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- Wilroth, Johanna, 1994-
(författare)
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Exploring Auditory Attention Using EEG
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Listeners with normal-hearing often overlook their ability to comprehend speech in noisy environments effortlessly. Our brain’s adeptness at identifying and amplifying attended voices while suppressing unwanted background noise, known as the cocktail party problem, has been extensively researched for decades. Yet, many aspects of this complex puzzle remain unsolved and listeners with hearing-impairment still struggle to focus on a specific speaker in noisy environments. While recent intelligent hearing aids have improved noise suppression, the problem of deciding which speaker to enhance remains unsolved, leading to discomfort for many hearing aid users in noisy environments.In this thesis, we explore the complexities of the human brain in challenging auditory environments. Two datasets are investigated where participants were tasked to selectively attend to one of two competing voices, replicating a cocktail-party scenario. The auditory stimuli trigger neurons to generate electrical signals that propagate in all directions. When a substantial number of neurons fire simultaneously, their collective electrical signal becomes detectable by small electrodes placed on the head. This method of measuring brain activity, known as electroencephalography (EEG), holds potential to provide feedback to the hearing aids, enabling adjustments to enhance attended voice(s).EEG data is often noisy, incorporating neural responses with artifacts such as muscle movements, eye blinks and heartbeats. In the first contribution of this thesis, we focus on comparing different manual and automatic artifact-rejection techniques and assessing their impact on auditory attention decoding (AAD).While EEG measurements offer high temporal accuracy, spatial resolution is inferior compared to alternative tools like magnetoencephalography (MEG). This difference poses a considerable challenge for source localization with EEG data. In the second contribution of this thesis, we demonstrate anticipated activity in the auditory cortex using EEG data from a single listener, employing Neuro-Current Response Functions (NCRFs). This method, previously evaluated only with MEG data, holds significant promise in hearing aid development.EEG data may involve both linear and nonlinear components due to the propagation of the electrical signals through brain tissue, skull, and scalp with varying conductivities. In the third contribution, we aim to enhance source localization by introducing a binning-based nonlinear detection and compensation method. The results suggest that compensating for some nonlinear components produces more precise and synchronized source localization compared to original EEG data.In the fourth contribution, we present a novel domain adaptation framework that improves AAD performances for listeners with initially low classification accuracy. This framework focuses on classifying the direction (left or right) of attended speech and shows a significant accuracy improvement when transporting poor data from one listener to the domain of good data from different listeners.Taken together, the contributions of this thesis hold promise for improving the lives of hearing-impaired individuals by closing the loop between the brain and hearing aids.
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| 2. |
- Broman, David, 1977-
(författare)
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Meta-Languages and Semantics for Equation-Based Modeling and Simulation
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Performing computational experiments on mathematical models instead of building and testing physical prototypes can drastically reduce the develop cost for complex systems such as automobiles, aircraft, and powerplants. In the past three decades, a new category of equation-based modeling languages has appeared that is based on acausal and object-oriented modeling principles, enabling good reuse of models. However, the modeling languages within this category have grown to be large and complex, where the specifications of the language's semantics are informally defined, typically described in natural languages. The lack of a formal semantics makes these languages hard to interpret unambiguously and to reason about. This thesis concerns the problem of designing the semantics of such equation-based modeling languages in a way that allows formal reasoning and increased correctness. The work is presented in two parts.In the first part we study the state-of-the-art modeling language Modelica. We analyze the concepts of types in Modelica and conclude that there are two kinds of type concepts: class types and object types. Moreover, a concept called structural constraint delta is proposed, which is used for isolating the faults of an over- or under-determined model.In the second part, we introduce a new research language called the Modeling Kernel Language (MKL). By introducing the concept of higher-order acausal models (HOAMs), we show that it is possible to create expressive modeling libraries in a manner analogous to Modelica, but using a small and simple language concept. In contrast to the current state-of-the-art modeling languages, the semantics of how to use the models, including meta operations on models, are also specified in MKL libraries. This enables extensible formal executable specifications where important language features are expressed through libraries rather than by adding completely new language constructs. MKL is a statically typed language based on a typed lambda calculus. We define the core of the language formally using operational semantics and prove type safety. An MKL interpreter is implemented and verified in comparison with a Modelica environment.
