Prostate cancer is the most common malignancy among men. To confirm an initial detection by a prostate-specific antigen test, magnetic resonance imaging (MRI) is used, but MRI is costly. Ultrasound is a costeffective imaging modality and shows promising results in diagnosing prostate cancer, especially the dynamic contrast-enhanced ultrasound. Dynamic contrast-enhanced ultrasound (DCEUS) is an imaging modality that allows the imaging of the injected microbubbles by exploiting their non-linear acoustic scatter. Because of their size, comparable to red blood cells, ultrasound contrast agents can flow through the vascular tree down to the microvessels, enabling the visualization and, possibly, quantification of the angiogenic processes associated with cancer growth. Although several techniques are applied to DCEUS to reduce noise, speckle noise still exists.

Speckle noise occurs due to the coherent imaging of many microbubbles in one resolution cell. In existing methods, a low-rank matrix decomposition is applied to the DCEUS acquisitions using singular value decomposition, and the despeckling is done by keeping the highest few singular vectors and values. The DCEUS acquisitions come in a tensor format, rich with higher-order structure. The application of the matrixbased denoising technique does not utilize the original tensor structure. This dissertation focuses on despeckling through higher-order tensor decomposition methods. We tackle the following research question: "How can low-rank tensor decomposition methods be leveraged to effectively denoise DCEUS acquisitions of the prostate for improved prostate cancer detection?" We apply tensor decomposition methods that utilize orthogonal factors. In the spatial domain, the orthogonality allows the separation of the malignant and benign regions and the separation of the tissue and the vasculature. In the time domain, the orthogonality allows for capturing the components that correspond with the bubble movement and rejects the components related to the noise.

Despeckling of DCEUS through low-rank tensor decomposition has not been conducted before, and we propose tensor estimation algorithms for this application by utilizing established tensor decomposition frameworks. We start our research by modeling speckle noise as white Gaussian noise (WGN) with sparse outliers. We assess the performance of convex tensor estimation algorithms through simulation. We propose a novel weighting scheme for the soft-thresholding of the singular values. Instead of iterative thresholding, we can truncate the tensor and deviispeckle the DCEUS acquisitions. We propose a rank estimation method for DCEUS acquisitions. Instead of modeling speckle noise as WGN with sparse outliers, we minimize its negative log-likelihood and propose a gradient-based denoising algorithm.

Next, we investigate the classification performance of prostate cancer by comparing the proposed algorithms with the literature. We use the area under the receiver-operator characteristic curve (ROC-AUC) metric to assess the classification performance. For the voxel-based cancer diagnosis of 94 prostate cancer patients, truncated multilinear singular value decomposition has a better performance for the majority of the prostate cancer markers when the ROC-AUC metric is used. A rank estimation technique incorporating WGN with sparse outliers, followed by truncated multilinear singular value decomposition (tr-MLSVD), is the best-performing denoising method for DCEUS. In the context of the main research question, the cancer diagnosis performance of DCEUS acquisitions improves the majority of the time when a tensor-based denoising technique is used. On average, the tensor-based denoising techniques yield approximately a 1.6% relative improvement in the ROC-AUC metric compared to the literature. This translates to billions of additional correct voxel-level malignancy discriminations in our clinical study, which may significantly impact downstream classification and localization performance.

