Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …

There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …

The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm.
However, the application of DL techniques in attention-deficit/hyperactivity disorder (ADHD) …

Background Attention deficit hyperactivity disorder (ADHD) is a ubiquitous
neurodevelopmental disorder affecting many children. Therefore, automated diagnosis of …

The study of permutation complexity can be envisioned as a new kind of symbolic dynamics
whose basic blocks are ordinal patterns, that is, permutations defined by the order relations …

Quantitative EEG (qEEG) has emerged as a potential intermediate biomarker for diagnostic
clarification in mental illness. This systematic review examines published studies that used …

Childhood psychiatric disorders are rarely static; rather they change over time and
longitudinal studies are ideally suited to capture such dynamic processes. Using …

Recent reports of functional and anatomical studies have provided evidence that aberrant
neural connectivity lies at the heart of many mental disorders. Information related to neural …

Background Children with attention-deficit/hyperactivity disorder (ADHD) show a marked
temporal variability in their display of symptoms and neuropsychological performance. This …

Background Analysis of effective connectivity among brain regions is an important key to
decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity …