Intelligent content-aware model-free low power evoked neural signal compression: 9th Pacific Rim Conference on Multimedia, PCM 2008

Han-Chung Chen, Yu-Chieh Jill Kao, Liang-Gae Chen, Fu-Shan Jaw

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

Neural recording is an important key for us to realize the neuron activity, and multi-channel recording will be more and more crucial. However, nowadays research can only deal with spontaneous signals, which characteristics are far different from evoked signals. For evoked signals, we cannot just judge the spike at the front-end because evoked signals can't be distinguished by recent spike sorting algorithm. Then, we need to send "full" waveform for bio-researchers. Therefore, proper compression algorithm is unavoidable due to full waveform transmission creates huge data amount. We use signal processing skills to get the targets for lossless compression, SNR>25db, and compression rate (compressed data / origin data)
Original languageEnglish
Number of pages4
DOIs
Publication statusPublished - 2008
Externally publishedYes

Keywords

  • Evoked signals
  • Neural recording
  • Neural signal compression
  • Compression algorithms
  • Compression rates
  • Content-aware
  • Full-waveforms
  • Lossless compression
  • Low Power
  • Model free
  • Multi-channel recording
  • Neural recordings
  • Neural signals
  • Neuron activity
  • Spike sorting algorithms
  • Wave forms
  • Data processing
  • Image compression
  • Multimedia systems
  • Pulse code modulation
  • Signal processing
  • Data compression

Fingerprint Dive into the research topics of 'Intelligent content-aware model-free low power evoked neural signal compression: 9th Pacific Rim Conference on Multimedia, PCM 2008'. Together they form a unique fingerprint.

  • Cite this