skip to main content
research-article

Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

Published:22 May 2018Publication History
Skip Abstract Section

Abstract

High-density electrode arrays used to read out neural activity will soon surpass the limits of the amount of data that can be transferred within reasonable energy budgets. This is true for wired brain implants when the required bandwidth becomes very high, and even more so for untethered brain implants that require wireless transmission of data. We propose an energy-efficient spike data extraction solution for high-density electrode arrays, capable of reducing the data to be transferred by over 85%. We combine temporal and spatial spike data analysis with low implementation complexity, where amplitude thresholds are used to detect spikes and the spatial location of the electrodes is used to extract potentially useful sub-threshold data on neighboring electrodes. We tested our method against a state-of-the-art spike detection algorithm, with prohibitively high implementation complexity, and found that the majority of spikes are extracted reliably. We obtain further improved quality results when ignoring very small spikes below 30% of the voltage thresholds, resulting in 91% accuracy. Our approach uses digital logic and is therefore scalable with an increasing number of electrodes.

References

  1. Hervé Abdi and Lynne J. Williams. 2010. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2, 4 (2010), 433--459. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Oskar Andersson, Babak Mohammadi, and Joachim Neves Rodrigues. 2016. A Wide-Operating Range Standard-Cell Based Memory in 28nm FD-SOI. Retrieved from http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:386-kluedo-43308Google ScholarGoogle Scholar
  3. E. Biffi, D. Ghezzi, A. Pedrocchi, and G. Ferrigno. 2010. Development and validation of a spike detection and classification algorithm aimed at implementation on hardware devices. Computational Intelligence and Neuroscience 2010 (2010), 8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Hsiao-Lung Chan, Ming-An Lin, Tony Wu, Shih-Tseng Lee, Yu-Tai Tsai, and Pei-Kuang Chao. 2008. Detection of neuronal spikes using an adaptive threshold based on the max--min spread sorting method. Journal of Neuroscience Methods 172, 1 (2008), 112--121.Google ScholarGoogle ScholarCross RefCross Ref
  5. Tony F. Chan, Gene H. Golub, and Randall J. LeVeque. 1983. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician 37, 3 (1983), 242--247.Google ScholarGoogle ScholarCross RefCross Ref
  6. Guillaume Charvet, Lionel Rousseau, Olivier Billoint, Sadok Gharbi, Jean-Pierre Rostaing, Sébastien Joucla, Michel Trevisiol, Alain Bourgerette, Philippe Chauvet, Céline Moulin, FranÃğois Goy, Bruno Mercier, Mikael Colin, Serge Spirkovitch, Hervé Fanet, Pierre Meyrand, Régis Guillemaud, and Blaise Yvert. 2010. : A versatile high-density 3D microelectrode array system using integrated electronics. Biosensors and Bioelectronics 25, 8 (2010), 1889--1896.Google ScholarGoogle ScholarCross RefCross Ref
  7. J. Dragas, D. Jäckel, F. Franke, and A. Hierlemann. 2013. An unsupervised method for on-chip neural spike detection in multi-electrode recording systems. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2535--2538.Google ScholarGoogle Scholar
