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.