Granger Causality Analysis of Steady-State Electroencephalographic Signals during Propofol-Induced Anaesthesia

Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as ‘integrated information’ and ‘causal density’. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed

Doctor of Philosophy

dissertationThe field of strategic management comprises the scientific exploration of organizational heterogeneity, scope, and performance. Subsequently, the large majority of extant theory builds predictions of organization and industry level outcomes from aggregate constructs (e.g., organizational structure, resources, routines, capabilities, institutions). Emerging interest surrounding the microfoundations of strategy, however, has begun to refocus attention on important antecedent events, specifically individual psychological and cognitive processes driving firm heterogeneity, scope, and performance. Building on the problem-finding problem-solving perspective, this dissertation adopts methodologies from both psychology and neuroscience to examine cognitive processes underlying the generation of novel and valuable solutions. Three studies exploring sources of heterogeneity in solution development are presented. The first investigates how comprehensive problem formulation and time constraints interact to determine the degree of novelty and value of complex and illdefined strategic problems. The second study, leveraging NK landscape logic, develops a theoretical model of how affect operates to enhance the generation of value-creating solutions. Specifically, two separate cognitive mechanisms and their neurological correlates are identified, producing systematic differences in both how knowledge search and recombination unfold and the types of solutions developed. The third and final study develops and tests a set of organizational routines posited to enhance the neurological processes of novel and valuable solution generation by overcoming the constraining effects of mental maps and heuristics. Microfoundational research investigating the cognitive processes of value creation effectively repositions the strategist at the center of strategic management. While early research within the field directly acknowledged and explored the psychological and cognitive foundations of firm performance and competitive advantage, continued focus on aggregate constructs and phenomena has obscured important sources of heterogeneity arising from lower levels of analysis. Building on the problem-finding problem-solving framework, this dissertation increases understanding of the cognitive processes underlying novel and valuable solution generation and lays the foundation for future research investigating models of cognition within the field of strategy

Bayesian Modeling and Estimation Techniques for the Analysis of Neuroimaging Data

Brain function is hallmarked by its adaptivity and robustness, arising from underlying neural activity that admits well-structured representations in the temporal, spatial, or spectral domains. While neuroimaging techniques such as Electroencephalography (EEG) and magnetoencephalography (MEG) can record rapid neural dynamics at high temporal resolutions, they face several signal processing challenges that hinder their full utilization in capturing these characteristics of neural activity. The objective of this dissertation is to devise statistical modeling and estimation methodologies that account for the dynamic and structured representations of neural activity and to demonstrate their utility in application to experimentally-recorded data. The first part of this dissertation concerns spectral analysis of neural data. In order to capture the non-stationarities involved in neural oscillations, we integrate multitaper spectral analysis and state-space modeling in a Bayesian estimation setting. We also present a multitaper spectral analysis method tailored for spike trains that captures the non-linearities involved in neuronal spiking. We apply our proposed algorithms to both EEG and spike recordings, which reveal significant gains in spectral resolution and noise reduction. In the second part, we investigate cortical encoding of speech as manifested in MEG responses. These responses are often modeled via a linear filter, referred to as the temporal response function (TRF). While the TRFs estimated from the sensor-level MEG data have been widely studied, their cortical origins are not fully understood. We define the new notion of Neuro-Current Response Functions (NCRFs) for simultaneously determining the TRFs and their cortical distribution. We develop an efficient algorithm for NCRF estimation and apply it to MEG data, which provides new insights into the cortical dynamics underlying speech processing. Finally, in the third part, we consider the inference of Granger causal (GC) influences in high-dimensional time series models with sparse coupling. We consider a canonical sparse bivariate autoregressive model and define a new statistic for inferring GC influences, which we refer to as the LASSO-based Granger Causal (LGC) statistic. We establish non-asymptotic guarantees for robust identification of GC influences via the LGC statistic. Applications to simulated and real data demonstrate the utility of the LGC statistic in robust GC identification