We conclude with a theoretical study on the lower bound of the tensor decomposition method that performs the best for despeckling DCEUS. We calculate a lower bound for estimating the components of MLSVD when the ranks are known. In general, the CCRB that lower bounds the variance of the unbiased estimates of the components of MLSVD does not exist due to the non-uniqueness of the decomposition. However, when the mode-n singular values are unique, the CCRB exists. Additionally, when the multilinear ranks are high and modal singular values are well-separated, it is a tight bound. Such cases do not typically occur with real data such as DCEUS, highlighting the limited modeling capability of the CCRB.
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Understanding the mechanisms that underpin learning effects remains a central goal in neuroscience, with functional neuroimaging offering a powerful avenue for observing brain activity. This work is situated within the domain of haemodynamic functional neuroimaging, with a specific focus on functional ultrasound (fUS)—a relatively novel modality that combines high spatio-temporal resolution and portability. The goal of this project is to investigate the capacity of tensor-decomposition-based techniques to extract meaningful, interpretable representations of stimulus-evoked learning effects from a multi-subject fUS dataset while being robust to the non-idealities present in said data. While numerous approaches exist for analysing neuroimaging data, many are limited by scalability, interpretability, or an inability to capture changing neurological patterns. This work is thus motivated by the need for methods capable of handling large, dynamic datasets while extracting interpretable results. Tensor decompositions offer such a framework but their ability to capture subject-specific time-varying effects and their application to functional ultrasound is still under-explored. Several tensor decomposition algorithms are examined, assessed in terms of their learning effect extraction capabilities and robustness to non-idealities, and a novel shifted canonical polyadic decomposition variant is developed to address time-varying effects. Results from synthetic data analysis demonstrate that tensor decompositions are robust to non-idealities in functional ultrasound data (to varying degrees) and can recover interpretable latent components. Due to the data's partial scan coverage, no novel signatures of learning were identified in the real data; however, the extracted components further substantiate the potential of these algorithms and highlight a promising direction for analysing learning effects in functional neuroimaging data. ... Read more

Hidden Markov Models (HMMs) are probabilistic models that are widely used in various fields, including machine learning, economics, information theory, neuroimaging, and more. In particular, they are frequently employed in functional Magnetic Resonance Imaging (fMRI) studies, which involve large datasets, to analyze the behavior of brain networks or states. For example, HMMs can help determine whether a patient is healthy.

HMMs represent the probability of transitions between different states of a system, which operates as a discrete Markov chain and is not directly observable. Additionally, they describe the probability of observing specific measurements based on the current state of the model. These probabilities are characterized by a state transition matrix (T), an emission matrix (O), and an initial state distribution (π).

The current methods for fitting HMMs to data primarily utilize the Baum-Welch algorithm, which is a specific type of Expectation Maximization (EM). One of the advantages of Baum-Welch is its ability to be extended to continuous observations or multivariate data. However, the algorithm involves a forward-backward pass during each iteration, which makes it increasingly slow as the size of the dataset grows. Moreover, it can easily become trapped in local minima.

An alternative approach is to use Canonical Polyadic Decomposition (CPD) to decompose a Joint Probability Tensor (JPT). This decomposition allows for the extraction of factor matrices that can be used to calculate the HMM matrices (T, O, π). Compared to the Baum-Welch algorithm, CPD and other JPT decomposition methods tend to be faster. However, they are often sensitive and may suffer from instability if the data does not fully capture the statistical behavior of the underlying HMM. Additionally, methods for JPT decomposition in HMM learning have not yet been extended to multivariate datasets or continuous settings.

To enhance stability and accommodate multivariate data, we propose a novel method that extends JPT decomposition HMM learning to a multivariate setting. This involves using a coupled CPD problem, where each observational sequence has a separate emission matrix (O) but shares a common state transition matrix (T). By transitioning from Baum-Welch to CPD, the process of learning HMMs can be significantly accelerated. Coupled CPD makes HMM learning more robust than Uncoupled CPD by relating the multivariate data through a common transition matrix T and initial distribution π. This improvement should enhance the iterability of HMM methods and make them more suitable for rapid prototyping in scientific research and the aforementioned fields.

