- This thesis presents a control metric designed to create clinically interpretable, high–signal quality recordings with water-based EEG, utilizing impedance and zero-crossings
- The metric was integrated into a newly developed, intuitive, user-centred interface for independent patient use in a home environment.
- A home study using this interface demonstrated that it enabled independent use. Approximately half of the recordings were clinically interpretable, the other half were not, primarily due to low-voltage signals. Signal quality was lower than in previous studies conducted under technical supervision.
- The metric requires further optimization to improve signal quality and clinical interpretability, with strict electrode-specific impedance (<100 kΩ) and low-voltage signal handling.
- In a controlled environment, prolonged water-based EEG recordings of up to three hours were feasible, with stable signal quality and acceptable user comfort.
- The usability of the proposed solution should be further validated in people with epilepsy. Its potential role between wearable solutions and inpatient monitoring for therapeutic applications should be further established.

This study investigates how electrode number and spatial configuration affect electroencephalography (EEG) source localization performance in the context of visual evoked potentials (VEPs). Five healthy participants performed visual stimulation tasks while EEG signals were recorded with a 256-channel cap. Six electrode montages were evaluated: four whole-head configurations (32, 64, 128, and 256 channels) and two targeted layouts (an 84-channel occipital-only montage and a 164-channel montage excluding frontal sensors). Source localization performance was assessed based on VEP neurophysiological expectations using three complementary indices: a Lateralization Index (LI) for hemispheric dominance, a Signal-to-Noise Ratio (SNR) at the source-space level, and a custom Occipital Precision Index (OPI) that quantifies how tightly activation is confined to occipital regions. Higher-density montages (128 and 256 channels) achieved the highest OPI values (≥0.68), yielding the most focal localization and reflecting accurate activity confinement to the visual cortex. Notably, the targeted 84-channel occipital montage performed comparably to both the 128-channel whole-head and the 164-channel targeted configurations in OPI and SNR, demonstrating that dense sampling over the region of interest can rival broader higher-density coverage. In contrast, lower-density whole-head montages (≤64 channels) exhibited inflated LI and SNR values but lower OPI, indicating reduced spatial precision despite seemingly higher signal metrics. The findings suggest that targeted electrode configurations can approach the performance of high-density caps when the region of interest is known in advance, though the small sample size (N=5) warrants caution and further validation.

Electrical stimulation of the median nerve is known to activate somatosensory pathways and elicit somatosensory evoked potentials (SEPs), measurable with the electroencephalogram (EEG). In contrast to the traditional stimulation with individual pulses, alternative approaches indicate that periodic pulses may elicit, at some frequencies of stimulation, a steady-state SEP in which the dominant frequency of the EEG corresponds with that of the stimulation. Even though this steady-state approach presents several practical benefits and it may enable new applications, its study in the somatosensory system has been limited so far, especially with electrical stimulation.
To bridge this gap, this study aimed to identify measurable changes in the brain response to periodic electrical stimulation when stimulation frequency is increased, as well as to identify a potential threshold frequency at which the steady state appears.
With this purpose, transcutaneous electrical stimulation was used to stimulate the median nerve of 9 healthy subjects using pulse trains with frequencies of 3, 7, 13, 19 and 36 Hz. Time and frequency domain analyses were used to compare responses across all stimulation frequencies.
Results of the analysis in the time domain showed a significant decrease in the amplitude of SEP component P40 for the highest frequencies (19 and 36 Hz) compared to the lowest frequency (3 Hz), as well as a maximal activation centered at the somatosensory cortex, contralateral to the stimulated side. Results of analysis in the frequency domain showed salient peaks in the frequency spectra at the frequencies of 19 and 36 Hz, which were not present for the lower frequencies. Additionally, the power distribution across the scalp at these frequencies showed higher values at the side contralateral to the stimulation.
According to previous definitions of a steady-state in evoked potentials, our results indicate that a steady-state SEP can be elicited by transcutaneous electrical stimulation at frequencies of 19 Hz and above. These observations could be interpreted as a transition in the nature of the response due to the activation of neural pathways different to those activated for lower frequencies. However, further research is needed to confirm this hypothesis.

Pulse oximetry is an indispensable, non-invasive tool for monitoring functional arterial oxygen saturation (SpO2) across diverse clinical settings. Its accuracy, however, remains limited by discrepancies between SpO2 and arterial blood gas-derived oxygen saturation (SaO2), the clinical gold standard. These inaccuracies arise from both technical factors (e.g., motion artifacts, device variability) and physiological influences (e.g., skin pigmentation, peripheral perfusion, underlying health conditions). Moreover, SpO2 measurements vary across anatomical sites, and no consensus exists on the optimal sensor location, as performance depends heavily on contextual and patient-specific factors.
The thesis addresses the research question: Can predictive models, leveraging physiological and contextual features together with multi-site pulse oximetry data, enhance SpO2 estimation accuracy compared to conventional single-site measurements? To investigate, predictive models were developed for eight sensors using data from two desaturation studies (SaO2 70-100%). Parsimonious regression models were designed to predict SpO2 bias (SpO2-SaO2) with minimal informative feature sets. Corrected Spo2 values derived from these models were further integrated through three multi-sensor fusion strategies.
Results demonstrated significant improvements in bias prediction for 3 of the 8 sensors, with 7 showing reductions in ARMS (accuracy root mean square), ranging from 4.43% to 37.02% relative to baseline. The quadratic weighting fusion method, which weighted corrected SpO2 values inversely to the square of their predicted bias, achieved statistically significant improvements in 21 of 28 sensor pairs, while consistently reducing ARMS across all combinations. Importantly, these models also reduced differential bias, mitigating the systematic overestimation of SpO2 in individuals with darker skin tones.
This work demonstrated that sensor-specific bias correction models and context-aware multi-site fusion can substantially improve both accuracy and fairness in SpO2 monitoring. While the study is limited by its reliance on healthy volunteers, a small dataset, and a restricted set of sensors, the framework provides a promising foundation. Future work should focus on clinical validation in diverse patient populations. With further development, these algorithms could be embedded into oximeter devices, enabling real-time, patient-specific bias correction and paving the way toward more reliable and equitable pulse oximetry.

This thesis presents a novel neurofeedback system for mu-rhythm modulation using an adversarial deep learning approach. The goal was to train subjects to modulate the mu-rhythm in their brain activity and to investigate the usability of this system for reflex modulation experiments. Two EEG classifiers were implemented: a Rest vs. Motor Imagery (RestMI) model and a Motor Imagery vs. Motor Movement (MIMM) discriminator. Five healthy subjects participated in five sessions of BCI training followed by a reflex assessment. During the reflex assessment the subjects had to hold a constant flexion in their wrist in order to provoke mechanical reflexes, which introduced an extra challenge for the classifiers. The RestMI model achieved a mean classification accuracy of 0.73 in the first two sessions, however performance decreased when trials with wrist flexion were introduced. The MIMM model showed a low online performance during early sessions, indicating subjects could deceive the discriminator. The reflex assessment showed mixed results, with indication of modulation of the long latency response. These findings suggest adversarial DL models can support specific mu-rhythm training in some subjects, although further work is needed with a larger sample size and more task-specific training sessions.

Purpose: Parkinson's Disease (PD) is the second most common neurodegenerative disease with a still increasing incidence. The implementation of new medical technology also increases yearly, to achieve better and a more efficient healthcare. One implementation of such a corresponding medical technology is Medtronic's sensing technology, which allows for reading of Local Field Potentials (LFPs). Furthermore, new assessment options are also investigated, with Markerless Motion Tracking (MMT) programs as interesting option for assessment of the Unified Parkinson's Disease Rate Scale (UDPRS). This study aims to investigate correlations between these LFP signals and parameters extracted from MMT programs during gait.
Methods: Pose estimation in this study was performed using MMT programs, while simultaneously recording LFP data in PD patients implanted with a Deep Brain Stimulation (DBS) device. LFP activity was filtered to only include beta activity, while this is primarily correlated with motor impairment. Normalisation methods were then applied on pose estimation data for allowance of distance calculation and extraction of the arm-swing parameters: velocity, acceleration and jerk.
Results: Results indicate a negative trend between LFP data and among almost all examined parameters. This applies for both trends observed: beta power analysis, as well as the UPDRS analysis. Left hemisphere shows significant correlation for the velocity (rho = -0.356, p = 0.046), acceleration (rho = -0.456, p = 0.01) and jerk (rho = -0.465, p = 0.01). While right hemisphere does not show this significance. Whereas, amplitude calculations even show contrary outcomes.
Conclusion: This study shows multiple connections between LFP data and gait parameters. Furthermore, it confirms the importance of arm-swing as indication for gait abnormalities. Finally, these findings suggest the need for more research on other parameters originated from different UPDRS tasks.

