abstract: Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important consideration of these localization algorithms, however, is to try and minimize the overall power consumption in order to improve the study and treatment of brain disorders. This thesis considers the problem of estimating dynamic parameters of neural dipole sources while minimizing the system's overall power consumption; this is achieved by minimizing the number of EEG/MEG measurements sensors without a loss in estimation performance accuracy. As the EEG/MEG measurements models are related non-linearity to the dipole source locations and moments, these dynamic parameters can be estimated using sequential Monte Carlo methods such as particle filtering. Due to the large number of sensors required to record EEG/MEG Measurements for use in the particle filter, over long period recordings, a large amounts of power is required for storage and transmission. In order to reduce the overall power consumption, two methods are proposed. The first method used the predicted mean square estimation error as the performance metric under the constraint of a maximum power consumption. The performance metric of the second method uses the distance between the location of the sensors and the location estimate of the dipole source at the previous time step; this sensor scheduling scheme results in maximizing the overall signal-to-noise ratio. The performance of both methods is demonstrated using simulated data, and both methods show that they can provide good estimation results with significant reduction in the number of activated sensors at each time step. / Dissertation/Thesis / M.S. Electrical Engineering 2012

<p>  </p> <p>Hazard identification is one of the most significant components in safety management at construction jobsites to prevent undesired fatalities and injuries of construction workers. The current practice, which relies on a limited number of safety managers’ manual and subjective inspections, and existing research efforts analyzing workers’ physical and physiological signals have achieved limited success, leaving many hazards unidentified at the jobsites. Motivated by this critical need, this research aims to develop a human sensing network that allows for ubiquitous hazard identification in the construction workplace.</p> <p>To attain this overarching goal, this research analyzes construction workers’ collective EEG signals collected from wearable EEG sensors based on machine learning, virtual reality (VR), and advanced signal processing techniques. Three specific research objectives are: (1) establishing a relationship between EEG signals and the existence of construction hazards, (2) identifying correlations between EEG signals/physiological states (e.g., emotion) and different hazard types, and (3) developing an integrated platform for real-time construction hazard mapping and comparing the results developed based on VR and real-world experimental settings.</p> <p>Specifically, the first objective establishes the relationship by investigating the feasibility of identifying construction hazards using a binary EEG classifier developed in VR, which can capture EEG signals associated with perceived hazards. In the second objective, correlations are discovered by testing the feasibility of differentiating construction hazard types based on a multi-class classifier constructed in VR. In the first and second objectives, the complex relationships are also analyzed in terms of brain dynamics and EEG signal components. In the third objective, the platform is developed by fusing EEG signals with heterogeneous data (e.g., location), and the discrepancies in VR and real-world environments are quantitatively assessed in terms of hazard identification performance and human behavioral responses.</p> <p>The primary outcome of this research is that the proposed approach can be applied to actual construction jobsites and used to detect all potential hazards, which was challenging to be achieved based on the current practice and existing research efforts. Also, the human cognitive mechanisms revealed in this research discover new neurocognitive knowledge in construction workers’ hazard perception. As a result, this research contributes to enhancing current hazard identification capability and improving construction workers’ safety and health.</p>

Studying newborns in the first days of life prior to experiencing the world provides remarkable insights into the neurocognitive predispositions that humans are endowed with. First, it helps us to improve our current knowledge of the development of a typical brain. Secondly, it potentially opens new pathways for earlier diagnosis of several developmental neurocognitive disorders such as Autism Spectrum Disorder (ASD). While most studies investigating early cognition in the literature are purely behavioural, recently there has been an increasing number of neuroimaging studies in newborns and infants. Electroencephalography (EEG) is one of the most optimal neuroimaging technique to investigate neurocognitive functions in human newborns because it is non-invasive and quick and easy to mount on the head. Since EEG offers a versatile design with custom number of channels/electrodes, an ergonomic wearable solution could help study newborns outside clinical settings such as their homes. Compared to adult EEG, newborn EEG data are different in two main aspects: 1) In experimental designs investigating stimulus-related neural responses, collected data is extremely short in length due to the reduced attentional span of newborns; 2) Data is heavily contaminated with noise due to their uncontrollable movement artifacts. Since EEG processing methods for adults are not adapted to very short data length and usually deal with well-defined, stereotyped artifacts, they are unsuitable for newborn EEG. As a result, researchers manually clean the data, which is a subjective and time-consuming task. This thesis work is specifically dedicated to developing (semi-) automated novel signal processing methods for noise removal and for extracting reliable neural responses specific to this population. The solutions are proposed for both high-density EEG for traditional lab-based research and wearable EEG for clinical applications. To this end, this thesis, first, presents novel signal processing methods applied to newborn EEG: 1) Local Outlier Factor (LOF) for detecting and removing bad/noisy channels; 2) Artifacts Subspace Reconstruction (ASR) for detecting and removing or correcting bad/noisy segments. Then, based on these algorithms and other preprocessing functionalities, a robust preprocessing pipeline, Newborn EEG Artifact Removal (NEAR), is proposed. Notably, this is the first time LOF is explored for EEG bad channel detection, despite being a popular outlier detection technique in other kinds of data such as Electrocardiogram (ECG). Even if ASR is already an established artifact real algorithm originally developed for mobile adult EEG, this thesis explores the possibility of adapting ASR for short newborn EEG data, which is the first of its kind. NEAR is validated on simulated, real newborn, and infant EEG datasets. We used the SEREEGA toolbox to simulate neurologically plausible synthetic data and contaminated a certain number of channels and segments with artifacts commonly manifested in developmental EEG. We used newborn EEG data (n = 10, age range: 1 and 4 days) recorded in our lab based on a frequency-tagging paradigm. The chosen paradigm consists of visual stimuli to investigate the cortical bases of facelike pattern processing, and the results were published in 2019. To test NEAR performance on an older population with an event-related design (ERP) and with data recorded in another lab, we also evaluated NEAR on infant EEG data recorded on 9-months-old infants (n = 14) with an ERP paradigm. The experimental paradigm for these datasets consists of auditory stimulus to investigate the electrophysiological evidence for understanding maternal speech, and the results were published in 2012. Since authors of these independent studies employed manual artifact removal, the obtained neural responses serve as ground truth for validating NEAR’s artifact removal performance. For comparative evaluation, we considered the performance of two state-of-the-art pipelines designed for older infants. Results show that NEAR is successful in recovering the neural responses (specific to the EEG paradigm and the stimuli) compared to the other pipelines. In sum, this thesis presents a set of methods for artifact removal and extraction of stimulus-related neural responses specifically adapted to newborn and infant EEG data that will hopefully contribute to strengthening the reliability and reproducibility of developmental cognitive neuroscience studies, both in research laboratories and in clinical applications.