Compressive Sensing system for recording of ECoG signals in-vivo

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IndexAcknowledgments 7Abstract 9Gannt’s Chart 151. Introduction: Framework for Compressive Sensing 252. State of the art 292.1. Neural Signals: EEG, ECoG and AP 292.2. Neural Signal Acquisition Systems On-Chip 302.3. Data Compression Methods 322.4. Compressive Sensing 332.4.1. Compressive sensing in a nutshell 332.4.2. Sparsifying bases 352.5. Reconstruction Methods 353. ECoG and AP Compressive Sensing System Design 373.1. Power Consumption Analysis 373.2. Single and Multi Channel Approach 384. Random Matrix Generation 394.1. Digital Implementation: Pseudo Random Binary Sequence (PRBS) 394.1.1. Basics of PRB: Serial and Parallel Implementation 394.1.2. Flips-Flops: Power and Area Analysis 424.1.3. Serial Implementation with two PRBS 434.1.4. Randomness Checking 455. System Level Design 475.1. Matlab and Simulink Models 475.2. Multi-Channel Implementation of Compressive Sensing 505.3. Reconstruction Method Application 5217
Page 1: August, 31 th , 2012Compressive Sen
Page 5: The present project has been submit
Page 9: AbstractThe project “Compressive
Page 13: RiassuntoIl progetto “Compressive
Page 18 and 19: 5.3.1. Basis Pursuit Denoising Meth
Page 20 and 21: Index of ImagesFigure 1.1. Energy a
Page 22 and 23: Figure B.2.3.1. Switched Capacitor
Page 25 and 26: 1. Introduction: Framework for Comp
Page 27: The final target is to reliably rec
Page 30 and 31: Signal Amplitude BandwidthAP 50 to
Page 32 and 33: each channel must have a shared or
Page 34 and 35: In order to have a clear understand
Page 36 and 37: Figure 2.5.1. l 1-minimization appr
Page 38 and 39: If calculations in Appendix B (Eq.2
Page 40 and 41: By means of the serial implementati
Page 42 and 43: 4.1.2. Flip-Flops: Power and Area A
Page 44 and 45: and interleave these outputs with t
Page 47 and 48: 5. System Level DesignCompressive S
Page 49 and 50: In this way the multipath combinati
Page 51 and 52: frequency when all the electrodes a
Page 53 and 54: 5.3.2. Least Absolute Shrinkage and
Page 55 and 56: 6.2. Mixing and IntegrationFirst of
Page 57 and 58: considered switch is almost ideal,
Page 59 and 60: Figure 6.2.2.1. Mixer and ideal act
Page 61 and 62: Figure 6.2.2.5. BPDN method reconst
Page 63 and 64: Figure 6.2.3.3. LASSO method recons
Page 65 and 66: a virtual ground. The top plate cap
Page 67 and 68:
Figure 6.2.5.1. Compressed signal c
Page 69:
Topology LASSO (dB) BPDN (dB)Matlab
Page 72 and 73:
fact that in order to integrated th
Page 75 and 76:
Appendix BB.1. Digital Implementati
Page 77 and 78:
successive integration windows in o
Page 79 and 80:
approximation is discussed by takin
Page 81 and 82:
Appendix CC.1. Analog Implementatio
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Appendix DOutput 4FF-PRBS Output 5F
Page 85:
Figure E.2.1. Several techniques to
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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