Search

Due to the rising prevalence and severe influence on society's physiological and mental health, anxiety has emerged globally as one of the most severe disorders. While delayed detection and treatment can lead to fatal consequences for an individual's health, preventive interventions are required to identify and treat the disease prematurely. Detecting trait anxiety could significantly reduce the prevalence of anxiety disorders. This study proposes a machine learning model that uses unfolded EEG spectra and frontal asymmetric brain activity characteristics to diagnose anxiety tendencies. With our approach, we identified the most predictive electrodes and the corresponding frequency subbands to distinguish between low and high levels of trait anxiety in individuals (F8/F7 9.110 Hz, 16.117 Hz; FC6/FC5 5.16 Hz; AF4/AF3 18.119 Hz; F2/F1 33.134 Hz). By achieving a balanced accuracy of 81.25 percent, our novel detection approach represents a benchmark in objectively detecting different stages of trait anxiety. [ABSTRACT FROM AUTHOR]

While self-esteem is an important concept for health, psychological well-being, and work success, correctly measuring self-esteem is a long-standing and unresolved problem. Here we propose a more objective measure of self-esteem based on electroencephalographic data. Using a novel machine learning approach analyzing specific fine-graded electroencephalographic sub-bands, we can correctly classify high and low self-esteem with an accuracy of over 79 percent, which represents a methodological landmark for health and information systems research. Our results have theoretical, methodological and practical implications. [ABSTRACT FROM AUTHOR]

The human working memory and its cognitive functionality are essential for a range of fundamental to complex processes throughout our entire life. Damage and abnormalities can cause severe effects on an individual's life. Predictive healthcare analytics can support and accelerate the diagnosis of such effects in the early stages. Using restingstate EEG data and the results of a cognitive testbattery for attentional performance, we developed a machine learning approach to evaluate the human working memory and detect hyperor hypoactivation. By predicting the testbattery results using the EEG recording of a patient, we enable a fast, objective, and accurate evaluation. Furthermore, we identified the most relevant brain regions (prefrontal cortex and dorsolateral prefrontal cortex) and the corresponding frequency subbands (9.511.5 Hz and 1113 Hz). With a balanced accuracy of 87.50%, our results set a new benchmark in evaluating the working memory using only restingstate EEG recordings. [ABSTRACT FROM AUTHOR]