Introduction
Parkinson’s disease (PD) is the second most common neurodegenerative disorder worldwide, leading to a wide range of motor and non-motor symptoms that significantly affect quality of life. Motor symptoms include, among others, rigidity, slowness of movement (bradykinesia), tremor and gait/balance problems. As the disease progresses, response to dopaminergic medication may become increasingly variable, resulting in motor fluctuations, including OFF periods and dyskinesia—involuntary hyperkinetic movements as a side effect of high dopaminergic medication state.

For these motor response fluctuations, continuous Deep Brain Stimulation (cDBS) is an effective treatment that delivers continuous high-frequency electrical stimulation via implanted electrodes to brain nuclei in the basal ganglia. However, continuous DBS (cDBS) has several limitations in some patients, including stimulation-induced side effects, such as dysarthria or balance problems, and persisting symptom fluctuations related to concurrent medication treatment.

Adaptive Deep Brain Stimulation (aDBS) was designed to overcome these limitations by automatically adjusting the stimulation parameters according to a biomarker that correlates with symptom severity to provide personalized stimulation. A commonly used biomarker is activity in the β-frequency band in the local field potentials (LFPs), local compound neuronal activity measured via the implanted electrodes. Despite promising results in early studies, no standardized workflow for aDBS has been defined yet, while the process is inherently complex with multiple different parameters that should be selected and optimized. Therefore, the primary aim of this thesis was to develop a workflow algorithm for aDBS.

According to a previously reported systematic review, an initial protocol was proposed consisting of four phases: (1) pre-aDBS patient selection, (2) biomarker selection, (3) initial aDBS setup and (4) iterative optimization. In the optimization phase, a decision tree was proposed to decide on which parameter to adjust based on clinical symptoms and results of home monitoring analysis.

Methods
In a retrospective case series, the setup process of all patients treated with aDBS was described and compared to the initial protocol. PD symptoms and home-monitoring data from the last clinical visit before aDBS initiation and from all subsequent visits during aDBS optimization were retrieved from the EHR and from the programming tablets. For the optimization phase, all adjustments were grouped by clinical state and adherence to protocol. Furthermore, an existing Toolbox was extended to allow for visualization of home monitoring data to aid in parameter adjustment decision-making.

Results
Eleven patients were assessed for aDBS, of whom nine patients eventually initiated aDBS. Initial setup varied greatly among patients and did not include biomarker selection OFF-medication. Optimization took median 2 (range: 1–13) sessions. Four patients discontinued aDBS due to lack of clinical benefit after a brief optimization period. Retrospectively, 13 parameter adjustments were in accordance with the protocol, 19 deviated from the protocol, and 14 were not covered by the protocol. Descriptively, protocol adherence led more often to beneficial outcomes than protocol deviations (46% vs 21%). Within these case series, different phenomena were identified. These included changes in baseline biomarker value, resulting in changes in overall stimulation. Moreover, not all patients showed a relationship between biomarker and events or medication intake, but still benefited from aDBS.

Conclusion
In this work, an extended protocol for aDBS setup and optimization was proposed to account for real-world scenarios encountered during clinical practice. This protocol provides a basis for further refinement and validation of standardized aDBS workflows. ... Read more

Real-time simulation of biophysically accurate neuron models is essential for advanced neuromorphic computing and neuroprosthetic applications. The Hodgkin–Huxley (HH) model provides high biological fidelity but is computationally expensive, making large-scale FPGA implementations challenging. Existing hardware implementations typically trade biological accuracy for scalability or require substantial hardware resources to achieve real-time performance.

This thesis presents BrainLUT, a resource-efficient FPGA architecture that implements the nonlinear ion-channel computations of the HH model using neural network-generated lookup tables. The proposed approach leverages Quantisation-Aware Training (QAT) and adapted NeuraLUT and ReducedLUT methodologies to approximate complex HH functions with compact logic-based lookup tables, eliminating the need for expensive arithmetic evaluation while preserving model fidelity. Multiple hardware architectures and optimisation techniques, including structural decomposition, lookup table compression, derivative-based prediction, neural-network-based LUT generation, and reduced numerical precision, are explored to identify an optimal design.

