1,720,972 research outputs found
Tensors for neuroimaging: A review on applications of tensors to unravel the mysteries of the brain
No full textNeuroimaging techniques are used to image the structure and function of the nervous system for medicine, psychology, and neuroscience research. Brain data are inherently multidimensional and complex, and the recent advances in neuroimaging allow the acquisition of brain signals at an increasing spatiotemporal resolution. Being able to process the resulting large-scale data and capturing the multiway structure of the brain, tensor-based analyses are well suited for a variety of neuroimaging applications. In this review, we provide a comprehensive overview of successful tensor-based solutions used in the field of neuroimaging discuss practical challenges and the future of tensors in medical technology.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
IMU-based adaptive filtering for movement artifact removal from ecg recorded with a single lead wearable device
No full textBackground and Objectives: Wearable devices (WDs) capable of recording electrocardiograms (ECGs) for prolonged periods in ambulatory settings offer the possibility of detecting non-predictable events such as epileptic seizures and atrial fibrillation. Nevertheless, these systems suffer from additional noise sources, such as movement artifacts (MA).Several adaptive (AF) algorithms have been proposed in the literature to suppress movement artifacts from ECG without consistent results. Adaptive algorithms for signal enhancement require a reference signal correlated with the noise and not correlated with the signal of interest. This correlation can change significantly depending on the absolute and relative location of the electrodes and the location of the reference sensor (i.e., accelerometer, gyroscope); objectively measuring the correlation is not a trivial problem.For this reason, first, we used an algorithm to obtain a rough estimate of the movement artifacts from the recorded ECG to calculate the correlation between them and the available reference signals (three-axis accelerometer and three-axis gyroscope) and selected the one with the highest correlation. Then, we compare three adaptive filter algorithms using the signal-to-noise ratio (SNR) coefficient as the evaluation parameter.Methods: For testing the implemented adaptive filters, first, we used a simulated signal, then data from an openonline database, and last, a single lead ECG wearable device called AFi® (Praxa Sense™, The Netherlands) with an embedded IMU. To induce the movement artifacts in a controlled setting, participants performed a set of predefined movements within three intensities; high (running, jumping), moderate (torso rotations, pushups), and low (walking). Then we analyzed the recorded data offline as follows:1. Test the correlation between noise and the IMU components, and select the component with the highest correlation to be used as a reference input for the adaptive filters.2. Compare three adaptive filters in terms of SNR improvement; the Least means squares (LMS), the Normalized least means squares (NLMS), and the Recursive least squares RLS.3. Filter the selected reference input with wavelet decomposition, and test if there is a filter performance improvement in SNR.Results: The implemented adaptive filters performed as expected with the simulated signals, but they showed very poor results once we used them on real data.The RLS filter showed superior performance than the least mean squares-based filters in terms of convergence speed and the root mean squared error minimization. Nevertheless, it requires a high correlation (ρ) above ρ>0.8 between the reference input and the undesired signal or noise to provide a proper signal enhancement and morphology recovery.The low correlation between the movement artifacts and the components of the IMU used as a reference input for the adaptive filters affected the filter performance heavily. Filtering the reference input with the wavelet decomposition did not improve the correlation or the filter performance.Conclusions The correlation between ECG motion artifacts and movement recorded with inertial sensors appeared to be low and inconsistent. Given this, adaptive filters using inertial sensors as reference input are unsuitable for removing ECG movement artifacts.Biomedical Engineerin
BCI user interface
No full textThis document presents the development of a user interface for an EEG motor imagery based Brain-Computer Interface (BCI) as the interface subgroup. The aim of this subgroup in the project was to design and implement a graphical user interface (GUI) incorporating visual neurofeedback to enhance the accuracy of the decode algorithm, developed by the other subgroup, during the calibration/training stage for the user such that the user will have better motor imagery control using the GUI. The GUI features two interactive games, namely pong and breakout, and incorporates topographic maps displaying the user’s brain activity. The primary goal of these maps was to improve the training process of the algorithm for each individual user. Additionally, the thesis explores the feasibility of incorporating steady-state visually evoked potential (SSVEP) elements in the games or for creating a new game that combines motor imagery and SSVEP elements allowing for more complex games to be played thus positively affecting the user experience.Bachelor Graduation ProjectElectrical Engineerin
