Compressive Sensing system for recording of ECoG signals in-vivo

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fact that in order to integrated the neural input signals in a range of frequencies till 10 kHz, thearea needed to implement the resistor and the capacitors is too large to be a design whichcould spread out all over all the paths are needed in all the channels of the chip array. Due tothis, switched capacitor architecture has been deployed. By considering an implementation thatmakes depend the magnitude of the components on a clock frequency, area constraints arerelaxed, so that has revealed as a practicable design to implement an analog design of the CSlikedacquisition system.7.2. Next stepsDuring the accomplishment of this project, Compressive Sensing-based systems has revealedas a promising compression method to be applied in biological signals which satisfy sparsityconditions.As it is shown along the previous chapters, Compressive Sensing systems are becoming moreand more relevant in order to efficiently implement high information rate acquisition circuits.However, it is a new methodology, and many design parameters have not been settled yet, sothe project has turned into the first approximation to an entire CS-platform based on analogimplementation for EPFL and IMEC.After the work which is circumscribed to the scope of the master thesis, a real SC-based mixingand integration path will be implemented in IMEC based on real components. In the same way,LNA and ADC will be integrated along with the mixer, integrator and random generator whichhave been individually designed in order to attain a complete analog path for futuremultichannel implementations.For the other hand, in order to be able to apply this array-based neural acquisition system torecord as many different neural signals as possible, it is recommended to study the bestsparsifying basis for each of the characteristic neural signals. Therefore, neural sciences couldbenefit of clearer recordings in order to better understand the illness which are related with thebrain. Similarly, other reconstruction methods, as [37], could be applied and compare with theones have been used in this work, in order to verify which one gives the best results.Finally, the new CS-application which has been presented [31] has to be deeper studied inorder to specify its better applicability in neural recording.72
AppendixAppendix AConvexOptimizationIterative GreedyAlgorithmsOthersBasis Pursuit (BP)Basis Pursuit De-Noising (BPDN)Conjugate Gradients Pursuit (CGP)Preconditioned Conjugate Gradient Pursuit (PCGP)Matching Pursuit (MP)Orthogonal Matching Pursuit (OMP)Regularised Orthogonal Matching Pursuit (ROMP)Compressive Sampling Matching Pursuit (CoSaMP)Iterative Hard Thresholding (IHT)Iterative Shrinkage/Thresholding (IST)Stagewise Orthogonal Matching Pursuit (StOMP)Stagewise Weak Orthogonal Matching Pursuit (SWOMP)Stagewise Conjugate Gradient Pursuit (StCGP)Weak Conjugate Gradient Pursuit (SWCGP)Stagewise and Reduced Conjugate Gradient (RCG)Subspace Pursuit (SP)Iterative Reweighted l 1 -norm Minimization Algorithm(IRWL1) + Hidden Markov Tree (HMT) modelGradient Projection (GP)Gradient Projection for Sparse Reconstruction (GPSR)Least-Angle Regression (LARS)Least Squares Quadratic Regression (LSQR)Least Absolute Shrinkage and Selection Operator(LASSO)Bound Constrained Quadratic Programming (BCQP)Interior Point (IP)Table A.1. Main Reconstruction Methods.73
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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
Page 19 and 20:
E.2. Signal Digital Conversion 84Gl
Page 21 and 22: Figure 6.2.1.2. Input signal Spectr
Page 23: Index of TablesTable 2.1.1. Summary
Page 26 and 27: Figure 1.1. Energy a power costs fo
Page 29 and 30: 2. State of the art2.1 Neural Signa
Page 31 and 32: patterns in response to visual targ
Page 33 and 34: 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: 7. Conclusions7.1. RemarksAs it is
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 and 88: GlossaryADC: Analog-to-Digital Conv
Page 89 and 90: References[1] D. Gangopadhyay et al
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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