We show in the results that Coupled CPD indeed outperforms both Uncoupled CPD and the industry-standard Baum-Welch, in most cases. It has improved stability over both methods, as well as significantly improving the calculation time, and results in higher accuracy when it comes to estimating the HMM matrices. Finally, more future improvements are suggested concerning calculation time and extensions.
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In the last decade, the new functional neuroimaging technique functional ultrasound (fUS) has emerged as a potential new tool for clinical and neuroscientific applications. Unlike several conventional methods for functional brain imaging, fUS offers an unparalleled combination of submillimeter-subsecond spatiotemporal resolution and the ability to penetrate deep into brain tissue and capture large areas of interest. This makes fUS an exceptionally valuable tool for investigating brain function. The principal physiological mechanism utilized by fUS imaging is neurovascular coupling, which connects neuronal activity with local hemodynamic changes. Within the brain, increased neuronal activity results in the dilation of nearby blood vessels, subsequently leading to an increased local blood flow and volume.
In fUS, the increase in blood volume is measured by the intensity of the Doppler signal caused by the Doppler effect. To obtain relevant information from functional neuroimaging data, blind source separation (BSS) is often used. BSS is the separation of a set of source signals from a set of mixed signals, without the aid of information about the source signals or the mixing process. A commonly used BSS method for identifying brain networks and artifacts in functional neuroimaging techniques like fMRI is independent component analysis, which is a low-rank matrix decomposition. This work explores the use of canonical polyadic decomposition (CPD), a low-rank tensor decomposition, for BSS of fUS data. The CPD approximates a 3rd-order tensor, consisting of a space, frequency, and time dimension, by a sum of rank-1 tensors. The proposed method offers three key advantages that make it highly relevant to the field of fUS signal processing: 1) Tensor-based BSS by CPD allows for frequency as a third dimension to complement the spatial and temporal dimensions; 2) Tensor-based BSS by CPD allows for the use of all raw fUS data (compound images) rather than only power doppler images, exploiting more available information; and 3) Tensor-based BSS by CPD allows for a BSS method without strong constraints such as the case with matrix-based methods like independent component analysis. The full processing pipeline developed in this work consists of four distinct stages. In stage one, pre-processing is carried out through SVD filtering, temporal demeaning, and pixel-based normalization. During the SVD filtering, normalization of the singular values is performed, which is novel. In stage two, compression is performed using truncated multilinear singular value decomposition. In stage three, BSS is carried out using CPD on the compressed data. In stage four, stable components are extracted by running the CPD 100 times and clustering the resulting components. These clusters were then averaged to create mean components. The CPD rank was estimated based on two quantifiable aspects: 1) Correlation between mean components and 2) Frequency of occurrence of mean components. The method is entirely data-driven, can be applied to entire raw fUS datasets, and can run on a laptop with standard RAM. Application to task experiment data of a mouse verified that activity that is temporally correlated with the stimulus can be extracted in expected regions. The components also indicate expected functional connectivity, which is often not the case for matrix-based methods. Moreover, frequency spectra showed different characteristics for different types of components, which displays the relevance of this dimension for BSS, especially for nuisance components. In conclusion, the proposed tensor-based blind source separation pipeline for raw fUS data was able to identify artifacts and meaningful neurological components based on distinctive characteristics in the temporal, spatial, and spectral domains. This work serves as a starting point for more advanced denoising, the identification of novel brain networks, and the comparison of brain networks across healthy and pathological conditions. Nevertheless, further research is necessary to ensure the utility of the method. ... Read more

In today's data-driven landscape, the capacity to efficiently process and analyze vast datasets is crucial across various domains, including healthcare, climate modeling, and finance. Despite the growing need for scalable and interpretable machine learning models, traditional approaches, particularly kernel machines, face significant challenges due to the curse of dimensionality. As data complexity increases, the computational and memory demands of kernel machines often become prohibitive, limiting their applicability in high-dimensional applications. This thesis addresses these challenges by investigating the integration of tensor networks (TNs) with kernel machines, aiming to enhance scalability, efficiency without sacrificing predictive power and interpretability.

We propose that TNs, with their ability to represent high-dimensional data through low-rank structures, can effectively alleviate the limitations of kernel machines. Our research is structured around three central inquiries: first, we examine how TNs can accelerate kernel machine scalability while accurately approximating kernel functions; second, we elucidate the theoretical links between TN-constrained kernel machines and Gaussian processes, providing insights into convergence and generalization; finally, we introduce a novel optimization framework characterizing a specific TN, the multi-linear singular value decomposition (MLSVD), in terms of primal and dual problems. ... Read more

Epilepsy is a neurological disorder that affects millions of people worldwide and is characterized by recurrent seizures. Managing epilepsy effectively remains a challenge, particularly for patients who do not respond to medication. Closed-loop neuromodulation systems have emerged as a promising approach for real-time seizure detection and intervention, offering the potential to improve the quality of life for individuals with epilepsy. These systems monitor brain activity and deliver therapeutic stimuli when abnormal patterns, indicative of an impending seizure, are detected. However, the effectiveness of these systems depends heavily on the accuracy and efficiency of the embedded seizure detection algorithms.

Implementing brain-computer interfaces (BCIs) for neuromodulation presents significant challenges, particularly in terms of power consumption, area constraints, and computational complexity. The use of machine learning (ML), especially lightweight classifiers, is critical for overcoming these challenges. ML models can be trained to identify complex seizure patterns with high sensitivity and specificity, but their deployment in implantable devices requires careful consideration of hardware limitations.

