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)

Abstract

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
DOIs
Publication statusPublished - 2009
Externally publishedYes

Fingerprint

Biomedical engineering
Bioinformatics
Image compression
Redundancy
Data reduction
Signal to noise ratio
Signal processing

Keywords

  • 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

Cite this

Optimal transform of multichannel evoked neural signals using a video compression algorithm : 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009. / Chung, Chen-Han; Chen, Liang-Gae; Kao, Yu-Chieh Jill; Jaw, Fu-Shan; Technology, IEEE Engineering in Medicine and Biology Society; Gordon Life Science Institute; Fudan University; Beijing University of Posts and Telecommunications; Beijing Institute of.

2009.

Research output: Contribution to conferencePaper

Chung, Chen-Han ; Chen, Liang-Gae ; Kao, Yu-Chieh Jill ; Jaw, Fu-Shan ; Technology, IEEE Engineering in Medicine and Biology Society; Gordon Life Science Institute; Fudan University; Beijing University of Posts and Telecommunications; Beijing Institute of. / Optimal transform of multichannel evoked neural signals using a video compression algorithm : 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009.
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abstract = "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. {\circledC}2009 IEEE.",
keywords = "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",
author = "Chen-Han Chung and Liang-Gae Chen and Kao, {Yu-Chieh Jill} and Fu-Shan Jaw and Technology, {IEEE Engineering in Medicine and Biology Society; Gordon Life Science Institute; Fudan University; Beijing University of Posts and Telecommunications; Beijing Institute of}",
note = "會議代碼: 79013 Export Date: 6 April 2016 通訊地址: Chung, C. H.; DSP/IC Design Lab, Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, 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, , June; Wise, K.D., Sodagar, A.M., Yao, Y., Gulari, M.N., Perlin, G.E., Najafi, K., Microelectrodes, Microelectronics, and Implantable Neural Microsystems (2008) Proceedings of the IEEE, 96 (7). , July; Perelman, Y., Ginosar, R., An Integrated System for Multichannel Neuronal Recording With Spike/LFP Separation, Integrated A/D Conversion and Threshold Detection (2007) Biomedical Engineering, IEEE Transactions on, 54 (1), pp. 130-137. , January; Casson, A.J., Rodriguez-Villegas, E., Data Reduction Techniques to Facilitate Wireless and Long term AEEG Epilepsy Monitoring (2007) IEEE EMBS Conference on Neural Engineering, , May 2-5; Kamamoto, Y., Harada, N., Moriya, T., (2008) Interchannel Dependency Analysis of Biomedical Signals For Efficient Lossless Compression By MPEG-4 ALS, , ICASSP; 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 (2007) IEEE Trans. On Circuits and Systems I, 54 (6), pp. 1266-1278. , June Pages; Chung, C.-H., Kao, Y.-C., Chen, L.-G., Jaw, F.-S., Intelligent Content-Aware Model-Free Low Power Evoked Neural Signal Compression (2008) LNCS, PCM 2008, pp. 898-901. , 5353, pp; Avila, A., Santoyo, R., Martinez, S.O., Hardware/Software Implementation of the EEG Signal Compression Module for an Ambulatory Monitoring Subsystem Devices, Circuits and Systems, Proceedings of the 6th International Caribbean Conference on April 2006, pp. 125-129; Buzsaki, G., Large-scale Recording of Neuronal Ensembles (2004) Nature Neuroscience, 7 (5). , May; Tsai, C.-Y., Chen, T.-C., Chen, L.-G., Low Power Entropy Coding Hardware Design for H.264/AVC Baseline Profile Encoder Multimedia and Expo, 2006 IEEE International Conference on July 2006, pp. 1941-1944",
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KW - Biomedical signal processing

KW - Multielectrode signals

KW - Video signal processing

KW - Data quality

KW - Motion Vectors

KW - Multi-channel

KW - Multi-electrode

KW - Neural signals

KW - Original signal

KW - Signal compression

KW - Video compression algorithms

KW - Video compression technology

KW - Video frame

KW - Video signal

KW - Wireless transmissions

KW - Bioinformatics

KW - Data compression ratio

KW - Image compression

KW - Signal processing

KW - Signal to noise ratio

KW - Video recording

KW - Data reduction

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