The literature on artificial intelligence (AI) or machine learning (ML) in mental health and
psychiatry lacks consensus on what “explainability” means. In the more general XAI …

Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy.
Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure …

Brain age prediction has been shown to be clinically relevant, with the errors in the
prediction associated with various psychiatric and neurological conditions. While the …

Introduction Multimodal classification is increasingly common in electrophysiology studies.
Many studies use deep learning classifiers with raw time-series data, which makes …

Depression is a common mental disorder that seriously affects patients' social function and
daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this …

The literature on artificial intelligence (AI) or machine learning (ML) in mental health and
psychiatry lacks consensus on what “explainability” means. In the more general XAI …

In the present retrospective and exploratory study, we tested the hypothesis that sex may
affect cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms …

Abstract Machine learning methods have shown great performance in many areas, including
neuroimaging data analysis. However, model performance is only one objective in …

The EEG is suggested as a potential diagnostic and prognostic biomarker for seasonal
affective disorder (SAD). As a pre-clinical form of SAD, seasonality is operationalized as …

Recent research in architectural neuroscience has found that visual exposure to biophilic
design may help reduce occupant physiological stress responses. However, there are still …