This thesis addresses these challenges by employing an oblique tree estimator to develop a low-power, low-area seizure detection classifier that relies solely on temporal features to minimize hardware costs. Specifically, the Sparse Projection Oblique Randomer Forest (SPORF) variant is utilized, to enable multiplier-less classifications, along with a multi-path inference approach thereby reducing memory requirements. The proposed model achieves state-of-the-art performance in both patient-specific (98.78%/ 99.89% and 98.52%/99.91% sensitivity/specificity in the CHB-MIT and ETHZ databases, respectively) and patient-independent (92.26%/98.29% in the CHB-MIT database) seizure detection frameworks. Additionally, post-layout simulations of the synthesized design reveal a power consumption of 1.22 µW and an area of 0.0076 mm², with an energy consumption of 7.58 nJ per classification using 40 nm CMOS technology.
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Using independent vector analysis (IVA) to analyze and find subgroups in functional magnetic resonance imaging (fMRI) and functional ultrasound (fUS) data requires a lot of manual labour. Recently methods like subgroup identification using IVA (SI-IVA) and IVA for common subspace identification (IVA-CS) have tried to reduce this labour through automation. However, both methods did not test for accuracy. This thesis shows through simulations that these proposed methods are not accurate or robust enough to be trusted and that spectral clustering is a better alternative for automatic subgroup identification. Spectral clustering is then incorporated into the analysis of experimental fUS data of two groups of mice to try and identify these automatically. In this analysis, adaptive constrained IVA (acIVA) was used to incorporate references, further improving the interpretability of the results as components are directly linked to prior constraints.
However, applying subgroup analysis showed that the mice could not be clustered based on their response to the stimuli. Still, spectral clustering is more accurate in the simulations making it a promising alternative for automatic subgroup identification. Furthermore, combining spectral clustering with acIVA makes the results more interpretable due to constrained components not being subject to permutation ambiguity. ... Read more

At the child brain facility at the Erasmus Medical Centre, multiple tests are performed with children who have one of several disorders. Two of these tests are done with electroencephalogram measurements and are called mismatch negativity and acoustic change complex. After a signal processing pipeline, the EEG measurements from these tests are shown as waveforms called event-related potentials. The goal of these measurements is to see if there is any relation between the waveforms and the disorder, age and other information about the subjects.\newline

These waveforms are measured for each subject, EEG electrode, and in the case of MMN, for different stimuli, which naturally results in a tensor data structure. Algorithms for discriminant analysis and regression that are developed to be applied to tensors are described and altered to take into account the properties of the EEG data. Discriminant analysis can be used to improve classification algorithms that distinguish disorders, while regression can be used to predict variables such as test scores based on the measured data. The algorithms are first tested on simulated data, which shows they can have some merit. Classification rates improve in most simulated cases when the discriminant analysis is applied to the data. Regression can also reliably predict variables when strong correlations are present between the input tensor and output variable. Based on the data from the child brain facility, the discriminant analysis still improves classification rates in some cases, but not as significantly as on the simulated data. Regression using the algorithms described in this thesis is not useful in predicting test scores from other experiments done with the subjects. \newline

The algorithms are also dissected to discover which specific features in the data tensor are weighted heavier by the algorithms. This is done to gain new insights into the differences between the disorders. When comparing the weights that are used for the simulated data with the features that are of importance, there is some relation, but not a very strong one. When the input tensor is however segmented in the time mode, the times of interest can be identified. The regression algorithm also resulted in weights that can be analysed to look at when and where the measurements relate to the output, but this did not show any interesting results. For real data, the segmented tensor resulted in some interesting insights about the differences between the various disorders.\newline

For the current dataset, the discriminant analysis algorithms do improve classification rates, but not by much. The features weighted the most by this algorithm in combination with a segmented tensor might give some insight into the disorders. The tensor regression methods do not work to predict a test score and do not give new insights into the disorders. ... Read more