Children with multiple sclerosis (MS) frequently have reduced visual processing speed, which can affect learning, social interaction and daily functioning. Identification of cognitive impairment at an early stage is essential for timely intervention. This study explores if electroencephalography (EEG)-based functional connectivity (FC) can be utilized as a non-invasive biomarker for visual processing speed in children with MS.

Resting state EEG data and neuropsychological assessments were analyzed from ten children diagnosed with MS. A custom preprocessing pipeline was developed and validated to ensure data quality for FC analysis. Connectivity metrics, including fronto-occipital and interhemispheric connectivity, network efficiency based on minimum spanning tree analysis, and individual alpha peak frequency, were extracted from source-reconstructed EEG. Correlations were tested between these metrics and visual processing speed, measured with the Processing Speed Index of the Wechsler Intelligence Scale for Children.

As hypothesized, trends toward greater network efficiency and stronger interhemispheric and fronto-occipital connectivity were associated with higher visual processing speed. Although none of these associations reached statistical significance, the results support the potential of EEG-based functional connectivity to evolve into a clinically relevant biomarker, enabling early diagnosis, personalized treatment and improved prognosis for children with MS.

Code repository - https://github.com/Ilse2001/code-repository-msc-thesis-eeg-fc

Introduction: Chronic pain is a widespread and complex condition. Spinal Cord Stimulation (SCS) offers effective pain relief in a portion of patients suffering from chronic pain, although its underlying mechanisms of action remain unclear and may differ between tonic and burst stimulation paradigms. Brain connectivity analysis can help reveal how chronic pain and SCS affect communication between brain regions. Magnetoencephalography (MEG) is particularly suited for this due to its high temporal resolution. Graph theory enables modelling of whole-brain networks, and Graph Neural Networks (GNNs), a deep learning approach designed for graph-structured data, is well-suited for distinguishing specific connectivity patterns within complex network structures. While promising, GNNs have not yet been applied to SCS or chronic pain. Furthermore, beyond classification, explainability approaches allow insights into which graph substructures drive GNN model's decisions.

Aim: The overarching aim of my exploratory study was to develop and train a GNN model based on MEG data from patients with chronic pain with SCS, to identify differences in brain networks during stimulation ON and OFF.

Methods: Resting-state MEG data were collected from 22 chronic pain patients receiving SCS, recorded in two institutes. A cyclic stimulation protocol (1 min ON, 1 min OFF) was used.
Brain connectivity graphs were constructed using the phase lag index as functional connectivity metric, and features for each brain region were derived from the power spectral density. Graph datasets were created per frequency band, stimulation paradigm (tonic and burst), and recording institute. Separate GNN models were trained to classify stimulation ON and OFF states, and explainability techniques were implemented to unravel the key graph substructures driving the model's classification decisions.

Results: GNN models accurately classified stimulation states, especially using full-band, beta, and gamma graphs (accuracies: 0.99, 0.97, 0.99). Delta, theta, and alpha bands showed lower performance (accuracies: 0.76, 0.80, 0.77). Model performance was consistent across tonic and burst SCS paradigms and both recording sites (accuracies: 0.97, 0.98, 0.99, 0.97), however, performance across paradigms showed inconsistencies. Specifically, the model trained on tonic SCS and tested on burst SCS recordings showed a cross-paradigm accuracy of only 0.69. The GNN model achieved cross-site accuracies of 0.81 and 0.87 across datasets from the recording institutes, demonstrating consistent performance across patient cohorts. Furthermore, the explainability analysis outcomes highlighted several pain-related brain regions as key substructures in the graph for distinguishing stimulation ON and OFF states.

Discussion: This study introduces GNNs as a novel method for decoding brain network dynamics in chronic pain patients with SCS. The classification results and node-level explainability suggest that pain-processing regions are modulated by SCS. The cross-paradigm accuracy suggests that burst SCS only partially captures the features of tonic SCS, possibly indicating that burst SCS engages a more widespread brain network. However, interpretation of the findings is limited by the small sample size, inter-patient variability, and the inability to separate chronic pain effects from stimulation effects. Nevertheless, this framework offers a promising direction for application of GNNs for unravelling complex brain network dynamics in chronic pain and SCS. Future studies should focus on expanding this framework by utilizing GNN models to classify SCS treatment effectiveness, potentially providing more insights into the brain regions and connectivity patterns that are most predictive of treatment success.

