Compression of spike data using the self-organizing map

António R C Paiva, José C. Príncipe, Justin C. Sanchez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Motivated by current attempts to use wireless in Brain-Machine Interfaces (BMIs), this paper presents a method for the compression of spike data. Supported by Vector Quantization (VQ) theory, we use a 1-dimensional Self-Organizing Map (SOM) to quantize vectors of input samples. The indices are entropy coded to further reduce the necessary bandwidth, taking advantage of the non-uniform frequency of firing of the SOM processing elements (PEs). The complexity of the use of the SOM is also considered and addressed. After training several SOMs, the method was simulated with real data achieving compression ratios as high as 185.7:1, i.e. a bitrate of 862 bits-per-second-per-channel, assuming sampling at 20 kHz with 8 bits-per-sample (bps).

Original languageEnglish
Title of host publication2nd International IEEE EMBS Conference on Neural Engineering
Pages233-236
Number of pages4
Volume2005
DOIs
StatePublished - Dec 1 2005
Externally publishedYes
Event2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States
Duration: Mar 16 2005Mar 19 2005

Other

Other2nd International IEEE EMBS Conference on Neural Engineering, 2005
CountryUnited States
CityArlington, VA
Period3/16/053/19/05

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Paiva, A. R. C., Príncipe, J. C., & Sanchez, J. C. (2005). Compression of spike data using the self-organizing map. In 2nd International IEEE EMBS Conference on Neural Engineering (Vol. 2005, pp. 233-236). [1419599] https://doi.org/10.1109/CNE.2005.1419599