Tensor decomposition for Independent Component Analysis: Through implicit cumulant tensor manipulation
No full textBlind Source Separation (BSS), the separation of latent source components from observed mixtures, is relevant to many fields of expertise such as neuro-imaging, economics and machine learning. Reliable estimates of the sources can be obtained through diagonalization of the cumulant tensor, i.e., a fourth-order symmetric multi-linear array containing the cross-kurtosis values of observed mixtures. The downside of such diagonalization methods is that they scale quartically with the increase of the amount of source components to estimate due to the tensor’s quartic size. Tensor decomposition can simultaneously diagonalize the cumulant tensor and address its size. However, it does not resolve the scalability issue due to the restriction of having to first explicitly compute the tensor.It is studied how decomposing the cumulant tensor in implicit fashion can be used to solve the BSS problem while simultaneously addressing its scalablity issue. A class of implicit cumulant tensor decomposition algorithms is derived which scale more favorably than their explicit counterparts in terms of either computational cost, storage cost or both. Firstly, a novel QR-Tensor algorithm (QRT) is introduced which allows for the simultaneous diagonalization of a tensor’s outer-slices. It is theoretically shown how an implicit version of the QRT algorithm can be used to solve the BSS problem at a linearly scaling computational cost. Secondly, a fixed-point Canonical Polyadic Decomposition (CPD) iteration method is presented. It reduces the computational complexity from a quartic dependence to a linear dependence on the amount of signals to estimate. The source estimation performance of the devised implicit decomposition methods is compared to that of the state-of-the-art FastICA for an artificial linear BSS problem.Results show that both fixed-point CPD and QRT are superior to FastICA when it comes to the computation time needed to reach convergence, while producing estimated sources of similar quality. It is shown that when the amount of sources to estimate is increased both QRT and FastICA struggle to converge. In contrast, the fixed-point CPD method converges within a consistent amount of iterations, suggesting a method more suitable for the estimation of a large amount of sources.https://github.com/padenarie/Independent-component-analysis-through-implicit-cumulant-tensor-decomposition.git Github repository containing implementations and experiment code.Mechanical Engineering | Systems and Contro
Investigation of focal epilepsy using graph signal processing
No full textEpilepsy is one of the most common neurological disorders worldwide. Its manifestations, the seizures, are due to a group of neurons' abnormal and synchronous activity. The unpredictable nature of these events hinders the quality of life of those affected. In particular, focal seizures show a localized onset of the abnormal activity and are the most common ones. Correct detection of the episodes can help clinicians to give the best medical treatments. This research project arises from the need to have automatic algorithms for seizure detection with a high number of correctly detected seizures for low false alarm rates. Recent studies have shown disorganization in how brain areas interact with each other before and during a seizure. We decided to model this change in connectivity patterns by inferring graphs from EEG recordings of epileptic patients. We work with seventeen subjects suffering from focal epilepsy, and we build, for each of them, a graph of the activity preceding (preictal) and during (ictal) a seizure. After that, we exploit techniques from graph signal processing to build a detector for seizures. Last, we analyze the density of connections of the inferred graphs to indicate the seizure onsets. The obtained results are unsuitable for real-life applications, but they are a starting point for further research. Furthermore, we find that most the proposed ictal or preictal graphs show less connections in the nodes involved with the seizure onset.Electrical Engineering | Signals and System
Using Tensor Decompositions To Obtain Biomarkers From Auditory Event-Related Potentials
No full textBrain 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.Electrical Engineering | Signals and System
Radar-based heartbeat estimation for indoor healthcare applications
No full textWith the aging population, the demand for healthcare and related services is increasing and, for this reason, technologies for remote patient monitoring are developing, aiming at indoor scenarios. Remote patient monitoring can help capture the clinical data of patients at home, which can save time and money, specifically reducing the need for hospitalization by potentially detecting health-related issues before they become too serious.The non-contact radar-based technology can be applied in the remote patient monitoring system for detecting vital signs. Radars are suitable for applications at home because they are non-invasive, robust in changing lighting and temperature, and suitable for patients with skin irritation. Heartbeat and respiration are critical clinical data for the diagnosis of the disease. The study of respiration frequency estimation was explored by previous work, such as the MSc thesis in \cite{Maxthesis}. Building on that work, this project proposes a pipeline to measure the heartbeat frequency and cancel the random body movement. The impact of different orientations is also studied. The phase history difference of the chest displacement due to vital signs is extracted, and the wavelet transform is used to separate heartbeat and respiration signals. Different methods are tested to calculate the heartbeat frequency in the time and frequency domain. The RBM is detected by the energy threshold of the phase difference, and the intervals with the RBM are discarded.The simulation and experimental results indicate that the proposed processing pipeline can work on the radar data.Electrical Engineerin