The resulting proof-of-concept implementation simulates 1,250 Hodgkin–Huxley neurons in real time on a Xilinx Artix-7 XC7A200T FPGA operating at 125 MHz, utilising only 6,307 LUTs (4.71%), 15 BRAMs (3.01%), and 4 DSP blocks (0.54%). Although not achieving the highest absolute neuron count reported in the literature, the proposed architecture demonstrates substantially improved resource efficiency and neuron density compared with existing FPGA implementations of the Hodgkin–Huxley model. These results establish BrainLUT as a scalable and resource-efficient foundation for future large-scale, biophysically accurate neuromorphic systems. ... Read more

Bidirectional brain-computer interfaces (BCIs) are essential for next-generation neuroprosthetics as they enable both neural stimulation and the simultaneous recording of neural activity. However, a significant challenge in these systems is the presence of stimulation artifacts: large voltage transients that saturate sensitive recording electronics and mask critical neural signals.

This thesis investigates the fundamental causes and characterization of stimulation artifacts in the context of visual prostheses through the lens of an electrode-tissue interface (ETI) model. A comprehensive review of existing artifact reduction techniques is provided, assessing their efficacy and trade-offs regarding implementation complexity and signal integrity. The review serves as a framework for the introduction of two novel artifact reduction techniques that target the residual artifact to permit recording of post-stimulation action potentials (APs).

A residual artifact reduction technique is proposed that replicates and cancels the artifact at the input of the recording analog front-end (AFE). Replication of the artifact is performed through the voltage decay of an RC network with a characteristic time constant equal to the ETI model. Although the concept of this technique is completely novel, the system implementation is impractical due to technological limitations.

A rapid charge reset technique for fast settling of artifact transients is also proposed and implemented with an AFE designed in the TSMC 40 nm CMOS technology node. The system features an AC-coupled low-noise boxcar sampler (LNBS) followed by a switched-capacitor low-pass filter (SC-LPF). The maximum artifact considered for the application of this work can be reduced from 725 mVpp to 2.1 mVpp—a reduction of approximately 50.8 dB—within a recovery time of 50 μs while maintaining a low power consumption of approximately 205 nW per channel and an input-referred integrated noise performance of 7.6 μVrms over the system bandwidth of 300 Hz - 5 kHz. Further reduction of the residual artifact requires minimization of the residual charge across the ETI that is present post-stimulation. ... Read more

Neurons are fundamental to cognitive and motor functions, relying on intricate electrical and chemical signaling. However, neurological diseases such as Parkinson’s, Alzheimer’s, and Amyotrophic Lateral Sclerosis impair neural function, posing a growing challenge due to aging populations and limited regenerative capacity of the nervous system. Advances in induced pluripotent stem cells (iPSCs) have enabled human-derived neuronal models for disease study, while neural interfaces, particularly microelectrode arrays (MEAs), facilitate electrophysiological investigation both in vivo and in vitro.

This thesis explores the use of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), a conductive polymer with mixed ionic-electronic conductivity, as a superior neural interface for in vitro neuronal cultures. The study addresses three key objectives: (1) elucidating the electrochemical mechanisms underlying PEDOT:PSS’s performance, (2) validating its biocompatibility and functionality in recording neuronal activity, and (3) establishing protocols for neuronal differentiation and maturation on PEDOT:PSS substrates.

First, a scientific literature search was performed to understand the current standing of PEDOT:PSS as a neuronal interface, exploring the different applications and approaches scientific peers have established, and understanding the working mechanisms of the conduction behind their work. This was complemented with the practical experience with PEDOT:PSS, showcasing its biocompatibility and methods to improve conductivity.

Secondly, neuronal recordings in vitro were made to assess the performance of a custom-built PEDOT:PSS-based MEA and the meaning behind the electrophysiological recordings. Data acquisition, pre-processing, and analysis are discussed to understand the results obtained. Key findings include the performance success of the MEA, while also explaining the shortcomings of the implemented processing algorithms.

Lastly, a motor neuron differentiation protocol from iPSCs was established to further investigate the role of PEDOT:PSS in such context for later studies. The success of the protocol was assessed by morphological, functional, and immunostaining assays.

