TY - CONF
T1 - Intelligent content-aware model-free low power evoked neural signal compression
T2 - 9th Pacific Rim Conference on Multimedia, PCM 2008
AU - Chen, Han-Chung
AU - Kao, Yu-Chieh Jill
AU - Chen, Liang-Gae
AU - Jaw, Fu-Shan
N1 - 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： [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, 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
PY - 2008
Y1 - 2008
N2 - 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)
AB - 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)
KW - Evoked signals
KW - Neural recording
KW - Neural signal compression
KW - Compression algorithms
KW - Compression rates
KW - Content-aware
KW - Full-waveforms
KW - Lossless compression
KW - Low Power
KW - Model free
KW - Multi-channel recording
KW - Neural recordings
KW - Neural signals
KW - Neuron activity
KW - Spike sorting algorithms
KW - Wave forms
KW - Data processing
KW - Image compression
KW - Multimedia systems
KW - Pulse code modulation
KW - Signal processing
KW - Data compression
U2 - 10.1007/978-3-540-89796-5_109
DO - 10.1007/978-3-540-89796-5_109
M3 - Paper
ER -