Optimal transform of multichannel evoked neural signals using a video compression algorithm: 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Chen-Han Chung, Liang-Gae Chen, Yu-Chieh Jill Kao, Fu-Shan Jaw, IEEE Engineering in Medicine and Biology Society; Gordon Life Science Institute; Fudan University; Beijing University of Posts and Telecommunications; Beijing Institute of Technology

Research output: Contribution to conferencePaper

1 Citation (Scopus)


One of the most important problems in the field of biomedical engineering is how to record a multichannel neural signal. This problem arises because recording produces a large amount of data that must be reduced to transfer it through wireless transmission, and data reduction must be made without compromising data quality. Video compression technology is very important in the field of signal processing, and there are many similarities between multichannel neural signals and video signals. Therefore, we use motion vectors (MVs) to reduce the redundancy between successive video frames and successive channels. We also test what transform for neural signal compression is best. Our novel signal compression method gives a signal-to-noise ratio (SNR) of 25 db and compresses data to 5% of the original signal. ©2009 IEEE.
Original languageEnglish
Publication statusPublished - 2009
Externally publishedYes



  • Biomedical signal processing
  • Multielectrode signals
  • Video signal processing
  • Data quality
  • Motion Vectors
  • Multi-channel
  • Multi-electrode
  • Neural signals
  • Original signal
  • Signal compression
  • Video compression algorithms
  • Video compression technology
  • Video frame
  • Video signal
  • Wireless transmissions
  • Bioinformatics
  • Data compression ratio
  • Image compression
  • Signal processing
  • Signal to noise ratio
  • Video recording
  • Data reduction

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