Compressive Sensing system for recording of ECoG signals in-vivo

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LSQR: Least Squares Quadratic RegressionMDAC: Multiplying Digital-to-Analog ConverterMP: Matching Pursuit MPMSB: More Significant BitOMP: Orthogonal Matching PursuitOTA: Operational Transconductance AmplifierPCGP: Preconditioned Conjugate Gradient PursuitPRBS : Pseudorandom Binary SequencePSD: Power Spectra DensityRAmP: Restricted Amplification PropertyRC: Random ConvolutionRFID: Radio Frequency IdentificationRIP: RestrictedRDG: Reduced Conjugate GradientROMP: Regularised Orthogonal Matching PursuitSC: Switched CapacitorSCS: Spatial Compressive SensingSNR: Signal-to-Noise RatioStCGP: Stagewise Conjugate Gradient PursuitStOMP: Stagewise Orthogonal Matching PursuitSP: Subspace PursuitSWOMP: Stagewise Weak Orthogonal Matching PursuitTRNG: True Random Number GenerationWCGP: Weak Conjugate Gradient Pursuit88
References[1] D. Gangopadhyay et al. System Considerations for the Compressive Sampling of EEG and ECoG Biosignals.IEEE, pp.129-132, 2011.[2] R.F: Yazicioglu et al. Ultra-low-Power Wearable Biopotential Sensor Nodes. 31 th Annual InternationalConference of the IEEE, pp. 3205-3208, 2009.[3] F. Chen et al. Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for DataCompression in Wireless Sensors. IEEE Journal of Solid-State Circuits, vol.47, no.3, pp .744-756, 2012.[4] W. Wattanapanitch et al. An energy-efficient micropower neural recording amplifier, IEEE Trans.Biomed. Circuits Syst., vol. 1, no. 2, pp. 136–147, 2007.[5] B. Murmann, A/D converter trends: Power dissipation, scaling and digitally assisted architectures, Proc.2008 IEEE Custom Integrated Circuits Conf., vol. 1, pp. 105–112, 2008.[6] N. Verma et al. A micro-power EEG acquisition SoC with integrated feature extraction processor for achronic seizure detection system, IEEE J. Solid-State Circuits, vol. 45, no. 4, pp. 804–816, 2010.[7] V. Karkare, et al., A 130- W, 64-channel spike-sorting DSP chip, Proc. IEEE Asian Solid-State CircuitsConf., vol. 1, pp. 289–292, 2009.[8] J. L. Bohorquez et al., A 350 W CMOS MSK transmitter and 400 W OOK super-regenerative receiverfor medical implant communications, Symp. VLSI Circuits Dig. Tech. Papers, pp. 32–33, 2008.[9] C.T. Nordhausen et al., Single unit recording capabilities of a 100 microelectrode array, Brain Res., vol.726, pp. 129-140, 1996.[10] B. Gosselin, Recent Advanced in Neural Recording Microsystems, Sensors(11), pp. 4572-4597, 2011.[11] V. B. MountCastle, Modality and topography properties of single neurons of cat’s somatic sensorycortex, J. Neurophysiol. no.20, pp. 408-434, 1957.[12] D. H. Hubel et al., Receptive Fields of Single Neurones in the cat’s striade cortex, J. Physiol., no.148,pp. 574-591, 1959.[13] A. P. Georgopoulos et al., Neural population coding of movement direction, Science, no.233, pp.1416-1419, 1986.[14] A. B. Schwartz et al., Primate motor cortex and free arm movments to visual targets in threedimensionalspace, J. Neurophysiol., no.8, pp. 2913-2927, 1988.[15] D. M. Taylor et al., Direct Cortical Control of 3D neuroprosthetic devices, Sciences, no 296, pp.1829-1832, 2002.[16] J. Wessberg et al., Real-time prediction of hand trajectory by ensembles of cortical neurons inprimates, Nature, no. 408, pp. 361-365, 2000.89
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August, 31 th , 2012Compressive Sen
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The present project has been submit
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AbstractThe project “Compressive
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RiassuntoIl progetto “Compressive
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IndexAcknowledgments 7Abstract 9Gan
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E.2. Signal Digital Conversion 84Gl
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Figure 6.2.1.2. Input signal Spectr
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Index of TablesTable 2.1.1. Summary
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Figure 1.1. Energy a power costs fo
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2. State of the art2.1 Neural Signa
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patterns in response to visual targ
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2.4. Compressive Sensing2.4.1. Comp
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2.4.2. Sparsifying basesSparsifying
Page 37 and 38: It is worth mentioning that compres
Page 39 and 40: y specially considering the integra
Page 41 and 42: Figure 4.1.1.1. LSFR parallel (top)
Page 43 and 44: 4.1.3. Serial Implementation with t
Page 45: 4.1.4. Randomness CheckingIn order
Page 48 and 49: een designed independently as it is
Page 50 and 51: time, which has been approximated a
Page 52 and 53: Figure 5.2.2. Original and reconstr
Page 54 and 55: 6. Analog Path Design6.1. Design Di
Page 56 and 57: The spectra of the input signal hav
Page 58 and 59: Figure 6.2.1.4. LASSO reconstructio
Page 60 and 61: Figure 6.2.2.3. Compressed signals
Page 62 and 63: In this way, the pole frequency is
Page 64 and 65: A can be floating when the mixing s
Page 66 and 67: Figure 6.2.4.4. BPDN method reconst
Page 68 and 69: Figure 6.2.5.3. BPDN method reconst
Page 71 and 72: 7. Conclusions7.1. RemarksAs it is
Page 73: AppendixAppendix AConvexOptimizatio
Page 76 and 77: The total power cost for the digita
Page 78 and 79: Figure B.2.2.1. Block diagram for a
Page 80 and 81: 80Figure B.2.3.2. Binary-weighted S
Page 82 and 83: The main model is shown in Fig.C.1.
Page 84 and 85: Appendix EE.1. AmplificationThe sma
Page 87: GlossaryADC: Analog-to-Digital Conv
Page 91: [35] M.F. Duarte et. al., Recovery
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Compressive Sensing system for recording of ECoG signals in-vivo

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