Future directions include optimizing conductivity through acid treatments, integrating PEDOT:PSS into motor neuron maturation protocols, and exploring electrical stimulation and 3D culture systems. This work contributes to the development of advanced bioelectronic tools for neuronal models and engineering.
... Read more

Organic electrochemical transistors (OECTs) are a promising technology in the field of bioelectronics. They bridge electronics and biology through their ability to operate in aqueous environments. Their ability to transduce biological signals into electronic ones makes them a preferred choice for bioelectronic applications, such as biosensing and neural interfaces. While p-type materials like PEDOT:PSS have become the benchmark for the OECT performance, the development of stable, high-mobility n-type channel materials remains a major challenge. These materials are often limited by poor air stability, low electron mobility, and restricted ionic transport. In this work, a newly developed semiconducting n-type polymer with cleavable side chains is investigated. The polymer, YYBC, undergoes a post-deposition thermal treatment, which leads to a porous, more hydrophilic polymer, YYAC. This cleaved version aims to improve the performance of an n-type OECT.
The research followed three main objectives. OECT devices were first fabricated in a cleanroom environment, then a measurement setup for steady-state and transient characterization was developed, and lastly, the new n-type polymer was evaluated. For device fabrication, standard microfabrication methods were employed. For the measurement setup, programmable source-measure units, a function generator, and an oscilloscope were controlled through MATLAB for data acquisition and analysis. Lastly, the performance of the n-type polymer was assessed using electrochemical and electrical characterization. Parameters such as volumetric capacitance, transconductance, and time response were evaluated.
Results of this work show that the post-deposition side-chain removal significantly improves the polymer’s electrochemical behaviour. The findings show improved performance metrics, indicating that the new n-type polymer can be a promising material for future OECT applications. ... Read more

Automated diagnosis of epilepsy for differentiating epileptic EEGs without Interictal Epileptic Discharges (IEDs) from normal EEGs remains a critical challenge in clinical settings.
Current state-of-the-art methods use algorithms that can effectively detect epilepsy seizures which improves the current treatment methods for people suffer from epilepsy. Electroencephalograms (EEGs) analyzed by neurologists which are not able to meet the criteria are further looked into to obtain an efficient classification. This thesis presents an automated epilepsy diagnosis approach using a multi-algorithmic feature extraction pipeline. The final models include the development of a robust multi-processing feature extraction pipeline, the application of advanced machine learning / deep learning algorithms, and the validation of the proposed methods on comprehensive EEG datasets. The results, achieved using an XGBoost classifier with leave-one-subject-out (LOSO) cross-validation, demonstrate comparable performance to state-of-the-art epilepsy detectors. The study emphasizes the detection of epilepsy without IEDs, optimizing models through nested cross-validation, and evaluates their performance on the Temple University Hospital (TUH) and Erasmus Medical Center (EMC) Rotterdam datasets. ... Read more

The device that is designed and described in this thesis aims to non-invasively stimulate neural activity in an earthworm at multiple, specific locations. This is done by inducing an electric field from two inductors with a specific electric field strength in mind. The response of the earthworm is recorded and amplified to enable analysis of this signal. Initially, requirements are set that need to be met by this device. From these requirements a design process was started and simulations were made. After this, a device was created on which the requirements could be evaluated. Due to the physical trials not being completed the requirements could not be fully verified. ... Read more

This thesis aims to evaluate the viability of using a Field Programmable Gate Array (FPGA) in a neural Implantable Medical Device (IMD). The primary motivation for incorporating FPGAs is their potential to support future functionalities, such as running neural networks for medical condition analysis but also advanced cybersecurity algorithms. These algorithms are compute-intensive, and accelerators like FPGAs offer advantages in terms of speed and efficiency. To assess the effectiveness of such a device, state-of-the-art Microcontroller Units (MCUs) commonly used in similar applications are employed as a reference. Comparisons are made between MCU-only platforms and hybrid platforms integrating both an MCU and an FPGA. Feasibility analysis considers operational modes and use cases based on various realistic scenarios. The results show mixed outcomes across scenarios. Under a 100% duty cycle, the FPGA demonstrates higher efficiency, consuming less active power than the MCU. However, at lower duty cycles, MCUs are generally more effective on average. The use of an FPGA becomes practical when power-gating techniques are applied to minimize power consumption during inactive periods. ... Read more

To achieve greater specificity in neurostimulation, bidirectional neural interfaces are required to verify the recorded neural response after stimulation. The specific neural interface targeted in this thesis is the epiretinal implant. Due to the heterogeneity of the retinal ganglion cells (RGCs), high-fidelity vision is only possible when all the types of RGCs near the neurostimulator are mapped. This necessitates the bidirectionality of the implant and poses significant difficulties, as large stimulation artifacts obscure the small neural response that needs to be recorded. Furthermore, in order to cover a large area of the retina, a channel count in the order of 10⁴ will be required. Scaling existing neurostimulators would lead to chips that are >30mm², which is too large.