This thesis investigates whether an EEG headset can be used to distinguish motor imagery signals in real time for a Brain Computer Interface (BCI).The specific EEG headset used for this project is the gtec Unicorn Hybrid Black. The aim of this subgroup is to stream the data in real time, preprocess the data, and create a training dataset using recordings from subjects. The sample from the EEG headset is streamed to a laptop using a live data streaming framework called Labstreaminglayer. The data is then filtered using frequency filters and ICA to remove noise and artifacts. Finally, ERDS plots are used to check the signal quality of the recordings. Recordings of sufficient quality should have (de)synchronisation peaks after the prompt is displayed. Before the recordings are made, an experimental setup is set up. This includes prompts with four different movements that the subject is asked to imagine. The data is sent to a GUI to be visualized with various graphs. It is also sent to a machine learning model to classify the movements. It was concluded that the subsystem could successfully stream data from the cap, process the data, and verify the quality of the data. Using ERDS plots, it was verified that some, but not all, MI actions are distinguishable for one individual. However, further verification of other individuals is required to conclude whether this is a systematic problem or is due to the individual.
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This thesis details the theoretical background and development process of a classification model for electroencephalogram-based (EEG) motor imagery (MI) signals, to be used in a brain-computer interface (BCI) system. This project was undertaken in order to demonstrate the possibility of distinguishing MI-EEG signals acquired using the g.tec Unicorn Hybrid Black EEG measurement cap. The classification model devised in this thesis is a hybrid deep neural network model, which combines a convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network (RNN) in parallel, closing with a fully-connected (FC) layer. Much experimentation and research was needed to create this model, and this is extensively discussed within the thesis. The classification model produced for this thesis is one part of a complete end-to-end BCI system, which also entails a measurement and data processing procedure, and the development of a graphical user interface which provides visual feedback to the user.

On publicly available datasets, the classification model produced promising performance, achieving 55% on the BCI Competition IV-2a (4 class) dataset and 78% on the BCI IV-2a (2 class) dataset. The model has not yet been tested on self-collected data. ... Read more

The thesis focuses on developing a brain-computer interface (BCI) aimed at differentiating between left-hand and right-hand motor imagery using EEG signals. Its primary objective was to create a scalable and user-specific model for accurately interpreting motor imagery tasks. The study involved the development of one SVM and two advanced deep learning models: the Advanced-EEG-MI-CNN and the Advanced-EEG-MI-CNN-LSTM-Transformer. These models were tested on the Physionet and BCIC IV 2a datasets, as well as on self-gathered data. Results indicated that the SVM model achieved an accuracy of 65% for all subjects and 78% for one subject, while the Advanced-EEG-MI-CNN model achieved an accuracy of 65% for all subjects and 78% for a single subject on average. The Advanced-
EEG-MI-CNN-LSTM-Transformer demonstrated promising results in capturing temporal dependencies, with an accuracy of 80% for all subjects and 92% for a single subject on average. Future work includes enhancing user-specific model implementation, applying Independent Component Analysis (ICA) for cleaner data, and improving data pre-processing to minimize noise. ... Read more

The main goal of this project is to utilize a commercially available OpenBCI Ultracortex IV for the measurement of Electroencephalogram(EEG) signals. A pipeline consisting of preprocessing, classification and extraction is employed to transform the motor execution EEG signal into a singular Left or Right output. This output is then further displayed on an Interface that offers the option to either calibrate or play a simple game.

The Ultracortex and relevant software were used to determine the sensor layout, with the placement of the sensors focused on areas which exhibited high cortical activity during motor execution. Experiments were strategically designed to optimize our chance of successful readings and OpenVIBE was used in conjecture with preprocessing filters to save the raw and filtered data which was further sent to the Machine Learning group.

The collected data was analyzed through Spectrograms, Power Spectral Density(PSD) and Event-Related Desynchronization/Synchronization(ERDS) plots. The analysis aimed to confirm whether the desired activity occurred and whether the observed patterns resemble those documented in other research papers.