Epileptic Seizure Detection using a Tensor-Network Kalman Filter for LS-SVMs
No full textEpilepsy is one of the most common neurological conditions, affecting nearly 1% of the global population. It is defined by the seemingly random occurrence of spontaneous seizures. Anti-epileptic drugs provide adequate treatment for about 70% of patients. The remaining 30%, on the other hand, continue to have seizures, which has a significant impact on their quality of life as they are constantly unsure when these seizures will occur. Reliable seizure detection methods would thus have a significant impact on the lives of these patients. Despite ongoing research efforts involving academia and industry in large international collaborations, epileptic seizure detection and especially prediction is still an unsolved problem. The key to the solution could lie within ultralong-term, reallife datasets that are currently being generated using wearable sensors. However, due to the size of these datasets, conventional learning techniques such as least-square support vector machines (LS-SVMs) can become intractable. Therefore, this work proposes the use of a recently developed tensor network Kalman filtering approach for LS-SVMs (TNKFLSSVM) to detect epileptic seizures [1]. In the TNKF-LSSVM algorithm, the dual problem of the LS-SVM is solved using a recursive Bayesian filtering approach. This way the least-square problem can be solved row-by-row using a Kalman filter, thereby avoiding explicit matrix inversions, while also being able to provide confidence bounds on the estimates. By making use of the tensor-train format [2] to represent the matrices and vectors in the Kalman equations, it is even possible to avoid the construction of the (N + 1) × (N + 1) covariance matrix1. To be able to apply the TNKF-LSSVM algorithm for seizure detection there are still some issues that need to be tackled. One such problem is that the TNKF-LSSVM only performs well when the dataset is properly balanced, which is generally not the case for seizure datasets. Furthermore, for the TNKF-LSSVM to work efficiently for large scale problems the modes of the tensortrains representing the matrices and vectors should be as small as possible, thus it must hold that N + 1 = Q i ni, such that ni is ‘small’ for all i. To overcome both of these challenges we propose using the SMOTE method to oversample the seizure class, such that a balanced training set can be generated that has good factorization properties. Some preliminary results using a small subset of data from a public EEG dataset [3] show that taking the above considerations into account, the TNKF-LSSVM method can have performance that is competitive with a regular LS-SVM. Where the TNKFLSSVM method has the benefit of scaling log-linearly with the size of the dataset (in terms of memory usage) and can provide an uncertainty estimate of the detection. Future work will need 1N is the number of data points in the training set and 1 is added for the bias. to show whether this scaling up works as expected for the entire dataset.Signal Processing System
Tensor-based Hemodynamic Response Estimation in Functional Ultrasound Data
No full textFunctional ultrasound (fUS) is an emerging technique that provides high sensitivity imaging of cerebral blood volume (CBV) changes. As increased metabolic demand of active tissue induces changes in CBV, these changes reflect neuronal activity in the corresponding brain area. The main advantages of this technique are that it can image the entire brain with unprecedented spatial (50-500um) and temporal resolution (10- 100ms), and that it constitutes a potentially portable solution, as opposed to functional magnetic resonance imaging (fMRI), the currently dominant modality in functional brain imaging. The high resolution as well as the plane-wave illumination lead to a large amount of raw ultrasound data per aquisition. The fundamental challenge is that fUS only provides an indirect measure of brain activity through the neurovascular coupling; this system is the link between the local neuronal activity and the resulting blood flow changes and has only partially known dynamic and nonlinear characteristics. Moreover, besides the activity of interest, fUS records a mixture of other ongoing brain activity, physiological artifacts and noise. The goal of this research is to develop tensor-based source separation techniques in order to estimate the brain’s hemodynamic response function (HRF) to stimuli and the activity of interest by learning its nonlinear coupling with the fUS signal.Signal Processing System
EEG based BCI: measurement and quality control
No full textPurposeThe 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.MethodFirst, 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. ResultMerging 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.ConclusionThe 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.Electrical Engineerin
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