Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

An advanced method for eliminating impacts from frequency deviations in power system signal processing

Frequency deviation is a common problem for power system signal processing. Many power system measurements are carried out in a fixed sampling rate assuming the system operates in its nominal frequency (50 or 60 Hz). However, the actual frequency may deviate from the normal value from time to time due to various reasons such as disturbances and subsequent system transients. Measurement of signals based on a fixed sampling rate may introduce errors under such situations. In order to achieve high precision signal measurement appropriate algorithms need to be employed to reduce the impact from frequency deviation in the power system data acquisition process. This paper proposes an advanced algorithm to enhance Fourier transform for power system signal processing. The algorithm is able to effectively correct frequency deviation under fixed sampling rate. Accurate measurement of power system signals is essential for the secure and reliable operation of power systems. The algorithm is readily applicable to such occasions where signal processing is affected by frequency deviation. Both mathematical proof and numerical simulation are given in this paper to illustrate robustness and effectiveness of the proposed algorithm. Crown Copyright (C) 2003 Published by Elsevier Science B.V. All rights reserved.

Design of improved rail-to-rail low-distortion and low-stress switches in advanced CMOS technologies

This paper describes the efficient design of an improved and dedicated switched-capacitor (SC) circuit capable of linearizing CMOS switches to allow SC circuits to reach low distortion levels. The described circuit (SC linearization control circuit, SLC) has the advantage over conventional clock-bootstrapping circuits of exhibiting low-stress, since large gate voltages are avoided. This paper presents exhaustive corner simulation results of a SC sample-and-hold (S/H) circuit which employs the proposed and optimized circuits, together with the experimental evaluation of a complete 10-bit ADC utilizing the referred S/H circuit. These results show that the SLC circuits can reduce distortion and increase dynamic linearity above 12 bits for wide input signal bandwidths.

ECG denoising based on adaptive signal processing technique

An Electrocardiogram (ECG) monitoring system deals with several challenges related with noise sources. The main goal of this text was the study of Adaptive Signal Processing Algorithms for ECG noise reduction when applied to real signals. This document presents an adaptive ltering technique based on Least Mean Square (LMS) algorithm to remove the artefacts caused by electromyography (EMG) and power line noise into ECG signal. For this experiments it was used real noise signals, mainly to observe the di erence between real noise and simulated noise sources. It was obtained very good results due to the ability of noise removing that can be reached with this technique. A recolha de sinais electrocardiogr a cos (ECG) sofre de diversos problemas relacionados com ru dos. O objectivo deste trabalho foi o estudo de algoritmos adaptativos para processamento digital de sinal, para redu c~ao de ru do em sinais ECG reais. Este texto apresenta uma t ecnica de redu c~ao de ru do baseada no algoritmo Least Mean Square (LMS) para remo c~ao de ru dos causados quer pela actividade muscular (EMG) quer por ru dos causados pela rede de energia el ectrica. Para as experiencias foram utilizados ru dos reais, principalmente para aferir a diferen ca de performance do algoritmo entre os sinais reais e os simulados. Foram conseguidos bons resultados, essencialmente devido as excelentes caracter sticas que esta t ecnica tem para remover ru dos.

An embedded 1149.4 extension to support mixed-signal debugging

Debugging electronic circuits is traditionally done with bench equipment directly connected to the circuit under debug. In the digital domain, the difficulties associated with the direct physical access to circuit nodes led to the inclusion of resources providing support to that activity, first at the printed circuit level, and then at the integrated circuit level. The experience acquired with those solutions led to the emergence of dedicated infrastructures for debugging cores at the system-on-chip level. However, all these developments had a small impact in the analog and mixed-signal domain, where debugging still depends, to a large extent, on direct physical access to circuit nodes. As a consequence, when analog and mixed-signal circuits are integrated as cores inside a system-on-chip, the difficulties associated with debugging increase, which cause the time-to-market and the prototype verification costs to also increase. The present work considers the IEEE1149.4 infrastructure as a means to support the debugging of mixed-signal circuits, namely to access the circuit nodes and also an embedded debug mechanism named mixed-signal condition detector, necessary for watch-/breakpoints and real-time analysis operations. One of the main advantages associated with the proposed solution is the seamless migration to the system-on-chip level, as the access is done through electronic means, thus easing debugging operations at different hierarchical levels.

Forest cover monitoring in the Bara district (Nepal) with remote sensing and geographic information systems

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering

Analysis of Natural and Artificial Phenomena Using Signal Processing and Fractional Calculus

In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

A built-in self-test technique for high speed analog-to-digital converters

Fundação para a Ciência e a Tecnologia (FCT) - PhD grant (SFRH/BD/62568/2009)

Compressed Sensing Single-Breath-Hold CMR for Fast Quantification of LV Function, Volumes, and Mass.