In Upper Motor Neuron Lesion (UMNL) following stroke, patients can experience increased joint impedance, resisting joint rotation and hindering functional movement. This heightened impedance in UMNL is driven by both exaggerated reflexes and increased intrinsic muscle activation through co-contraction, hypertonus, or synergies. The simultaneous presence of these mechanisms complicates clinical distinction, especially given their theorised interplay, where increased intrinsic activation would further heighten reflex responses. Separate quantification of this intrinsic and reflexive impedance and their interaction, can aid in further investigation of the pathophysiology of post-stroke joint impairment and its treatment.

This work presents the investigation of an Open Loop System Identification (OL-SID) protocol, to perform this separate quantification of intrinsic and reflexive impedance for the elbow joint. Perturbation experiments were performed with 16 healthy subjects, using multisine positional perturbations and measuring the elbow torque response. An impedance model consisting of both intrinsic and reflexive parameters was fit to the estimated frequency response function (FRF), relating perturbation angle to joint torque. It was assessed how background muscle activation, as well as the frequency and velocity of the perturbation signal, influenced the modelled intrinsic stiffness, intrinsic damping, and reflex velocity-gain.

For this, three different biceps muscle activation levels were requested from the participants in different trials; 0%, 10%, and 30% of Maximum Voluntary Contraction (MVC), as confirmed by online EMG measurements. Participants were requested to not actively resist perturbations, but only to comply with the requested biceps activation level. Furthermore, three rotational multisine perturbations with a max. amplitude of 2 degrees were applied; Wide Bandwidth - High Velocity, Narrow Bandwidth - Low Velocity, and Wide Bandwidth - Low Velocity. Cross-combination of biceps activation levels and perturbation signal resulted in 9 impedance quantifications per participant.

Increased biceps activation resulted in a significant increase of intrinsic stiffness, intrinsic damping, and the reflex-gain. This confirmed the expected relationship between muscle activation and intrinsic impedance, as well as the theorised relation between intrinsic activation and the reflex response. Unexpectedly, differences in used perturbation bandwidth or velocity showed no clear influence on identified reflex gain. This contradicts findings of reflex suppression during high-bandwidth force perturbations in tasks that require resisting these perturbations, as well as during high-velocity binary or unidirectional joint stretches. This discrepancy shows that joint system identification results are highly dependent on perturbation type and subject task, emphasising the need to align the experimental design with the clinical question at hand.

Despite some shortcomings regarding low coherence of the estimated FRFs, and necessary further research on perturbation signal properties and their effect on the reflex response, the results of this study are promising. The observed trends in fitted parameters with increased activation levels in line with physiological expectations, indicate the ability of this technique to validly identify reflexive and intrinsic joint impedance. This distinction is highly valuable for advancing investigation of the pathophysiology and clinical presentation of UMNL post-stroke, in the pursuit of adequate treatment for different patients.

Introduction:
High impact respiratory conditions are common amongst preterm infants. Measuring the electromyogram of the diaphragm (dEMG) is a very promising technique to accurately measure respiratory state. However, the currently used Ag/AgCl electrodes are reported to be big and thick, making them less suitable to measure dEMG in preterm infants. Therefore, the aim of this study was to create a prototype for a thin, flexible electrode that could measure dEMG in preterm infants with at least the same signal quality as the currently used Ag/AgCl electrode.
Methods:
To do so, design requirements and wishes were set. Literature and three tests were used to assess whether a certain prototype met the design requirements. If one of the design requirements was not met, an iteration cycle was started and the prototype was redesigned. With the final version of the prototype, a proof of principle test was performed, where dEMG measurements were conducted on a healthy female adult using the prototype and the standard Ag/AgCl electrodes simultaneously.
Results:
The final design was created from two layers of Shieldit conductive fabric ironed onto cotton and one insulating layer of TPU. These layers were connected to a shielded cable by weaving the copper wire through the fabric.
Ultimately, not all design requirements were met. The frequency plot of the final prototype still showed a peak at 50 Hz, indicating insufficient shielding from electromagnetic interference. However, the final prototype was indeed dry, thinner, and more flexible than the Ag/AgCl electrode. The RMSE value for the prototype (0,5984 mV) was smaller than that for the Ag/AgCl electrode (0,5998 mV), although the opposite was true for the SD (prototype: 0,1031 mV, Ag/AgCl: 0,0316 mV). In addition, it was proven that the final prototype could be used to measure dEMG in healthy adults. The breathing frequency measured by the final prototype was equal to the breathing frequency measured by the Ag/AgCl electrode, whilst showing no significant difference in amplitude of the peaks of the breathing curve (p = 0,0830).
Discussion:
Design improvements could be made by eliminating the 50 Hz peak, decreasing diameter even further, creating a new version where the cable could pivot around the electrode or exploring wireless options.
Conclusion:
In this paper, it has been proven that a textile prototype is well capable of measuring dEMG in humans and offers several improvements over the Ag/AgCl electrode.

