The human brain, with its intricate web of billions of neurons and trillions of synaptic connections, is a remarkable organ responsible for performing complex cognitive processes. While brain imaging techniques like fMRI and EEG provide insights into neural activity, there is no broadly accepted mathematical framework for the collaborative activity of neuronal populations and their communication. In the interdisciplinary fields of neuroscience and computational modelling, this research introduces a data-driven mathematical framework for modelling the brain's cortical network, derived from EEG data, while incorporating an exogenous input. This framework captures brain dynamics, forming a state-space model using subspace identification, and evaluates statistical dependencies among brain sources, known as functional connectivity. Employed on a dataset mimicking a sensorimotor task, it proves effective in characterizing brain dynamics, with the PO-MOESP method outperforming N4SID. Additionally, the framework is competent to assess functional connectivity between brain sources, resulting in a network connectivity diagram that offers valuable insights into the statistical relationships between distinct brain regions. Altogether, it is concluded the designed algorithm can determine the intricate interactions within the brain during a simple passively performed task stimulating the brain. Consequently, this achievement forms a robust foundation for advancing Brain-Computer Interfaces (BCIs) and enhancing the diagnosis of neurological disorders.

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.

Reflex mechanisms are an efficient method humans use to deal with mechanical disturbances in daily life. In the muscle we see two reflexes, the M1 reflex and the M2 reflex. The M2 reflex is cortically modulated and is affected by intention and habituation. A lot of research has been conducted on understanding the M2 reflexes, their origin and the factors influencing these reflexes. Less research, however, has been done to understand the cortical contribution of these M2 reflexes and what affects the muscle stretch evoked potentials. This research investigates the relationship between the stretch evoked potential and the corresponding M2 reflexes. We investigate the effects of the traditional ’Resist’ and ’Let Go’ tasks with a wrist manipulator on the evoked potential and M2 reflex, as well as the effect of longer periods between perturbations. This research showed that between the ’Let Go’ and ’Resist’ tasks, no difference in evoked potentials were present. There was a difference in the late M2 reflex however. The evoked potential was affected by the time between perturbations. A higher time between perturbations elicited a higher cortical response at the motor cortex as well as a higher M2 reflex. This result suggests that besides the task goal, the habituation and time between perturbation can be a significant factor at both the muscle as well as cortical levels. These results facilitate more elaborate research where the effect of these factors is investigated during voluntary movements.

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.

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.

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.

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.

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.

Temporomandibular disorders (TMD) affect about 5-12 percentage of individuals with consequences such as jaw noises, clicking, myofascial pain, discomfort, limited mandibular range of motion and stress. Treatments depend on the cause and extent of the damage and part of the joint or jaw affected. When exact aetiology of TMD is unclear, generic treatments (splint therapy) are offered. Different oral activities performed on a daily-basis result in different loading conditions on the joint, possibly triggering TMD. These need to be investigated to know the usage of the masticatory system and the potential damage, in order to perform specific treatments. Our work aims at developing an online algorithm that can classify oral tasks performed by individuals. It can be used during daytime or overnight’s sleep to see how often different activities are performed by subjects. A 4 stage wavelet decomposition was employed to the signals and then subjected to feature extraction to train a support vector machine algorithm with. The prediction accuracy was found to be 90 percent for a group of selected oral activities (static, jaw opening, chewing and maximal voluntary clenching). The algorithm had about 80 percent prediction accuracy when classifying both functional (chewing, jaw opening and static) and parafunctional activities (grinding, incisal biting, maximal voluntary clenching, protrusion and laterotrusion) together. However, 80 percent accuracy is regarded as a set back due to the lack of more data. On reviewing the recognised activities, further research on any overuse of muscles or loading on the jaw joint during each activity can be conducted to give specific treatment and therapy preventing any deteriorating actions. Thus,the developed classification algorithm works as a prototype for future studies on online recognition of oral activities.

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.

Human joint admittance changes with numerous factors constituting the operational point. For large changes of the operational point, joint admittance can be identified using Linear Time-Varying methods on torque and angular position signals measured on human joints. Out of the available methods, the Skirt Decomposition method was selected due to its nonparametric structure and the limited number of a priori assumptions it makes. Its employment on the identification of human joint admittance was completely novel. The method was applied to a simulation model representing joint admittance and on experimental data measured from the wrist joint. In the experiment, the subjects were changing the applied torque to follow a desired trajectory, while the angle of the wrist was perturbed by the manipulator.With a properly designed multisine input, taking into consideration the speed and complexity of the time variation, a variance accounted for (VAF) close to 100 % was obtained in the simulation study on all the tested conditions. From the experiment, it was seen that the contribution of the time variation in the frequency domain was partially masked by the output noise. The noise level could be decreased by lowering the amplitude of the desired torque, and by removing the voluntary torque from the analyzed data. With a desired torque level ranging between 5% and 20%, and considering the bandwidth between 2 Hz and 20 Hz, the mean power of the output residuals in the frequency domain ranged between 16.2 and 27.1 for all the tested conditions. Furthermore, the time-varying dynamics retrieved from the system function showed a clear correlation with the desired torque trajectory.