The aim of this thesis is therefore to design a neurostimulator for the epiretinal implant that is capable of implementing artifact-reducing algorithms, and is smaller than the current state-of-the-art. The proposed system makes use of a mismatch-based digital-to-analog converter (DAC), and has been optimized for an output range of 0-6 μA at an effective resolution of 8-bits. Furthermore, in order to decrease the amount of stimulation units required, waveform interleaving has been proposed, where the anodic and cathodic stimulator are separated. A voltage compliance monitor is also designed to ensure proper stimulation output. The designed system has been fabricated and occupies 0.0003mm² for two channels. Scaling this directly to 10⁴ channels would result in an area of 1.645mm². This area can be reduced even further via electrode multiplexing, which the designed system readily allows for. An output availability (i.e. how many input codes are possible after calibrating) of 99.2% and 97.3% is reported for the anodic and cathodic stimulator at an 8-bit resolution over the full output range. ... Read more

Introduction: Over the past two decades deep brain stimulation (DBS) has emerged as an important therapeutic option for Parkinson’s disease (PD). However, the current DBS programming method, monopolar review (MPR), is time-consuming, requires highly trained personnel and causes discomfort for patients. This study aimed to predict the optimal stimulation contact(s) based on local field potential (LFP) recordings by the implanted leads and a sensing enabled DBS system, and as such, improve the efficiency of DBS programming in PD patients.

Methods: Level-based LFP recordings (OFF-medication) within the first two post-operative weeks in PD patients implanted with directional Sensight leads® and the Percept PC® neurostimulator in the Haga Teaching Hospital were retrospectively analysed. Time and frequency domain data were inspected for artefacts. From the individual theta (4-7 Hz), alpha (8-12 Hz), beta (13-35Hz) and gamma (≥36 Hz) bands the maximum power (Max.) and area under the curve above 1/frequency (AUC_flat) were extracted. The clinically chosen contact during MPR served as reference for all predictions. Machine learning models using AUC_flat features from frequency band combinations were evaluated using nested cross-validation. Two custom ranking methods, pattern based and decision tree, were developed for both beta band features individually. The predictive accuracy (Acc.) of the 1st and 2nd prediction combined was evaluated on a training and unseen test set, considering all data and subgroups based on amount of symptoms during MPR and amount of beta activity above 1/frequency. The ranking methods were additionally compared to an existing algorithm (DETEC).

Results: Recordings from 34 patients (68 subthalamic nuclei) were analysed. Artefacts did not overlap with frequencies of interest or were sporadic of nature. The machine learning model with the highest performance was a linear discriminant analysis combining raw beta and alpha features (AUC: Design: 0.86, Test: 0.69). For the 1st and 2nd predicted contacts combined, the two best performing ranking models were pattern based using AUC_flat (Acc. training set: 86.2%; test set: 100%) and decision tree using Max. (Acc. training set: 87.9%; test set: 100%). Correct pattern based (AUC_flat) predictions were more often 1st opposed to 2nd predictions than correct decision tree (Max.) predictions (Tr = 55.2%, T = 90% vs. Tr = 10.3%, T = 10%). Acc. obtained for subgroups based on amount of symptoms during MPR and amount of beta activity above 1/frequency were similar across all subgroups for the custom ranking methods. The Acc. of the DETEC algorithm was inferior to all custom ranking methods.

Conclusions: This study demonstrates the feasibility of using level-based LFP recordings to predict the optimal stimulation contact in patients with PD. The best results were obtained using the pattern based (AUC_flat) and decision tree (Max.) custom ranking methods. For clinical implementation the decision tree (Max.) ranking method is expected to be favoured. Although prospective research is required to identify the true Acc. of the models in clinical practice, these results show potential to halve the required DBS programming time (only two out of four contacts require evaluation), and can thus improve DBS programming efficiency. ... Read more

Currently, EEG recordings are mostly made using scalp recording devices. These devices are typically difficult to put on and bulky, limiting their use to a lab environment. In-ear EEG recording is proposed as a more practical alternative to scalp EEG recording.