Moving through immersive virtual reality (VR) is commonly achieved by physically walking in the real room or using other techniques like an omnidirectional treadmill or walk-in-place. Roomscale walking is most similar to normal walking but is limited by physical space. However, other techniques can cause user experience issues such as VR sickness, balance problems, and feeling unnatural. Newer locomotion techniques are available such as powered VR shoes, which are shoes with motorized treadmills underneath. While walking, the shoes drive the user backward and actively negate the forward velocity, reducing the needed physical space. Yet, there is little evidence of the effect of powered VR shoes on user experience, which part of this work addresses. Additionally, previous research shows mismatched VR motion (optical flow) can increase VR sickness, cognitive load, and break presence. However, full-gait locomotion studies often focus on the device, neglecting optical flow, and what is the best body part to control optical flow direction is still an open question. Therefore, we first developed a novel algorithm to convert leg-based walking to optical flow while walking on VR shoes, which may also be used for other full-gait locomotion techniques. We conducted a study with 20 participants to find which of four optical flow implementations, differing in VR motion direction, resulted in the best user experience. These direction conditions were based on body-mounted trackers: i) head orientation, ii) hip orientation, iii) standing foot velocity direction, and iv) average orientation of both feet. Head-oriented walking resulted in a significantly worse user experience compared to other conditions, with no significant differences among any other conditions. Additionally, we found no effect of optical flow on VR sickness, Mental Effort, and Presence, contrary to previous studies, but instead significant differences in Ease of Use, Input responsiveness, and Appropriateness, and indication that other user experience factors might be impacted more. Finally, we discovered that walking on VR shoes, although not completely comfortable and natural, was learnable within 10 minutes for all participants under 60 years old.

Introduction:
Chronic Pain (CP) presents a complex and prevalent issue that significantly affects individuals and society. Exploring the complexities of CP involves analyzing Functional Connectivity (FC), a process that identifies how different brain regions communicate across distances. Magnetoencephalography (MEG) is particularly effective for FC analysis, offering advantages over Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) due to its superior temporal resolution. Most studies on FC in CP have focused on resting-state analyses, leaving a gap in research on connectivity responses to noxious stimuli in CP.

Study aim:
The overarching goal of my exploring study is to investigate FC differences in response to noxious stimuli between individuals with CP and Healthy Controls (HCs) across different frequency bands, using MEG. This encompasses the comparison of FC patterns within pain-related brain regions between these two groups, the analysis of their response to a noxious stimulus, and the synthesis of these findings to identify potential differences in how the two groups respond to noxious stimuli.

Methods:
The study involved 17 individuals with CP and 17 HCs, each undergoing MEG sessions within a conditioned pain modulation (CPM) paradigm. During each CPM block, 22 noxious stimuli were applied to the right tibial nerve. FC was computed between pain-processing regions using phase and amplitude-based metrics in different frequency bands. Connectivity patterns were compared between the groups using a non-parametric permutation test. Connectivity was also evaluated on a time-scale to observe potential changes in the FC in response to the stimulus. These results were taken together to observe potential differences in the groups in response to the stimulus.

Results:
In comparing FC patterns across the entire epoch between the HC and CP groups, there is a predominant observation of increased FC in the CP group relative to the HC group. The insula and Dorsolateral Prefrontal Cortex (DLPFC) emerged as central hubs, and these alterations were most prominent in the beta (13-29 Hz) and gamma-low bands (30-45 Hz). An increase in FC in the mean response over all scout pairs and both groups was observed immediately following the stimulus, particularly in the theta band (5-7 Hz). Additionally, in investigating the specific hypothesis that there may be distinct FC responses to noxious stimuli between the HC and CP group, the findings indicate subtle differences rather than clear, pronounced patterns, with findings in the theta, alpha and gamma-low bands.