Joint impedance describes the dynamic resistance of a joint in response to position perturbations. Joint impedance is known to vary nonlinearly during movement caused by varying joint angle and muscle activation. The nonlinear behaviour can be described using linear time-varying models under certain assumption. Recently a number of time-varying system identification algorithms to estimate joint impedance have been developed. System identification algorithms are typically validated in simulation. These algorithms have not yet been compared using the same simulated data or validated using real experimental data in this application. In this study the algorithms are assessed on their ability to estimate joint stiffness using three data sets. The first data set is from a simulation model representing joint dynamics with time-varying stiffness and damping. The second data set is from a mechanical variable stiffness device. A mapping of the true stiffness of the device was extracted by interpolating the estimated stiffness of time-invariant trials. The last data set is real experimental data from a human ankle with varying contraction levels to which small position perturbations were applied. The simulation study and the experimental mechanical study suggest that when estimating stiffness the linear parameter varying (LPV) method has a bias, the kernel based regression (KBR) method overall has the highest error, the ensemble impulse response function (eIRF) method needs many repetitions, the basis impulse response function (bIRF) method is able to achieve the lowest error, the short data segment (SDS) method is the most robust to different perturbation signals, and the ensemble spectral methods (ESM and mESM) are able to achieve reasonable results. The results of the experimental human study show that the estimated stiffness by the ensemble and short data segment methods have a trend similar to that of the EMG signal, albeit with different offsets. The bIRF, SDS, ESM and mESM make a reasonable compromise between smoothness and required repetitions.

Background: Determining how the reflex voluntarily modulates while performing activities of daily living (ADL), and understanding how this modulation might differ between healthy subjects and patients with motor disorders, can aid in developing a rehabilitative program to improve the functional performance of thewrist. The current study designs an ideal experimental protocol that would test reflex modulation as a response to perturbations during a goal-directed movement. Methods: A total of twelve participants participated in the five experiments (mean age 25.9 9 ± 3.6 years in the range 22 - 34, seven men and five women, all right-handed and with no history of neuromuscular impairment). The TU Delft Human Research Ethics Committee gave approval for the current study. The experimental protocol is separated into three pilot experiments, one main experiment and an addendum experiment after proven effectiveness of the protocol. The main experiment is conducted with ten subjects. The design process was iterative. The experiments were tested, designed and implemented on a wrist manipulator (PoPe). The transient perturbations are designed to be Ramp-and-Hold (R&H) perturbations of amplitude 0.08rad, 2rad/s with a 180ms hold, causinga stretch reflex response in the flexor carpi radialis (FCR). Results: The main experiment showed that the short-latency (M1) and long-latency (M2) areas in the FCR significantly differ with respect to the posture at which a perturbation is elicited. Upon showing the effectiveness, the experiment is further improved with the implementation of continuous pseudo-random binary signal (PRBS) perturbations to allow for continuous measuring of the stretch reflex, and thereby increasing protocol efficiency. Conclusions: The designed experimental protocol is, therefore, effective in observing reflex modulation during a goal-directed movement performed by the wrist.

The ability to control and adapt joint stiffness is essential in human motor control. Both control loops on the spinal as well as cortical level are likely to play a role in this regulation. However, the cortical mechanisms involved with the online adaptive control of joint stiffness remain largely unresolved. This study aimed at identifying cortical areas associated with the process of joint stiffness modulation using electroencephalography (EEG). EEG was recorded in twelve healthy right-handed individuals performing an active posture control task while receiving continuous random force perturbations applied using a robotic manipulator. To provoke a change in the neuromuscular control strategy, i.e. adaptation of joint stiffness, external viscous loads were applied or removed between tasks or instantaneously within tasks. Linear time-invariant system identification techniques were used to estimate joint stiffness between tasks. Cortical oscillatory dynamics were analysed for eight clusters of independent components, which were found using independent component analysis (ICA) and a subsequent dipole source localization method. Power spectral analysis of the time-invariant trials revealed significant enhancement of theta and beta oscillations in the left sensorimotor cortex (S1/M1) and suppression of delta rhythms in the supplementary motor area (SMA) when external damping was present. Analysis of event-related spectral perturbations (ERSPs) in the time-variant trials revealed delta and theta band enhancement in the SMA and sensorimotor cortex following immediately after external damping removal, as well as broadband enhancement in the prefrontal cortex (anterior cingulate cortex (ACC)). Moreover, we found more pronounced modulations in cortical activity with an unexpected decrease in external damping as compared to an increase in viscous loads. These results suggest that multiple cortical areas are likely to be involved in modulating joint stiffness when stability is at risk being the sensorimotor cortex, SMA and prefrontal cortex, whereas adaptive processes in response to increased stability margins might be regulated on a subcortical level.