Compressive Sensing system for recording of ECoG signals in-vivo

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GlossaryADC: Analog-to-Digital ConverterAP: Action potentialBAN: Body Area NetworkBCI: Brain-Computer InterfaceBCQP: Bound Constrained Quadratic ProgrammingBP: Basis PursuitBPDN : Basis Pursuit De-NoisingBW : BandwithdCGP : Conjugate Gradients PursuitCS : Compressive Sensing or Compressive SamplingCoSaMP: Compressive Sampling Matching PursuitECG: ElectrocardiogramECoG: ElectrocorticogramEEG: EectroencefalogramGP: Gradient ProjectionGPSR: Gradient Projection for Sparse Reconstruction (GPSR)HMT: Hidden Markov TreeIHT: Iterative Hard ThresholdingIP: Interior PointIRWL1: Iterative Reweighted l 1-norm Minimization AlgorithmIST: Iterative Shrinkage/ThresholdingLARS: Least-Angle RegressionLASSO: Least Absolute Shrinkage and Selection OperatorLFSR: Linear Feedback Shift RegisterLNA: Low Noise AmplifierLPF: Low Pass Filter87
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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
Page 35 and 36: 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 88 and 89: LSQR: Least Squares Quadratic Regre
Page 90 and 91: [17] L. R. Hochberg et al., Neurona
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Compressive Sensing system for recording of ECoG signals in-vivo

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