Conclusion:
My study explored FC differences in response to noxious stimuli between individuals with CP and HCs across different frequency bands, using MEG. Higher FC was predominantly observed in the CP group, suggesting more interconnected pain-processing networks. Key regions demonstrating this increased FC included the insula and the DLPFC, suggesting an altered insula-DLPFC network potentially influenced by underlying physiological factors of the CP group. Specifically examining differences in FC response to the noxious stimulus between the HC and the CP group yielded in subtle differences rather than clear, distinct patterns. This study stands out as the first using MEG to identify FC in CP in response to noxious stimuli. Future research should focus on refining connectivity as a biomarker for treatment follow-up and potential outcome predictor.

Background context: Intraoperative neuromonitoring (IONM) has proven effective in reducing postoperative neurological complications. However, current understanding of IONM is limited and its precise meaning in relation to neurological outcomes remains unclear. Machine learning (ML) is a promising solution to analyze the excessive amount of IONM data quickly, objectively and in real-time.
Purpose: The goal is to develop a ML algorithm that can effectively predict neurological outcomes after spinal surgery using IONM data that include both motor evoked potentials (MEPs) and somatosensory evoked potentials (SSEPs), and analyze its key predicting features. To more effectively determine the specific independent contribution of both separate modalities, a separate ML model will be created for both MEP and SSEP in addition to a combined MEP-SSEP model.
Study setting: Retrospective study.
Patient sample: A total of 67 patients were analyzed.
Outcome measures: The neurological status three months postoperatively compared to the preoperative status, categorized into three classes: 'Neurological stable deficits', ‘Neurologically intact’ and 'Neurological improvement'.
Methods: 260 features were obtained from patients who underwent spinal surgery monitored by IONM. During nested cross-validation, the data was split into five folds, for both the inner and the outer loop. The four ML classifiers developed were support vector machine, K-nearest neighbors, random forest and extreme gradient boosting, and tested along the three modalities MEP, SSEP, and MEP-SSEP combination.
Results: Extreme gradient boosting outperformed the other classifiers on all performance metrics. The combined MEP-SSEP model exhibited the highest scores for sensitivity: 70.4%, specificity: 88.3% and accuracy: 87.1%, while the MEP model exhibited the highest performance for precision: 75.6%. Highest predicting scores per individual class were also obtained by this XGBoost classifier on the combined MEP-SSEP model. Key predicting features were the presence or absence of preoperative neurological deficits and last measured signal latency compared to baseline, with a contribution of 29% and 13.5% in the best performing model, respectively.
Conclusion: A reliable prediction of neurological outcomes three months postoperatively can be made combining MEP and SSEP IONM features, provided that the patient's preoperative status is accurately documented and included in the prediction. Though either MEP or SSEP features alone offer predictive value, MEP features show superior predictive values compared to SSEP features when both modalities are accessible, with latency emerging as a prominent predictive IONM feature.

Robotic rehabilitation systems that provide proprioceptive information offer a promising approach to helping stroke survivors regain lost proprioceptive functions. One potential way to provide proprioceptive information is viscosity rendering. However, how the modulated viscosity rendering affects brain activity remains unclear. To investigate the correlation between viscosity rendering and brain activity and to provide a neurological basis for the design of robotic rehabilitation systems, an experimental setup was developed to deliver various viscosity rendering and fixed stiffness rendering during hand movement. In the experiment, twelve healthy participants interacted with virtual bottles with the same bottle stiffness but filled with liquids of different viscosities under the same movement speed, providing different levels of proprioceptive information in the form of force on muscles and joints. Control conditions without viscosity and stiffness rendering were also tested, involving both passive and active hand movements at the same speed. Results showed that stronger mu-ERD and beta-ERD were observed during movements with viscosity and stiffness rendering compared to control conditions. No significant evidence suggested that different viscosity in rendering caused variations in mu-ERD or beta-ERD. Additionally, no significant differences were found between active movement without haptic rendering and passive movement in EEG activities. These findings suggest that while the existence of viscosity and stiffness rendering during movement strengthens brain activity, modulating viscosity rendering does not significantly affect this response. This insight is particularly valuable for designing robotic rehabilitation systems that incorporate viscosity rendering.

