TY - CONF
T1 - Optimal transform of multichannel evoked neural signals using a video compression algorithm
T2 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
AU - Chung, Chen-Han
AU - Chen, Liang-Gae
AU - Kao, Yu-Chieh Jill
AU - Jaw, Fu-Shan
AU - Technology, IEEE Engineering in Medicine and Biology Society; Gordon Life Science Institute; Fudan University; Beijing University of Posts and Telecommunications; Beijing Institute of
N1 - 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： [email protected]
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
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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
U2 - 10.1109/ICBBE.2009.5163149
DO - 10.1109/ICBBE.2009.5163149
M3 - Paper
ER -