The data from the headset is live-streamed to the interface via Lab Streaming Layer(LSL) where it undergoes further filtering before being sent to the Machine learning group. This process was done through python libraries which then allowed for efficient and effective communication between the other groups. ... Read more

In this thesis, the development of a graphical user interface for a brain computer interface (BCI) system is discussed. This system is based on electroencephalographic (EEG) signals from motor imagery (MI). BCI applications for low consumer-grade are very limited, since most of the applications are towards medical use or games. This limits the potential BCI can have, since it only applies to one specific environment. The goal of this thesis is to show that a low consumer-grade BCI can be made, ranging from educational to personal use, with the needed quality and usability. This is achieved by showing the possibility of distinguishing MI signals. Specifically in this thesis, left hand, right hand, tongue and feet. This can be done and utilized through a machine learning algorithm. Presentation methods for this are displaying EEG signals and using demos that show the purpose of the BCI system. An example of such a demo is moving a simulated computer cursor. In order to have this system for a wider public, the interface should be able to adapt to each individual user using personalized machine learning models. Due to the distinct, personal EEG patterns, the accuracy of classifying the MI-EEG signals using personalized ML models is much higher than with an ML model trained solely on a public dataset. For more advanced personal use of the system, the interface contains everything needed regarding EEG signals, including real-time data streaming in order to directly see change in EEG signals and to help the user detect potential errors in the measurement setup ... Read more

The purpose of this thesis is to study the possibility of developing a computer interface that makes use of electroencephalogram (EEG) signals in order to improve the interaction between humans and computers. The major objective is to develop a system that is able to read brain activity in real time and then transform that information into instructions that can be executed on a computer. This study combines electroencephalogram (EEG) technology with sophisticated machine learning algorithms in order to develop an interface that is fluid, responsive, and user-friendly. Among the most important goals are the development of methods for robust signal processing, the design of an interface that is easy to use, and the installation of tools for real-time data presentation and analysis. A comprehensive set of tests was performed on the system, which revealed considerable improvements in terms of accuracy, responsiveness, and the overall user experience. This research makes a significant contribution to the expanding field of neurotechnology by providing an interface that is simple to use and may be used as an inspiration for a broad variety of purposes, including medical diagnosis and treatment, as well as entertainment and other games. ... Read more

The brain stands as the most powerful processor in the known universe. It generates a continuous stream of electrical and chemical signals that underpin every thought, sensation, and action. Our past efforts in decoding these signals have made it possible to diagnose and treat many neurological disorders, helped us gain a deeper understanding of cognitive processes and consciousness, and paved the way for brain-computer interfaces. To take another step forward in our long but rewarding journey of discovering the brain's complex organization, we rely on advances in imaging technologies and signal processing.

Functional ultrasound is a neuroimaging technique that has emerged in the last decade, and has gained remarkable attention since then. The popularity of this technique stems from its portability, high resolution and affordability. Functional ultrasound can detect subtle fluctuations in local blood dynamics, which serve as delayed indicators of the underlying changes in neuronal activity. The goal of this thesis is to develop novel signal models and processing algorithms that can reveal the spatial and temporal characteristics of hemodynamic activity induced by external stimuli using functional ultrasound.

Existing techniques that explore how the brain reacts in response to stimuli model the design variables using a linear time-invariant system with binarized input representations, marking when a stimulus is on or off. However, experimental evidence suggests that the brain reacts in a more intricate manner. While some regions exhibit consistent responses to repeated stimuli, others can show substantial variation even when exposed to the same stimulus seconds apart. In our in-vivo experiments, we particularly focus on key regions within the mouse visual processing pathway, which are analogous to those in the human brain. We track how visual information flows across these areas, and propose methods that can incorporate the spatiotemporal variability of brain responses when identifying evoked activity. Using these methods, we show that functional ultrasound can capture the dynamic nature of brain responses with high spatial and temporal resolution, and provide us with further insights into the functional organization of the brain. Future directions of this dissertation include multimodal processing of the functional ultrasound signal together with neuronal activity, aiming to enhance our understanding of neurovascular coupling. ... Read more

Brain disorders in children pose significant challenges to their development, impacting cognition, speech, movement, and behavior. The uncertainty surrounding prognostic information at the time of diagnosis leaves families with numerous questions about the future. The Child Brain Lab at Erasmus MC Sophia Children's Hospital conducts IQ, electroencephalogram (EEG), speech, and movement tests in playful environments, enhancing scientific research and healthcare practices for a better understanding of disease progression.

The Otolaryngology department at the Child Brain Lab focuses on auditory-related potentials (ERPs) obtained from EEG measurements to predict the future development of children with brain disorders. Analyzing ERP data from experiments like Mismatch Negativity (MMN) and Acoustic Change Complex (ACC) yields insights into developmental trajectories and connections between hearing, language, and brain development.