Scalp electroencephalography (EEG) is a widely used, noninvasive tool to assess brain activity. EEG is valuable for various neurological conditions like epilepsy and migraine. Conventional EEG uses gel-based electrodes, ensuring good signal quality but requiring complex setup and the removal of patients from their natural environments. Portable EEG devices with water electrodes offer easier home measurements but pose signal quality concerns. This thesis aims to evaluate the signal quality of water electrodes compared to gel electrodes and investigate the feasibility of home-based EEG measurements.
Three experimental protocols were used: resting-state (RS), single visual evoked potentials (SVEP) and 12 Hz steady-state visual evoked potentials (SSVEP). These signals were measured 1) in the lab, once with a gel cap and once with a water cap; and 2) at home with a water cap. Signal quality was assessed with the artefact proportion, the signal-to-noise ratio (SNR), the relative 12 Hz SSVEP power, the relative alpha band eyes-closed RS power, the presence of the Berger effect, and the SVEP waveforms.
In the lab setting, the water and gel cap showed similar signal quality as illustrated by a similar SNR, relative alpha power, alpha band presence and SVEP waveform. However, an increase in artefacts and slight decrease in relative 12 Hz power and SVEP amplitude show remaining shortcomings of the water cap compared to the gel cap. When comparing the water cap between lab and home settings, the performance closely matches. This is demonstrated by the similar SNR, relative 12 Hz power, alpha presence and SVEP waveform and amplitude. Differences were a decrease in artefacts and an increase in relative alpha band power for the signal measured at home.
Provided that the limitations of the water cap can be mitigated by further developments, the otherwise relatively comparable signal quality between the gel and water caps suggests that water-based EEG systems could be a viable alternative to traditional gel-based systems. Furthermore, the positive home study results suggest that home-based EEG measurements could be a viable alternative to lab-based studies with the help of a water electrode EEG cap.

This study investigates ”How does the modulation of the Soleus H-reflex vary across different levels of isometric voluntary contractions?” The hypothesis that the Soleus H-reflex amplitude follows a biphasic response, with an initial increase at lower contraction levels, followed by a plateau or decrease at higher levels of contractions. To test this, ten healthy subjects were recruited to perform isometric voluntary contractions at different levels of maximal voluntary contraction (MVC) (0%, 20%, 40%, 60%, and 80% MVC). The H-reflex peak-to-peak amplitude was measured and normalized as a percentage of the maximum M-wave (H/Mmax). The data were analyzed using a repeated measures ANOVA to assess the effect of MVC level on the Soleus H-reflex amplitude, followed by pairwise comparisons to identify significant differences between contraction levels. A repeated measures ANOVA revealed a significant effect of MVC level on Soleus H-reflex amplitude (H/Mmax) (F(4,32) = 4.82, p < 0.05). Pairwise comparisons showed a significant increase in H-reflex amplitude between 0% and 20% MVC (p = 0.013), while no significant differences were observed between higher contraction levels (20%, 40%, 60%, and 80% MVC). This pattern is indicative of the biphasic hypothesis, suggesting that the reflex amplitude significantly increases initially but plateaus as contraction intensity increases. These findings suggest that the initial increase and subsequent plateau in reflex amplitude may be influenced by changes in spinal excitability and inhibitory mechanisms as contraction intensity increases.

To have a better understanding of difference in characteristics between various mother wavelets, this paper presents a comprehensive investigation into the performance of three commonly used non-orthogonal mother wavelets, namely Morlet, Paul and DOG, in a wavelet-based system identification approach when used for evaluating joint impedance. This method is further modified to make the estimation result much closer to the realistic result. Additionally, the optimization of smoothing parameters is explored across ten distinct situations, encompassing diverse stiffness waveforms such as step, square, sine, triangle, and sawtooth, as well as two different input perturbations. Performance metrics, including running time, random error, bias error, total error, and variance accounted for (VAF), are used to assess the performance of the system identification method in each scenario. The result shows that Paul wavelet yields a better result of stiffness estimation together with bias error for most situations after averaging. The DOG has the shortest running time and Morlet wavelet gives the highest VAF and lowest random and total error. The findings of this study contribute to a better understanding of the strengths and weaknesses of various mother wavelets in joint impedance estimation, providing valuable insights for future applications in the field of system identification and parameter estimation in neuromechanics control.

Critically ill patients in the Intensive Care Unit (ICU) are often comatose and thoroughly monitored. Neurological complications occur in up to 20% of these patients. Therefore, monitoring of the brain, which can be performed using electroencephalography (EEG), has the potential to significantly impact the outcomes of patients at the ICU. Despite the potential of EEG as a noninvasive method for monitoring the neurological status of critically ill patients, the labor-intensive and complex nature of its application and assessment has hindered widespread implementation. Therefore, the aim of this thesis was to identify the logistical and technical prerequisites for efficient and effective neuromonitoring using EEG in the ICU.