  8. Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27, 8 (2006), 861--874. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. T. Gemmeke, M. M. Sabry, J. Stuijt, P. Raghavan, F. Catthoor, and D. Atienza. 2014. Resolving the memory bottleneck for single supply near-threshold computing. In 2014 Design, Automation Test in Europe Conference Exhibition (DATE’14). ACM, 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Daniel Halperin, Ben Greenstein, Anmol Sheth, and David Wetherall. 2010. Demystifying 802.11 n power consumption. In 2010 International Conference on Power Aware Computing and Systems. ACM, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Shantanu P. Jadhav, Caleb Kemere, P. Walter German, and Loren M. Frank. 2012. Awake hippocampal sharp-wave ripples support spatial memory. Science 336, 6087 (2012), 1454--1458.Google ScholarGoogle ScholarCross RefCross Ref
  12. Shantanu P. Jadhav, Caleb Kemere, P. Walter German, Loren M. Frank, and J. Macarthur. 2012. Supporting Online Material for Awake Hippocampal Sharp-wave Ripples Support Spatial Memory. Retrieved from www.sciencemag.org/cgi/content/full/science.1217230/DC1.Google ScholarGoogle Scholar
  13. Awais M. Kamboh, Andrew Mason, and Karim G. Oweiss. 2008. Analysis of lifting and B-spline DWT implementations for implantable neuroprosthetics. Journal of Signal Processing Systems 52, 3 (2008), 249--261. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Fabian Kloosterman, Stuart P. Layton, Zhe Chen, and Matthew A. Wilson. 2014. Bayesian decoding using unsorted spikes in the rat hippocampus. Journal of Neurophysiology 111, 1 (2014), 217--227.Google ScholarGoogle ScholarCross RefCross Ref
  15. E. Koutsos, S. E. Paraskevopoulou, and T. G. Constandinou. 2013. A 1.5 W NEO-based spike detector with adaptive-threshold for calibration-free multichannel neural interfaces. In 2013 IEEE International Symposium on Circuits and Systems (ISCAS’13). IEEE, 1922--1925.Google ScholarGoogle Scholar
  16. C. M. Lopez, S. Mitra, J. Putzeys, B. Raducanu, M. Ballini, A. Andrei, S. Severi, M. Welkenhuysen, C. Van Hoof, S. Musa, and R. F. Yazicioglu. 2016. 22.7 A 966-electrode neural probe with 384 configurable channels in 0.13 m SOI CMOS. In 2016 IEEE International Solid-State Circuits Conference (ISSCC). IEEE, San Francisco, CA, 392--393.Google ScholarGoogle Scholar
  17. Jens-Oliver Muthmann, Hayder Amin, Evelyne Sernagor, Alessandro Maccione, Dagmara Panas, Luca Berdondini, Upinder S. Bhalla, and Matthias H. Hennig. 2015. Spike detection for large neural populations using high density multielectrode arrays. Frontiers in Neuroinformatics 9 (2015), 21.Google ScholarGoogle ScholarCross RefCross Ref
  18. Pouya Ostovari, Jie Wu, and Abdallah Khreishah. 2014. Network Coding Techniques for Wireless and Sensor Networks. Springer, Berlin, 129--162.Google ScholarGoogle Scholar
  19. K. G. Oweiss, A. Mason, Y. Suhail, A. M. Kamboh, and K. E. Thomson. 2007. A scalable wavelet transform VLSI architecture for real-time signal processing in high-density intra-cortical implants. IEEE Transactions on Circuits and Systems I: Regular Papers 54, 6 (2007), 1266--1278.Google ScholarGoogle ScholarCross RefCross Ref
  20. D. M. W. Powers. 2011. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies 2, 1 (2011), 37--63.Google ScholarGoogle ScholarCross RefCross Ref
  21. Rodrigo Quian Quiroga. 2012. Spike sorting. Current Biology 22, 2 (2012), R45--R46.Google ScholarGoogle ScholarCross RefCross Ref
  22. B. C. Raducanu, R. F. Yazicioglu, C. M. Lopez, M. Ballini, J. Putzeys, S. Wang, A. Andrei, M. Welkenhuysen, N. van Helleputte, S. Musa, R. Puers, F. Kloosterman, C. van Hoof, and S. Mitra. 2016. Time multiplexed active neural probe with 678 parallel recording sites. In 2016 46th European Solid-State Device Research Conference (ESSDERC’16). IEEE, 385--388.Google ScholarGoogle Scholar
