title: Strategies for high-performance resource-efficient compression of neural spike recordings.
abstract: Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.
authors.affiliation: Department of Experimental Medical Science, Lund University, Lund, Sweden; Neuronano Research Center, Lund University, Lund, Sweden.
authors.forename: Palmi Thor
authors.initials: PT
authors.lastname: Thorbergsson
authors.affiliation: Department of Experimental Medical Science, Lund University, Lund, Sweden; Neuronano Research Center, Lund University, Lund, Sweden.
authors.forename: Martin
authors.initials: M
authors.lastname: Garwicz
authors.affiliation: Department of Experimental Medical Science, Lund University, Lund, Sweden; Neuronano Research Center, Lund University, Lund, Sweden.
authors.forename: Jens
authors.initials: J
authors.lastname: Schouenborg
authors.affiliation: Department of Electrical and Information Technology, Lund University, Lund, Sweden; Neuronano Research Center, Lund University, Lund, Sweden.
authors.forename: Anders J
authors.initials: AJ
authors.lastname: Johansson
mesh_terms: D000200:Action Potentials / Q000502:physiology; D062207:Brain-Computer Interfaces; D044962:Data Compression; D006801:Humans; D009474:Neurons / Q000502:physiology
references.citation: J Neurosci Methods. 1996 Nov;69(2):175-88
references.pmid: 8946321
references.citation: IEEE Trans Biomed Eng. 2004 Jun;51(6):905-11
references.pmid: 15188857
references.citation: Trends Neurosci. 2006 Sep;29(9):536-46
references.pmid: 16859758
references.citation: J Neurosci Methods. 2012 Oct 15;211(1):133-44
references.pmid: 22960053
references.citation: J Neurophysiol. 2007 Nov;98(5):3109-18
references.pmid: 17855584
references.citation: Neuron. 2002 May 30;34(5):797-806
references.pmid: 12062025
references.citation: IEEE Trans Neural Syst Rehabil Eng. 2010 Oct;18(5):469-78
references.pmid: 20525534
references.citation: Network. 1998 Nov;9(4):R53-78
references.pmid: 10221571
references.citation: IEEE Trans Neural Syst Rehabil Eng. 2009 Apr;17(2):116-27
references.pmid: 19193517
references.citation: IEEE Trans Biomed Eng. 2000 Oct;47(10):1406-11
references.pmid: 11059176
references.citation: Nat Neurosci. 2004 May;7(5):446-51
references.pmid: 15114356
references.citation: Neural Comput. 2004 Aug;16(8):1661-87
references.pmid: 15228749
references.citation: Curr Opin Neurobiol. 2012 Feb;22(1):11-7
references.pmid: 22023727
references.citation: Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4250-3
references.pmid: 21096640
references.citation: IEEE Trans Biomed Circuits Syst. 2014 Feb;8(1):129-37
references.pmid: 24681926
references.citation: J Neural Eng. 2012 Jun;9(3):036005
references.pmid: 22523005
references.citation: Electroencephalogr Clin Neurophysiol. 1997 Mar;102(3):228-39
references.pmid: 9129578
references.citation: J Neural Eng. 2011 Aug;8(4):045006
references.pmid: 21775783
references.citation: Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6998-7001
references.pmid: 19964726
references.citation: IEEE Trans Biomed Eng. 1998 Feb;45(2):180-7
references.pmid: 9473841