Integrated perception, modeling, and control paradigm for bistatic sonar tracking by autonomous underwater vehicles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 357-364).In this thesis, a fully autonomous and persistent bistatic anti-submarine warfare (ASW) surveillance solution is developed using the autonomous underwater vehicles (AUVs). The passive receivers are carried by these AUVs, and are physically separated from the cooperative active sources. These sources are assumed to be transmitting both the frequency-modulated (FM) and continuous wave (CW) sonar pulse signals. The thesis then focuses on providing novel methods for the AUVs/receivers to enhance the bistatic sonar tracking performance. Firstly, the surveillance procedure, called the Automated Perception, is developed to automatically abstract the sensed acoustical data from the passive receiver to the track report that represents the situation awareness. The procedure is executed sequentially by two algorithms: (i) the Sonar Signal Processing algorithm - built with a new dual-waveform fusion of the FM and CW signals to achieve reliable stream of contacts for improved tracking; and (ii) the Target Tracking algorithm - implemented by exploiting information and environmental adaptations to optimize tracking performance. Next, a vehicular control strategy, called the Perception-Driven Control, is devised to move the AUV in reaction to the track report provided by the Automated Perception. The thesis develops a new non-myopic and adaptive control for the vehicle. This is achieved by exploiting the predictive information and environmental rewards to optimize the future tracking performance. The formulation eventually leads to a new information-theoretic and environmental-based control. The main challenge of the surveillance solution then rests upon formulating a model that allows tracking performance to be enhanced via adaptive processing in the Automated Perception, and adaptive mobility by the Perception-Driven Control. A Unified Model is formulated in this thesis that amalgamates two models: (i) the Information-Theoretic Model - developed to define the manner at which the FM and CW acoustical, the navigational, and the environmental measurement uncertainties are propagated to the bistatic measurement uncertainties in the contacts; and (ii) the Environmental-Acoustic Model - built to predict the signal-to-noise power ratios (SNRs) of the FM and CW contacts. Explicit relationships are derived in this thesis using information theory to amalgamate these two models. Finally, an Integrated System is developed onboard each AUV that brings together all the above technologies to enhance the bistatic sonar tracking performance. The system is formulated as a closed-loop control system. This formulation provides a new Integrated Perception, Modeling, and Control Paradigm for an autonomous bistatic ASW surveillance solution using AUVs. The system is validated using the simulated data, and the real data collected from the Generic Littoral Interoperable Network Technology (GLINT) 2009 and 2010 experiments. The experiments were conducted jointly with the NATO Undersea Research Centre (NURC).by Raymond Hon Kit Lum.Sc.D

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

EEG connectivity in infants at risk for autism spectrum disorder

Autism Spectrum Disorder (ASD) is characterized by social and communication difficulties, and restricted and repetitive behaviours, and is typically diagnosed during toddlerhood. Electroencephalographic (EEG) connectivity during infancy may predict later diagnostic outcome, and dimensional traits, although results vary with differences in methods. The aim of this thesis is to examine how infant EEG connectivity relates to familial risk, and later categorical and dimensional outcomes of ASD. A previous study found alpha band hyperconnectivity in 14-month-old infants who developed ASD compared to infants who did not develop ASD at 36 months. Chapter 3 shows that methods used in this previous study indeed provide reliable results. Chapter 4 describes the replication study using identical methods to the previous study. Although the difference between groups was not replicated, the association between alpha connectivity and restricted and repetitive behaviours during toddlerhood was replicated. Chapter 5 tested the hypothesis that social and communication difficulties relate to theta connectivity in response to social and non-social stimuli. Theta connectivity was increased during social compared to non-social stimuli. Network topologies differed between groups with high and low familial risk, but not between categorical outcome groups. Theta connectivity was not associated with dimensional traits at toddlerhood. Chapter 6 showed that graph organisation was not related to familial risk, or diagnostic or dimensional outcomes at toddlerhood. Finally, Chapter 7 combined measures from previous chapters and examined how these relate to dimensional outcomes at childhood. Graph organisation at infancy showed a stronger association with dimensional outcomes at childhood than other connectivity measures. Overall, the results in this thesis illustrate the variability in developmental trajectories in ASD, while emphasizing the complexity of the disorder and use of a dimensional approach to ASD. Chapter 8 further discusses contributions and implications for research of EEG connectivity as early predictive marker for ASD