OBJECTIVES: The purpose of this study was to compare a novel compressed sensing (CS)-based single-breath-hold multislice magnetic resonance cine technique with the standard multi-breath-hold technique for the assessment of left ventricular (LV) volumes and function. BACKGROUND: Cardiac magnetic resonance is generally accepted as the gold standard for LV volume and function assessment. LV function is 1 of the most important cardiac parameters for diagnosis and the monitoring of treatment effects. Recently, CS techniques have emerged as a means to accelerate data acquisition. METHODS: The prototype CS cine sequence acquires 3 long-axis and 4 short-axis cine loops in 1 single breath-hold (temporal/spatial resolution: 30 ms/1.5 × 1.5 mm(2); acceleration factor 11.0) to measure left ventricular ejection fraction (LVEFCS) as well as LV volumes and LV mass using LV model-based 4D software. For comparison, a conventional stack of multi-breath-hold cine images was acquired (temporal/spatial resolution 40 ms/1.2 × 1.6 mm(2)). As a reference for the left ventricular stroke volume (LVSV), aortic flow was measured by phase-contrast acquisition. RESULTS: In 94% of the 33 participants (12 volunteers: mean age 33 ± 7 years; 21 patients: mean age 63 ± 13 years with different LV pathologies), the image quality of the CS acquisitions was excellent. LVEFCS and LVEFstandard were similar (48.5 ± 15.9% vs. 49.8 ± 15.8%; p = 0.11; r = 0.96; slope 0.97; p < 0.00001). Agreement of LVSVCS with aortic flow was superior to that of LVSVstandard (overestimation vs. aortic flow: 5.6 ± 6.5 ml vs. 16.2 ± 11.7 ml, respectively; p = 0.012) with less variability (r = 0.91; p < 0.00001 for the CS technique vs. r = 0.71; p < 0.01 for the standard technique). The intraobserver and interobserver agreement for all CS parameters was good (slopes 0.93 to 1.06; r = 0.90 to 0.99). CONCLUSIONS: The results demonstrated the feasibility of applying the CS strategy to evaluate LV function and volumes with high accuracy in patients. The single-breath-hold CS strategy has the potential to replace the multi-breath-hold standard cardiac magnetic resonance technique.

The role of awareness, information gathering and processing in school choice

This paper studies the determinants of school choice, focusing on the role of information. Weconsider how parents' search efforts and their capacity to process information (i.e., tocorrectly assess schools) affect the quality of the schools they choose for their children. Usinga novel dataset, we are able to identify parents' awareness of schools in their neighborhoodand measure their capacity to rank the quality of the school with respect to the officialrankings. We find that parents education and wealth are important factors in determiningtheir level of school awareness and information gathering. Moreover, these search effortshave important consequences in terms of the quality of school choice.

Spread spectrum magnetic resonance imaging.

We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s(2)MRI, consists of premodulating the signal of interest by a linear chirp before random k-space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases. The proposed technique is thoroughly studied by means of numerical simulations, as well as phantom and in vivo experiments on a 7T scanner. Our results suggest that s(2)MRI performs better than state-of-the-art variable density k-space under-sampling approaches.

Single breath-hold 3D measurement of left atrial volume using compressed sensing cardiovascular magnetic resonance and a non-model-based reconstruction approach.

BACKGROUND: Left atrial (LA) dilatation is associated with a large variety of cardiac diseases. Current cardiovascular magnetic resonance (CMR) strategies to measure LA volumes are based on multi-breath-hold multi-slice acquisitions, which are time-consuming and susceptible to misregistration. AIM: To develop a time-efficient single breath-hold 3D CMR acquisition and reconstruction method to precisely measure LA volumes and function. METHODS: A highly accelerated compressed-sensing multi-slice cine sequence (CS-cineCMR) was combined with a non-model-based 3D reconstruction method to measure LA volumes with high temporal and spatial resolution during a single breath-hold. This approach was validated in LA phantoms of different shapes and applied in 3 patients. In addition, the influence of slice orientations on accuracy was evaluated in the LA phantoms for the new approach in comparison with a conventional model-based biplane area-length reconstruction. As a reference in patients, a self-navigated high-resolution whole-heart 3D dataset (3D-HR-CMR) was acquired during mid-diastole to yield accurate LA volumes. RESULTS: Phantom studies. LA volumes were accurately measured by CS-cineCMR with a mean difference of -4.73 ± 1.75 ml (-8.67 ± 3.54%, r2 = 0.94). For the new method the calculated volumes were not significantly different when different orientations of the CS-cineCMR slices were applied to cover the LA phantoms. Long-axis "aligned" vs "not aligned" with the phantom long-axis yielded similar differences vs the reference volume (-4.87 ± 1.73 ml vs. -4.45 ± 1.97 ml, p = 0.67) and short-axis "perpendicular" vs. "not-perpendicular" with the LA long-axis (-4.72 ± 1.66 ml vs. -4.75 ± 2.13 ml; p = 0.98). The conventional bi-plane area-length method was susceptible for slice orientations (p = 0.0085 for the interaction of "slice orientation" and "reconstruction technique", 2-way ANOVA for repeated measures). To use the 3D-HR-CMR as the reference for LA volumes in patients, it was validated in the LA phantoms (mean difference: -1.37 ± 1.35 ml, -2.38 ± 2.44%, r2 = 0.97). Patient study: The CS-cineCMR LA volumes of the mid-diastolic frame matched closely with the reference LA volume (measured by 3D-HR-CMR) with a difference of -2.66 ± 6.5 ml (3.0% underestimation; true LA volumes: 63 ml, 62 ml, and 395 ml). Finally, a high intra- and inter-observer agreement for maximal and minimal LA volume measurement is also shown. CONCLUSIONS: The proposed method combines a highly accelerated single-breathhold compressed-sensing multi-slice CMR technique with a non-model-based 3D reconstruction to accurately and reproducibly measure LA volumes and function.