In-ear EEG replaces the scalp electrode array with more discrete earpieces, recording the EEG signals from the ear canal instead of the scalp. Because of their increased convenience with respect to their scalp electrode array counterpart, the earpieces allow for everyday use, enabling the possibility of long-term EEG recording, outside of a lab environment.

This work presents the design of a fully-integrated in-ear EEG device. The device has been designed to record EEG signals, process them locally, and transmit the processed data to a smartphone or personal computer. The device is made to be easily re-programmable, making it possible to change the device firmware depending on the intended application. This functionality has been integrated into a small form factor, to allow for the integration of the electronics into an earpiece. A low-power design has been created, to allow for long-term battery operation.

Two prototypes have been made. The first prototype focuses on exploring different system configurations, with the small form factor less of a priority. The performance of the prototype is tested by comparing it to a commercially available scalp EEG recording device based on scalp EEG recordings. For this comparison, the auditory steady-state response (ASSR), steady-state visual evoked potential (SSVEP), and alpha-band modulation paradigms are used. The prototype has shown similar performance to the scalp EEG device in these experiments, with the scalp EEG device performing slightly better for the ASSR and alpha-band modulation paradigms, while the prototype showed the best performance for the SSVEP paradigm.

The second prototype realises the best-performing system configuration that has been found with the first prototype into a form factor that fits into an earpiece. Ear EEG experiments are performed, with electrodes placed around the ears. The ASSR, SSVEP, and alpha-band modulation paradigms have again been used in these experiments. The measured performance on the ASSR and alpha-band modulation paradigms are slightly worse than that measured using the scalp EEG experiments. The SSVEP experiments showed significantly worse performance compared to the scalp EEG experiments. These results align with the expectations, as ear EEG has been documented to have performance close to scalp EEG for the ASSR and alpha-band modulation paradigms, while the SSVEP performance is typically worse for ear EEG than for scalp EEG. ... Read more

Engineered Heart Tissues (EHTs) are a valuable approach enabled by Organ-on-Chip (OoC) technology to model human cardiac tissue. These small microfluidic devices allow the culture and development of living cardiac cells in 3D structures, to reproduce tissue dynamics and functionality in-vitro, thus fostering the development of new and more precise models for human diseases and organs’ physiological response.
One of the most important gaps in this recent technology is the lack of integration of electronic devices in the platforms, such as electrodes for tissue stimulation or sensors for real-time monitoring of the tissue.

To cover this gap, a polymer-based platform with microwell and micropillars for culturing EHTs and measuring their contractile properties was developed in ECTM group at TU Delft. The contraction force exerted by the beating cardiac tissue, self-assembled around the micropillars, is quantified by measuring the displacement of the micropillars with spiral capacitive sensors embedded in the substrate. The force generated by the tissue corresponds to a capacitance change which is simulated to be in the aF range, requiring a high-precision, sensitive, and portable read-out circuitry.

The investigation and development of this readout electronics is the final goal of this Master Thesis. A literature survey about possible capacitive readout techniques was conducted to identify suitable architectures for measuring such small dynamic changes in capacitance. Two solutions available on the market (Smartec UTI and Analog Devices AD7746) implementing two of these architectures were chosen, tested, and characterised. Benchmark measurements with accurate laboratory instrumentation were performed, and noise figures of the two solutions were evaluated.
To allow the readout, EHT platforms with embedded sensors need to be transferred and assembled on a custom Printed Circuit Board (PCB): the viability of this challenging assembly process was evaluated. The multiple constraints deriving from such a complex project determined the development of a non-standard assembly process, which proved to be delicate and gave origin to multiple failure modes. Those were documented and analysed, to identify alternative or improved assembly procedures which need to be developed to fabricate reliable samples, since these weaknesses were identified as the most critical aspect at this point of the project.