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| 3. |
- Skoglund, Martin, 1981-
(författare)
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Inertial Navigation and Mapping for Autonomous Vehicles
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below.We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a so-called simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship.Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined.The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications.We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear least-squares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKF-SLAM.We show how NLS-SLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKF-SLAM. The results obtained using NLS-SLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended Rauch-Tung-Striebel smoother while the second problem is solved with a quasi-Newton method. The results using EM-SLAM are better than NLS-SLAM both in terms of accuracy and complexity.
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| 4. |
- Woksepp, Hanna, 1981-
(författare)
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Individualized treatment and control of bacterial infections
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Infectious diseases cause substantial morbidity and mortality, exacerbated by increasing antibiotic resistance. In critically ill patients, recent studies indicate a substantial variability in β-lactam antibiotic levels when standardized dosing is applied. New methods for characterizing nosocomial outbreaks of bacterial infections are needed to limit transmission. The goals of this thesis were to investigate new strategies towards individualized treatment and control of bacterial infections. In Paper I we confirmed high variability in β-lactam antibiotic levels among intensive care unit (ICU) patients from southeastern Sweden, where 45 % failed to reach treatment targets (100 % fT>MIC). Augmented renal clearance and establishing the minimum inhibitory concentration of the bacteria were important for evaluating the risk of not attaining adequate drug levels. In Paper II a rapid ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for simultaneous quantification of 11 commonly used antibiotics was developed and tested in clinical samples. Performance goals (CV<15%) were reached. A microbiological method for quantification of β-lactam antibiotics in serum was developed in Paper III. The method could be important for hospitals without access to an LC-MS method. Paper IV and Paper V investigated ligation-mediated qPCR with high resolution melt analysis (LMqPCR HRMA), for transmission investigation of extended spectrum β-lactamase (ESBL)-producing E. coli and other common bacterial pathogens. Results comparable to the reference method (PFGE) could be achieved within one day in a closed system and confirmed a nosocomial outbreak in Kalmar County. In Paper VI whole genome sequencing followed by bioinformatic analysis resolved transmission links within a nosocomial outbreak due to improved discriminatory power compared to LMqPCR HRMA.The high proportion of ICU patients with insufficient β-lactam drug levels emphasizes the need for individualized treatment by therapeutic drug monitoring (TDM). TDM is enabled by a highly sensitive method, such as UPLC-MS/MS, but if unavailable, also by a microbial method. Molecular typing methods used for transmission investigation can detect nosocomial outbreaks. LMqPCR HRMA can be used for screening purposes. For enhanced resolution, whole genome sequencing should be used, but always together with a rigorous epidemiological investigation.
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| 5. |
- Hol, Jeroen Diederik, 1981-
(författare)
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Pose Estimation and Calibration Algorithms for Vision and Inertial Sensors
- 2008
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with estimating position and orientation in real-time, using measurements from vision and inertial sensors. A system has been developed to solve this problem in unprepared environments, assuming that a map or scene model is available. Compared to ‘camera-only’ systems, the combination of the complementary sensors yields an accurate and robust system which can handle periods with uninformative or no vision data and reduces the need for high frequency vision updates.The system achieves real-time pose estimation by fusing vision and inertial sensors using the framework of nonlinear state estimation for which state space models have been developed. The performance of the system has been evaluated using an augmented reality application where the output from the system is used to superimpose virtual graphics on the live video stream. Furthermore, experiments have been performed where an industrial robot providing ground truth data is used to move the sensor unit. In both cases the system performed well.Calibration of the relative position and orientation of the camera and the inertial sensor turn out to be essential for proper operation of the system. A new and easy-to-use algorithm for estimating these has been developed using a gray-box system identification approach. Experimental results show that the algorithm works well in practice.
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