  23. Hernan Gonzalo Rey, Carlos Pedreira, and Rodrigo Quian Quiroga. 2015. Past, present and future of spike sorting techniques. Brain Research Bulletin 119, Part B (2015), 106--117. Advances in electrophysiological data analysisGoogle ScholarGoogle Scholar
  24. A. Rodriguez-Perez, J. Ruiz-Amaya, M. Delgado-Restituto, and Á Rodriguez-Vazquez. 2012. A low-power programmable neural spike detection channel with embedded calibration and data compression. IEEE Transactions on Biomedical Circuits and Systems 6, 2 (2012), 87--100.Google ScholarGoogle ScholarCross RefCross Ref
  25. Cyrille Rossant, Shabnam N. Kadir, Dan F. M. Goodman, John Schulman, Maximilian L. D. Hunter, Aman B. Saleem, Andres Grosmark, Mariano Belluscio, George H. Denfield, Alexander S. Ecker, Andreas S. Tolias, Samuel Solomon, György Buzsáki, Matteo Carandini, and Kenneth D. Harris. 2016. Spike Sorting for Large, Dense Electrode Arrays. Technical Report. Nature Publishing Group.Google ScholarGoogle Scholar
  26. Micha E. Spira and Aviad Hai. 2013. Multi-electrode array technologies for neuroscience and cardiology. Nature Nanotechnology 8, 2 (2013), 83--94.Google ScholarGoogle ScholarCross RefCross Ref
  27. Eran Stark and Moshe Abeles. 2007. Predicting movement from multiunit activity. Journal of Neuroscience 27, 31 (2007), 8387--8394.Google ScholarGoogle ScholarCross RefCross Ref
  28. Nicholas V. Swindale and Martin A. Spacek. 2015. Spike detection methods for polytrodes and high density microelectrode arrays. Journal of Computational Neuroscience 38, 2 (2015), 249--261. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. T. Torfs, A. A. A. Aarts, M. A. Erismis, J. Aslam, R. F. Yazicioglu, K. Seidl, S. Herwik, I. Ulbert, B. Dombovari, R. Fiath, B. P. Kerekes, R. Puers, O. Paul, P. Ruther, C. Van Hoof, and H. P. Neves. 2011. Two-dimensional multi-channel neural probes with electronic depth control. IEEE Transactions on Biomedical Circuits and Systems 5, 5 (Oct. 2011), 403--412.Google ScholarGoogle ScholarCross RefCross Ref
  30. L. Traver, C. Tarín, P. Martí, and N. Cardona. 2007. Adaptive-threshold neural spike detection by noise-envelope tracking. Electronics Letters 43, 24 (Nov. 2007), 1333--1335.Google ScholarGoogle ScholarCross RefCross Ref
  31. P. T. Watkins, G. Santhanam, K. V. Shenoy, and R. R. Harrison. 2004. Validation of adaptive threshold spike detector for neural recording. In 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS’04), Vol. 2. IEEE, 4079--4082.Google ScholarGoogle Scholar
  32. Eric W. Weisstein. 2017. Variance. Retrieved from http://mathworld.wolfram.com/Variance.html.Google ScholarGoogle Scholar
  33. B. P. Welford. 1962. Note on a Method for Calculating Corrected Sums of Squares and Products. Technometrics 4, 3 (1962), 419--420. arXiv:https://amstat.tandfonline.com/doi/pdf/10.1080/00401706.1962.10490022Google ScholarGoogle ScholarCross RefCross Ref
  34. Patrick D. Wolf. 2008. Thermal considerations for the design of an implanted cortical brain--machine interface (BMI). Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK3932/.Google ScholarGoogle Scholar
  35. Y. Yang, A. Kamboh, and J. M. Andrew. 2010. Adaptive threshold spike detection using stationary wavelet transform for neural recording implants. In 2010 Biomedical Circuits and Systems Conference (BioCAS). IEEE, Paphos, Cyprus, 9--12.Google ScholarGoogle Scholar
  36. J. Zhang, Y. Suo, S. Mitra, S. P. Chin, S. Hsiao, R. F. Yazicioglu, T. D. Tran, and R. Etienne-Cummings. 2014. An efficient and compact compressed sensing microsystem for implantable neural recordings. IEEE Transactions on Biomedical Circuits and Systems 8, 4 (Aug. 2014), 485--496.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!