This thesis aims to explore alternative methodologies for extracting comprehensive information from ERPs, overcoming limitations of the commonly used peak amplitude and latency analysis. Tensor decompositions are employed to exploit structural information present in the data, using data fusion methods to combine multiple datasets for improved classification and deeper insights into group differences.

Simulations on artificial ERP data demonstrate that data fusion methods perform better on two ERP tensors compared to single tensor decomposition when group differences are shared between datasets. On a real dataset, tensor decompositions show promise for classifying subjects based on auditory event-related potentials while giving more insights into the neurological sources.

This report proposes an alternative method for analyzing ERP data, highlighting the potential of tensor decompositions and data fusion techniques. ... Read more

Micro-Doppler (µDoppler) ultrasound imaging is a high frame rate ultrasound imaging modality that provides high spatiotemporal resolution ultrasound images of blood flow. It is sensitive to slow blood flow and particularly suitable for capturing fast-changing phenomena like rapid blood flow. Clutter filtering is an essential step in µDoppler data processing to reject tissue clutter signals and keep blood flow information as much as possible. 3D freehand µDoppler imaging is an emerging ultrasound technique that can construct full spatial vasculature images with a panoramic view that conventional 2D ultrasound is not able to provide. As freehand implies the continuous and nonuniform movement of the probe, it becomes more challenging for clutter filtering to acquire high-quality images.

This thesis explores and compares different state-of-art clutter filtering techniques on freehand in-vivo µDoppler imaging of the human brain. Specifically, Singular Value Decomposition (SVD), Robust Principle Component Analysis (Robust PCA), and Independent Component Analysis (ICA) clutter filtering techniques have been investigated. The aim is to test and compare their performance on in-vivo µDoppler ultrasound data with freehand probe movement and understand how freehand motion affects the threshold selection criteria. Besides that, a newly proposed method that combines ICA clutter filtering and clustering is included in this thesis to bring another perspective for sorting independent components corresponding to blood flow and rejecting unwanted ones consisting mostly of tissue clutter signals. ... Read more

Purpose
The main purpose of this report is to find out whether the OpenBCI "Ultracortex Mark IV" Electroencephalogram (EEG) headset is capable of differentiating EEG-signals of motor execution from neutral state with recorded data and to find out whether it can differ motor executions between left and right hand. Next to that, it is to be determined whether the OpenBCI headset was the optimal one for this purpose.

Method
First, the specifications of different headsets were compared. Afterwards, a montage of the electrodes was designed to detect motor execution and motor imagery, mainly centered around the locations C3, Cz and C4, on the top of the scalp. The software "Openvibe" was used to extract data from the headset during experiments and to record it in a csv file. A subject was asked to follow a video with a sound cue followed by a visual cue instructing to move either its left hand or right hand.


Result
Merging the left and right hand trial data together, the result is that the headset shows in the alpha band (7-12 Hz) mostly a decrease (ERD) in magnitude around the visual cue, sometimes followed by a bigger increase in magnitude (ERS). Looking at the extremes after the cue, it is seen that mostly the difference in magnitude is around a factor 1.5 compared to the average magnitude of before the visual cue. Splitting the trial data between left and right hand, similar results can be seen, but one hand produces slightly more ERD or ERS than the other hand depending on the position of the electrode on the left or right hemisphere of the brain.

Conclusion
The OpenBCI headset can in fact detect a difference between movement of the hands and the neutral state. Differentiating between the movements of left and right hands seems possible from the results, but the difference in the signal of left and right hand is minimal. It is recommended to repeat the experiment with more trials and different subjects to get a more solid conclusion. ... Read more

In the context of designing a real-time brain-computer interface for playing a game using the OpenBCI Ultracortex "Mark IV" headset, this paper focuses on the work of the decoding subgroup. The primary responsibility is to analyse EEG data retrieved from the OpenBCI headset and classify the intention of the user. Our objective is to achieve a high-accuracy classification of the EEG signals. The paper is structured into three main sections: preprocessing, feature extraction, and classification. Multiple methods for preprocessing and classification of motor execution EEG signals will be analysed, striving to contribute to the real-time implementation of the project. The results of our work provides valuable insights for future research and development in this field. ... Read more