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

Fingerprint

Pulse code modulation
Sorting
Neurons
Signal processing

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

Cite this

Intelligent content-aware model-free low power evoked neural signal compression : 9th Pacific Rim Conference on Multimedia, PCM 2008. / Chen, Han-Chung; Kao, Yu-Chieh Jill; Chen, Liang-Gae; Jaw, Fu-Shan.

2008.

Research output: Contribution to conferencePaper

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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)",
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note = "會議代碼: 77578 Export Date: 6 April 2016 通訊地址: Chen, H. C.; DSP/IC Design Lab., Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, 10617, Taiwan; 電子郵件: penal@video.ee.ntu.edu.tw 參考文獻: Sodagar, A.M., Wise, K.D., Najafi, K., A Fully Integrated Mixed-Signal Neural Processor for Implantable Multichannel Cortical Recording (2007) IEEE Trans. On Biomedical Engineering, 54 (6); Perelman, Y., Ginosar, R., An Integrated System for Multichannel Neuronal Recording With Spike/LFP Separation (2007) Integrated A/D Conversion and Threshold Detection, 54 (1). , January; Casson, A.J., Rodriguez-Villegas, E., Data reduction techniques to facilitate wireless and long term AEEG epilepsy monitoring (2007) Neural Engineering, , May 2-5; Oweiss, K.G, Mason, A, Suhail, Y, Thomson, K, Kamboh, A, A Scalable Wavelet Transform VLSI Architecture for Real-Time Signal Processing in Mutichannel Cortical Implants. IEEE Trans. On Circuits and SystemsKamamoto, Y., Harada, N., Moriya, T., Interchannel Dependency Analysis of Biomedical Signals for Efficient Lossless Compression by MPEG-4 ALS (2008) ICASSP 2008; Kamboh, A.M., Raetz, M., Oweiss, K.G., Mason, A., (2007) Area-Power Efficient VLSI Implementation of Multichannel DWT for Data Compression in Implantable Neuroprosthetics, , IEEE Trans. On Biomedical Circuit and Systems June; Narasimhan, S., Tabib-Azar, M., Chiel1, H.J., Bhunia, S.: Neural Data Compression with Wavelet Transform: A Vocabulary Based Approach. In: IEEE EMBS Conference on Neural Engineering, May2-5 (2007)UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-70350644883&partnerID=40&md5=cb0ecd87f6f2bb78d58972a2e1427540",
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