using the self-organizing map. In: anonymous neural engineering, 2005. Conference proceedings. 2nd international IEEE EMBS conference on, IEEE, pp 233–236

  28. Perelman Y, Ginosar R (2007) An integrated system for multichannel neuronal recording with spike/LFP separation, integrated A/D conversion and threshold detection. Biomed Eng IEEE Trans 54:130–137

    Article  Google Scholar 

  29. Quiroga RQ, Nadasdy Z, Ben-Shaul Y (2004) Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput 16:1661–1687

    Article  PubMed  Google Scholar 

  30. Rao S, Paiva AR, Principe JC (2007) A novel weighted lbg algorithm for neural spike compression. In: anonymous neural networks, 2007. IJCNN 2007. International joint conference on, IEEE, pp 1883–1887

  31. Rissanen J (2000) MDL denoising. Inf Theory IEEE Trans 46:2537–2543

    Article  Google Scholar 

  32. Rizk M, Bossetti CA, Jochum TA, Callender SH, Nicolelis MA, Turner DA, Wolf PD (2009) A fully implantable 96-channel neural data acquisition system. J Neural Eng 6:026002

    Article  PubMed Central  PubMed  Google Scholar 

  33. Roos T, Myllymaki P, Rissanen J (2009) MDL denoising revisited. Signal Process IEEE Trans 57:3347–3360

    Article  Google Scholar 

  34. Shalchyan V, Jensen W, Farina D (2012) Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings. Biomed Eng IEEE Trans 59:2576–2585

    Article  Google Scholar 

  35. Shapiro JM (1993) Embedded image coding using zerotrees of wavelet coefficients. Signal Process IEEE Trans 41:3445–3462

    Article  Google Scholar 

  36. Sodagar AM, Wise KD, Najafi K (2007) A fully integrated mixed-signal neural processor for implantable Multichannel cortical recording. Biomed Eng IEEE Trans 54:1075–1088

    Article  Google Scholar 

  37. Vetterli M, Kovačević J (1995) Wavelets and subband coding. Prentice Hall PTR Englewood Cliffs, New Jersey

    Google Scholar 

  38. Wesfreid E, Wickerhauser MV (1993) Adapted local trigonometric transforms and speech processing. IEEE Trans Signal Process 41:3596–3600

    Article  Google Scholar 

  39. Yang Y, Mason AJ (2011) Implantable neural spike detection using lifting-based stationary wavelet transform. In: anonymous engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE, IEEE, pp 7294–7297

  40. Zhang F, Aghagolzadeh M, Oweiss K (2010) An implantable neuroprocessor for multichannel compressive neural recording and on-the-fly spike sorting with wireless telemetry. In: biomedical circuits and systems conference (BioCAS), 2010 IEEE, IEEE, pp 1–4

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Acknowledgments

The authors are grateful to Sofyan Hammad at the Department of Health Science and Technology, Aalborg University, for collection of the experimental data.

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Authors and Affiliations

  1. Dipartimento di Automatica ed Informatica, Politecnico di Torino, Turin, Italy

    Elias S. G. Carotti

  2. Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

    Vahid Shalchyan

  3. Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark

    Winnie Jensen

  4. Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Von-Siebold-Str. 4, 37075, Göttingen, Germany

    Vahid Shalchyan & Dario Farina

Authors
  1. Elias S. G. Carotti
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  2. Vahid Shalchyan
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  3. Winnie Jensen
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  4. Dario Farina
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Correspondence to Dario Farina.

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Carotti, E.S.G., Shalchyan, V., Jensen, W. et al. Denoising and compression of intracortical signals with a modified MDL criterion. Med Biol Eng Comput 52, 429–438 (2014). https://doi.org/10.1007/s11517-014-1146-x

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  • DOI: https://doi.org/10.1007/s11517-014-1146-x

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