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Title: Microsystem Design and Signal Processing Towards Closed-Loop Bi-Directional Neural Interfaces (Microsysteemontwerp en signaalverwerking voor geslotenlus bidirectionele neurale interfaces)
Other Titles: Microsystem Design and Signal Processing Towards Closed-Loop Bi-Directional Neural Interfaces
Authors: Nguyen, Thi Kim Thoa
Issue Date: 24-Oct-2013
Abstract: Bi-directional interfacing electronics with living electrogenic cells has been used widely in neuroscience, mainly as research tool for studying brain mechanisms, the development of neuroprostheses, and medical therapies for several neurological disorders. Challenges for this interfacing include minimizing the interference of extracellular stimulation on the recorded neural signals, increasing the stimulation selectivity, integrating recording and/or stimulation capabilities in closed-loop systems, and fast online data analysis. The main objective of this thesis was to design and implement a system that enables the simultaneous stimulation and recording neural activity in vivo from multiple electrode sites, and that allows ultimately closed-loop operation, namely neural modulation based on activity data. The realized system should be miniaturized (hence microsystem) and be suitable for in vivo rodent experiments.Two different stimulation-recording systems have been implemented in the thesis. The first single-channel prototype explores the stimulation effects on recording capabilities in the case of planar silicon neural probes. We have been able to define the voltage thresholds for eliciting neural responses experimentally. Additionally, some configurations have revealed minimal interference with stimulation artifacts, allowing the observation of evoked action potentials right after the stimulation period.A novel concept of mixed-signal architecture for removing stimulus-induced electrical artifacts and allowing recordings during the stimulation phase, was further integrated in the prototype. The template subtraction algorithm has been applied in an analog-digital context, i.e. generating the template digitally after the training phase, and subtracting it from the recording signals in the stimulation phase. The topology has successfully been validated in simulations, in in vitro measurements, and, with promising results, in vivo. This system advances the possibilities for exploring brain mechanisms, particularly under multi-electrode stimulations.Optical stimulation, in which (visible) light pulses are delivered to brain tissue to elicit or inhibit neural activity, has recently opened new perspectives in brain research, allowing much higher spatial resolution and selective stimulation. In a second prototype we realized a hybrid neural interface combining optical stimulation with electrical recording capabilities in a closed-loop fashion with fast spike sorting. This system allows acquiring and classifying the data, and subsequently triggering the light pulses online. The fast operation and high data-rate signal processing are essential in improving the precision, selectivity, and effectiveness of stimulation.
Table of Contents: Acknowledgments i
Contents iii
Abstract vii
Samenvatting ix
List of Abbreviations xi
List of Symbols xiii
List of Figures xv
List of Tables xxv
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Aims and structure of this thesis . . . . . . . . . . . . . . . . . . . . . . 3
2 Neural electronic systems 9
2.1 Nature of neural signals . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Electrode array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 General characteristics . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 Microwire arrays . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.3 MEMS-based neural probes . . . . . . . . . . . . . . . . . . . . 19
2.3 Neural recording circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Neural signal processing . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Device controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6 Operating protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.7 Stimulus-induced artifacts . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.8 Remaining challenges of neural electronic systems . . . . . . . . . . . . . 36
3 Electrical stimulation using silicon neural probes 39
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 System design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2.1 Hardware implementation . . . . . . . . . . . . . . . . . . . . . 40
3.2.2 Software implementation . . . . . . . . . . . . . . . . . . . . . . 41
3.3 Experimental methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.1 Cardiac cell culture . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.2 Calcium imaging . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.3 Animal surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.4 Electrode arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.5 Experimental protocol . . . . . . . . . . . . . . . . . . . . . . . 47
3.4 Measurement results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.4.1 Characteristics of the system . . . . . . . . . . . . . . . . . . . . 48
3.4.2 Electrical stimulation of cardiomyocytes . . . . . . . . . . . . . . 48
3.4.3 Electrical stimulation in the hippocampus . . . . . . . . . . . . . 49
3.4.4 Stimulation artifacts . . . . . . . . . . . . . . . . . . . . . . . . 53
3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4 Mixed-signal template-based reduction scheme for stimulus artifact removal in electrical stimulation 59
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2 Mixed-signal topology for artifact removal . . . . . . . . . . . . . . . . . 62
4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.1 Lumped circuit model for PA emulation . . . . . . . . . . . . . . 64
4.3.2 Evaluation of the proposed artifact removal topology . . . . . . . 65
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.1 Validity of the lumped circuit model . . . . . . . . . . . . . . . . 66
4.4.2 Impact of parameters of the artifact removal setup . . . . . . . . . 68
4.4.3 Evaluation of the artifact removal algorithm in vitro . . . . . . . . 70
4.4.4 Initial evaluation of the artifact removal algorithm in vivo . . . . . 75
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5 A 32-channel low-noise recording system with online spike sorting 79
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2.1 Amplifier board . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2.2 LED driver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.2.3 Controlling software . . . . . . . . . . . . . . . . . . . . . . . . 84
5.3 Validation methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.3.1 Amplifier board characterization . . . . . . . . . . . . . . . . . . 88
5.3.2 Animal surgeries and recordings . . . . . . . . . . . . . . . . . . 89
5.3.3 Signal processing . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.4.1 Performance of the 32-channel low-noise amplifier board . . . . . 90
5.4.2 Multiplexed, large-scale data acquisition . . . . . . . . . . . . . . 95
5.4.3 Signal analysis of recorded data . . . . . . . . . . . . . . . . . . 95
5.4.4 Online acquisition and spike sorting . . . . . . . . . . . . . . . . 101
5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6 Conclusions and suggestions for future work 107
6.1 General conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.2 Suggestions for future work . . . . . . . . . . . . . . . . . . . . . . . . . 108
A Using Matlab code in the LabVIEW environment 111
A.1 System requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
A.2 Conversion procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
A.2.1 Generating a Matlab DLL . . . . . . . . . . . . . . . . . . . . . 111
A.2.2 Writing wrapper codes . . . . . . . . . . . . . . . . . . . . . . . 113
A.2.3 Importing into LabVIEW . . . . . . . . . . . . . . . . . . . . . . 115
References 117
Publications 141
Resumé 143
ISBN: 978-94-6018-739-1
Publication status: published
KU Leuven publication type: TH
Appears in Collections:ESAT - MICAS, Microelectronics and Sensors
Electrical Engineering - miscellaneous
Solid State Physics and Magnetism Section
Physics and Astronomy - miscellaneous

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