The results obtained showed how the two solutions for the readout provided results in good agreement with more precise non-portable laboratory instrumentation, and promising noise figures. The platforms with embedded sensors were successfully transferred to the developed PCBs, and measurements showed good agreement with the simulated static behaviour of the sensors, thus providing a valuable proof-of-concept for the whole project.
The dynamic behaviour of the sensors was preliminarily investigated and characterized using nanoindentation tests, indicating the possibility to measure a linear relationship between the force applied and the difference in the sensed capacitance. Despite this good result, further tests need to be performed to verify it and to address some criticalities that were identified.
Cardiac tissue was cultured on platforms with integrated sensors and the biocompatibility of the entire system was proved. The behaviour of the sensors during biological measurement with cardiac cells is left to future investigation. ... Read more

This thesis discusses the basic architecture, design details, circuit implementation, and measurements of a digital class D current driver.
The driver contains two main parts: a digital control loop and analog circuits.
Parts of the important part in the digital control loop contain noise shaper, plant compensator, and cable capacitance compensator. The noise shaper has two core functions: 1. It seeks to minimize the difference between the process variable and the set point. 2. It shapes the noise that is generated in the forward loop. The plant compensator is used to compensate for the output filter, the load, and the cable. The cable capacitance compensation is a measure-based technique to generate a negative output capacitance to compensate for the cable capacitance. The analog circuits consist of a full bridge, data acquisition circuits, and floating supplies.
Multiple simulation models have been made in Matlab. According to the simulation results, the amplifier shows its flexibility and performance to handle a large range of inductive loads.
A prototype has been implemented. The measurement results show that most of the hardware performance is as intended. Due to the time limitation, the whole amplifier is not tested. ... Read more

Patients that suffer from heart failure can benefit from wearable monitoring devices that continuouslymonitor the condition of the heart. One of the foremost symptoms of exacerbation of the heart is fluidcongestion in the lungs. One of the method to measure a change in biological tissue, such as thebuild­up of fluids in the lungs and chest, is bioimpedance measurements. By injecting an alternatingcurrent into the tissue, a voltage develops across the tissue that is proportional to the impedance ofthe biological tissue, the bioimpedance. Conventional bioimpedance measurement techniques are notsuitable for continuous monitoring of the patients as they are power consuming and hinder patient intheir daily living.This thesis proposes an alternative approach that is based on a pulse width modulation (PWM) inorder to convert the measured analog signals to time signals. To determine the measured bioimped­ance, the magnitude and phase should be derived. The proposed design employs one channel toconvert the measured voltage across the bioimpedance to a PWM signal. As this PWM signal con­tains the amplitude information of the measured voltage, the magnitude of the bioimpedance can bederived. Furthermore, a second reference channel is employed where a known resistive reference isconverted to another PWM signal. By comparing the two PWM signals, the phase of the bioimpedancecan be determined. The proposed system requires only a comparator and triangular wave in order toconvert the measured analog signals, compared to the complex implementation of the conventionallyused analog­to­digital converter.In order to validate the design, the circuit is simulated and implemented on a printed circuit board (PCB). The PCB operates correctly on 3.3V and, additionally, an voltage­controlled current sourceis implemented and connected externally to the PCB to provide an excitation current of 100μA and10kHz to the circuit. The circuit should be capable of measuring the voltage across a device under test(퐷푈푇) that consist of resistive and capacitive components. This is because the bioimpedance can bemodelled by the Fricke­Morse model, which consist of a resistor in parallel with a capacitor and resistorin series. The implemented PCB can measure 퐷푈푇 magnitudes up to 1kΩ and is determine the phaseshift between the two PWM signals.This work shows an important contribution towards a wearable continuous bioimpedance measure­ments system to monitor patient that suffer from heart failure. It has been shown that the presenteddesign can measure the magnitude and phase that are required to determine the measured bioimped­ance, and also reduce complexity of the measurements instrumentation. ... Read more

Medical Body Area Networks (MBANs) are a cluster of possibly heterogeneous devices, communicating with each other in, on or around the human body. Through these devices, medical data is collected, processed in some way and transferred outside of the network. The IEEE 802.15.6 standard aims to govern communications between such devices. It includes a set of constraints for physical features and communication on the PHY and MAC level, as well as association/ disassociation protocols and security services that applications need to comply with. Given the high sensitivity of the medical data transmitted via MBANs, network security is crucial. This thesis consists of three main contributions: (a) a structured procedure to analyse the security features of the IEEE 802.15.6 standard by using realistic hypothetical scenarios is introduced (b) a thorough security analysis of the standard is conducted (c) recommendations on how to improve the security posture of the standard are given. ... Read more

Vagus nerve stimulation (VNS) has been proven to be an effective treatment for patients suffering from drug­-resistant epilepsy. Current vagus nerve stimulators are either implantable devices or bulky, handheld devices. The implant consists of a pulse generator beneath the skin of the chest, connected to electrodes that are wired to the cervical vagus nerve. While capable of delivering localized electrical impulses, the implantation is highly invasive. Handheld devices do not ask for a risky surgery, but their imprecise targeting of the neck reduces efficiency and provokes additional side effects.

