Wireless transmission of neural signals using entropy and mutual information compression

Stefan Craciun, David Cheney, Karl Gugel, Justin C. Sanchez, Jose C. Principe

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Two of the most critical tasks when designing a portable wireless neural recording system are to limit power consumption and to efficiently use the limited bandwidth. It is known that for most wireless devices the majority of power is consumed by the wireless transmitter and it often represents the bottleneck of the overall design. This paper compares two compression techniques that take advantage of the sparseness of the neural spikes in neural recordings using an information theoretic formalism to enhance the well-established vector quantization (VQ) algorithm. The two discriminative VQ algorithms are applied to neuronal recordings proving their ability to accurately reconstruct action potential (AP) regions of the neuronal signal while compressing background activity without using thresholds. The two operational modes presented offer distinct characteristics to lossy compression. The first approach requires no preprocessing or prior knowledge of the signal while the second requires a training set of spikes to obtain AP templates. The compression algorithms are implemented on an on-board digital signal processor (DSP) and results show that power consumption is decreased while the bandwidth is more efficiently utilized. The compression algorithms have been tested in real time on a hardware platform (PICO DSP ) enhanced with the DSP which runs the algorithm before sending the compressed data to a wireless transmitter. The compression ratios obtained range from 70:1 and 40:1 depending on the signal to noise ratio (SNR) of the input signal. The spike sorting accuracy in the reconstructed data is 95% compatible to the original neural data.

Original languageEnglish (US)
Article number5560866
Pages (from-to)35-44
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume19
Issue number1
DOIs
StatePublished - Feb 1 2011

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Keywords

  • Compression
  • neural signals
  • vector quantization
  • wireless brain machine interface

ASJC Scopus subject areas

  • Neuroscience(all)
  • Computer Science Applications
  • Biomedical Engineering
  • Medicine(all)

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