Chapter 1 provides an overview of neuromonitoring techniques in the critically ill patient. It delves into the neurophysiological background of the EEG, the techniques used for applying the EEG electrodes, and the EEG assessment.

In Chapter 2 we present a qualitative study on the optimal conditions for EEG monitoring in the ICU. Through 12 individual and 2 focus group interviews with employees from different departments within and outside of the hospital, the current workflow regarding neuromonitoring in the ICU is identified. Additionally, we evaluate the barriers and facilitators for change in this monitoring process through the Consolidated Framework for Implementation Research (CFIR). Factors such as motivation and willingness to change serve as facilitators, while a lack of interdepartmental communication and the high workload for various healthcare professionals involved can be significant barriers.

The qualitative research reveals that the largest group monitored using EEG in the ICU consists of patients suffering from postanoxic encephalopathy, which can be a complication of a cardiac arrest. Therefore, in Chapter 3, we examine the technical requirements for optimal EEG monitoring. Specifically, we focus on the necessary number of EEG electrodes for reliable automatic classification of the EEG background pattern in postanoxic encephalopathy. By training an Random Forest (RF) classifier with input from 12, 10, 8, 6, and 4 EEG electrodes, we develop a model with a micro-averaged One-vs-Rest (OvR) Area Under the Curve - Receiver Operating Characteristics (AUC-ROC) value of 0.923, 0.924, 0.924, 0.925, and 0.923 (p-value: 0.279) for the different numbers of electrodes respectively. The constant performance of the model suggests that a reduced number of electrodes may be sufficient for monitoring this patient group, potentially reducing the workload for EEG technicians. Automatic assessment of the EEG can also contribute to a decreased workload for clinical neurophysiologists.

In Chapter 4 we provide the conclusions and future perspectives of this thesis. We have demonstrated the potential for change in the EEG monitoring workflow at the ICU of the Erasmus MC, indicating that there is an opportunity to work towards more effective and efficient neuromonitoring. Future research should focus on a broader range of logistical and technical prerequisites - including effective interdepartmental collaboration and which EEG equipment to use - thereby creating opportunities to improve treatment and outcomes of critically ill patients.

Background: A brain-computer interface (BCI) is a system that enables humans to control a computer by their brain signals. This can be achieved by modulating the sensorimotor rhythm (SMR) through both motor execution and motor imagery. The potential enhancement of spinal reflexes and motor control through SMR training is attributed to the hypothesis that activity-dependent brain plasticity guides spinal plasticity during motor skill learning. However, the signal processing needed for conversion from raw brain signals to a robust control signal is challenging. The recorded electroencephalogram (EEG) signals are contaminated by multiple unknown sources and suffer from inter-subject variability, complicating the development of the BCI.
Objective: To obtain a robust control signal the study 1) investigated the relation between event-related desynchronization (ERD) and mechanical stretch reflex size in the flexor carpi radialis across four muscle pre-loads consisting of 0%, 5%, 25% and 40% of maximum voluntary contraction (MVC), 2) investigated the ability of three offline signal processing paradigms in distinguishing between periods of rest and activity using EEG data associated with motor execution and motor imagery, 3) built a pseudo-online signal processing paradigm to simulate real-time signal processing based on a single trial and a continuous data stream.
Method: Mechanical stretch perturbations were applied to the wrist under four percentages of MVC during motor execution and imagery conditions in six healthy subjects. The data anal- ysis encompassed signal processing techniques including pre- processing with a large Laplacian filter, feature extraction through autoregressive modelling (AR), power spectral density (PSD), or discrete wavelet transform (DWT), and classification using linear discriminant analysis (LDA).
Results: Mechanical stretch reflex sizes and ERD amplitude significantly increased with increasing percentage of MVC for motor execution trials. For motor imagery trials, no significant correlation was found between the stretch reflex size and ERD amplitude. The offline signal processing paradigms resulted in classification accuracies of 73.55% (PSD), 71.96% (DWT) and 57.13% (AR). The classification accuracies significantly increased with increasing percentage of MVC. The pseudo-online paradigm resulted in a mean classification accuracy of 51.38%.
Conclusions: The EEG-based BCI shows potential for enhancing the functional recovery of patients with motor disorders. The findings demonstrate that feature extraction methods PSD and DWT could effectively distinguish between periods of rest and activity in motor execution data. Nevertheless, for the intended application, including real-time processing based on single trial motor imagery data, BCI performance should be improved. Future research should focus on motor imagery EEG data encompassing motor imagery training and feedback on motor imagery performance.