Ultrasound has been widely used in clinical applications ranging from focused ultrasound (FUS) tissue ablation to medical imaging. Much research is being carried out on the use of FUS neuromodulation as a low-­cost, non-invasive alternative to electrical VNS. The combination of using ultrasound for neuromodulation as well as imaging allows for image-­guided FUS where automated tracking of the nerve location improves the targeting of the vagus nerve.

In this thesis, the design of an ultrasound vagus nerve stimulator with combined imaging capabilities is presented. The novel architecture combines FUS for vagus nerve neuromodulation and plane wave imaging (PWI) with optimized sparse receive sub­arrays to obtain the location of the nerve within the neck.

A 2D array of piezoelectric transducers with λ/2-pitch poses a strict constraint on the pixel area. The goal of the system design is to minimize the area consumption of the on­-pixel electronics by making use of already present circuit blocks. A shared delay­-locked loop generates the required continuous phases for FUS neuromodation, while a row-­column pulse control block generates a single pulse from the available phases for use in PWI. This way the on-­pixel transmit hardware is reduced to a multiplexer and high­-voltage driver.

The receiver consists of an analog front­-end and analog-­to­-digital converter and is shared among a sub-­array of receive elements. Different degrees of random sparsity are explored for the optimal implementation of the receive sub-­arrays within the full array. Synthetic aperture imaging allows the multiplexing of multiple receive elements to the shared front-­end and reduces the time spent on imaging using dynamic beamforming on a single data set.

The full system­-level architecture of a complementary metal­-oxide­-semiconductor (CMOS) interface is proposed and the receiver front-end has been designed and simulated. ... Read more

The overarching goal of Engineered Heart Tissues (EHTs) is to develop functional 3D heart tissues in vitro with the potential to find drug targets, identify drug toxicity and predict the effects of the drugs in the body. This 3D model of the cardiac tissue consists of a bunch of cells, self-assembled around specific anchoring points the main aim of which is to support tissue formation. Although this model is very promising, still has to overcome an important drawback which is the lack of maturation of iPSC-derived cardiomyocytes (CM). This obstacle leads to limited recreation of the adult human cardiac tissue physiology. It has been shown that electrical stimulation of cardiac cells is one of the most important factors for cardiomyocyte’s maturation. Therefore, the principal goal of this thesis is to design and fabricate an electrical stimulator device and integrated electrodes in an existing EHT platform for electrical stimulation of cardiomyocytes. In the beginning, a literature study was conducted on different electrical stimulation methods and existing electrical stimulation devices for this purpose. For the first part of the thesis which was the design and fabrication of electrical stimulator, the electrical stimulation parameters that must be fulfilled by this device, were specified. Various hardware design approaches were studied in detail and compared before the design and implementation of the final idea. Simulations of the selected method using the LTSpice software tool were performed in order to study the behavior of the electrical stimulator design. The device is a 16-channel electrical stimulator that provides perfect rectangular biphasic pulses in the range of±15Vwith adjustable voltage amplitude, frequency and duty cycle. A user-friendly interface allows the user to select the desired channel of stimulation and the signal is distributed providing electrical stimulation to a total of EHT platforms. Therefore, this device was designed in a way to be connected with a second Printed Circuit Board(PCB) which hosts 16-EHT with integrated electrode chips. The 16-EHT holder (PCB)was also designed to suit in a 96-well plate, used by biologists for cardiac-cell culturing inside the incubator. The characterization of the electrical stimulator took place in the measurement room of the EKL lab. The results of the measurements verify the precision and efficiency of the electrical stimulator circuit as they are in excellent agreement with the simulations. The second part of the thesis includes the fabrication of integrated electrode-chips. The fabrication was conducted at EKL lab, in the Microelectronics Department of TUD using clean room microfabrication techniques. The selected electrode material was TiN due to its unique mechanical properties and the electrodes were fabricated on PDMS, encapsulated between two layers of polyimide. The main steps required for the integration of these electrodes in the existing EHT platform have been also carried out. ... Read more