EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value

Predicting Sleepiness from Driving Behaviour

This research investigates the use of objective EEG analysis to determine multiple levels of sleepiness in drivers. In the literature, current methods propose a binary (awake or sleep) or ternary (awake, drowsy or sleep) classification of sleepiness. Having few classification of sleepiness increases the risk of the driver reaching dangerous levels of sleepiness before a safety system can prevent it. Also, these methods are based on subjective analysis of physiological variables, which leads to lack of reproducibility and loss of data, when a lack of consensus is reached amongst the EEG experts. Therefore, the doctoral challenge was to determine whether multiple levels of sleepiness could be defined with high accuracy, using an objective analysis of EEG, a reliable indicator of sleepiness. The study identified awake, post-awake, pre-sleep and sleep as the multiple levels of sleepiness through the objective analysis of EEG. The research used Neural Networks, a type of Machine Learning algorithm, to determine the accuracy of the proposed multiple levels of sleepiness. The Neural Networks were trained using driving and physiological behaviour. The EEG data and the driving and physiological variables were obtained through a series of experiments aimed to induce sleepiness, conducted in the driving simulator at the University of Leeds. As the Neural Network obtained high accuracy when differentiating between awake and sleep and between post-awake and pre-sleep, it led to the conclusion that the proposed objective classification based on objective EEG analysis was suitable. However, this study did not reach the highest levels of accuracy when the 4 levels of sleepiness are combined, nevertheless the solutions proposed by the researcher to be carried in future work can contribute towards increasing the accuracy of the proposed method

Digital marketing, elements of the public sector competition value chain in Barranquilla, (Colombia)

La organización en la actualidad están obligadas a generar mayores beneficios a sus consumidores para lograr mayor posicionamiento en el mercado, eso depende del manejo de factores de competitividad internos y externos que predominan en las organizaciones medianas en el sector de la publicidad digital en Barranquilla. El objetivo de esta investigación fue describir el marketing digital del sector publicitario. La investigación es descriptiva con diseño no experimental y transversal. La muestra estuvo conformada por 15 empresas, cumpliendo los criterios: Empresa mediana, con departamento de Marketing digital, domiciliada en Barranquilla. Los resultados fueron descripción el marketing digital del sector publicitario, de acuerdo a los factores internos y externos en estas empresas presentan donde existe una consistencia moderada en la dinámica de respuesta de la empresa ante factores externos y viceversa. Se concluyó que las empresas de este sector requieren de estrategias que promuevan el desarrollo de los indicadores internos de competitividad que respondan a los factores cambiantes externo.The organization is currently forced to generate greater benefits to its consumers to achieve greater market positioning, that depends on the management of internal and external competitiveness factors that predominates in medium-sized organizations in the digital advertising sector in Barranquilla. The objective of this research was to describe the digital marketing of the advertising sector. The research is descriptive with non-experimental and transversal design. The sample was composed by 15 companies, fulfilling the criteria: Medium company, with department of Digital Marketing, placed in Barranquilla. The results were a description digital marketing of the advertising sector, according of the internal and external factors in these companies present where there is a moderate consistency in the dynamics of the company’s response to external factors and vice versa. It was concluded that companies in this sector have difficulties in strategies that promote the development of internal competitiveness indicators that respond to changing external factors