Region-Referenced Spectral Power Dynamics of EEG Signals: A Hierarchical Modeling Approach

Functional brain imaging through electroencephalography (EEG) relies upon the analysis and interpretation of high-dimensional, spatially organized time series. We propose to represent time-localized frequency domain characterizations of EEG data as region-referenced functional data. This representation is coupled with a hierarchical modeling approach to multivariate functional observations. Within this familiar setting, we discuss how several prior models relate to structural assumptions about multivariate covariance operators. An overarching modeling framework, based on infinite factorial decompositions, is finally proposed to balance flexibility and efficiency in estimation. The motivating application stems from a study of implicit auditory learning, in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. Using the proposed model, we examine differential band power dynamics as brain function is interrogated throughout the duration of a computer-controlled experiment. Our work offers a novel look at previous findings in psychiatry, and provides further insights into the understanding of ASD. Our approach to inference is fully Bayesian and implemented in a highly optimized Rcpp package

Automatic generation of textual descriptions in data-to-text systems using a fuzzy temporal ontology: Application in air quality index data series

In this paper we present a model based on computational intelligence and natural language generation for the automatic generation of textual summaries from numerical data series, aiming to provide insights which help users to understand the relevant information hidden in the data. Our model includes a fuzzy temporal ontology with temporal references which addresses the problem of managing imprecise temporal knowledge, which is relevant in data series. We fully describe a real use case of application in the environmental information systems field, providing linguistic descriptions about the air quality index (AQI), which is a very well-known indicator provided by all meteorological agencies worldwide. We consider two different data sources of real AQI data provided by the official Galician (NW Spain) Meteorology Agency: (i) AQI distribution in the stations of the meteorological observation network and (ii) time series which describe the state and evolution of the AQI in each meteorological station. Both application models were evaluated following the current standards and good practices of manual human expert evaluation of the Natural Language Generation field. Assessment results by two experts meteorologists were very satisfactory, which empirically confirm that the proposed textual descriptions fit this type of data and service both in content and layoutThis research was funded by the Spanish Ministry for Science, Innovation and Universities (grants TIN2017-84796-C2-1-R, PID2020-112623GB-I00, and PDC2021-121072-C21) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C2018/29 and ED431G2019/04). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S

What Works? A Study of Effective Early Childhood Mental Health Consultation Programs

Examines factors that lead to desirable outcomes in mental health consultation programs: solid program infrastructure, highly qualified consultants, and quality support services. Analyzes targeted outcomes, measurements, and intensity of interventions

Explainable pattern modelling and summarization in sensor equipped smart homes of elderly

In the next several decades, the proportion of the elderly population is expected to increase significantly. This has led to various efforts to help live them independently for longer periods of time. Smart homes equipped with sensors provide a potential solution by capturing various behavioral and physiological patterns of the residents. In this work, we develop techniques to model and detect changes in these patterns. The focus is on methods that are explainable in nature and allow for generating natural language descriptions. We propose a comprehensive change description framework that can detect unusual changes in the sensor parameters and describe the data leading to those changes in natural language. An approach that models and detects variations in physiological and behavioral routines of the elderly forms one part of the change description framework. The second part comes from a natural language generation system in which we identify important health-relevant features from the sensor parameters. Throughout this dissertation, we validate the developed techniques using both synthetic and real data obtained from the homes of the elderly living in sensor-equipped facilities. Using multiple real data retrospective case studies, we show that our methods are able to detect variations in the sensor data that are correlated with important health events in the elderly as recorded in their Electronic Health Records.Includes bibliographical reference