EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?

Background: Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.

Aims: The present EEG study addresses two central questions: 1) whether inner or overt speech condition result in higher diagnositc accuracy of depression detection; and 2) does the affective nature of the presented emotion words count in such diagnostic approach.

EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis.

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis.

A snapshot of 3649 Web-based services published between 1994 and 2017 shows a decrease in availability after 2 years.

Background: The long-term availability of online Web services is of utmost importance to ensure reproducibility of analytical results. However, because of lack of maintenance following acceptance, many servers become unavailable after a short period of time. Our aim was to monitor the accessibility and the decay rate of published Web services as well as to determine the factors underlying trends changes.