Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques.

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression.

Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression.

Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder.

Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD.

Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue.

Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question.

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.

Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized anxiety disorder (GAD). To this end, this study intends to develop an analysis framework for automatic GAD detection through incorporating multidimensional EEG feature extraction and machine learning techniques. Specifically, resting-state EEG signals with a duration of 10 min were obtained from 45 patients with GAD and 36 healthy controls (HC).

Design and validation of antibacterial and pH response of cationic guar gum film by combining hydroxyethyl cellulose and red cabbage pigment.

A simple and feasible method was adopted to construct the antibacterial and pH response of cationic guar gum (CGG) composite films (CGG-HEC, RC) through using hydroxyethyl cellulose (HEC) as an enhancer and red cabbage (RC) as a smart active substance. The effect of different HEC content on the binary composite films (CGG-HEC) performance shows that the highest tensile strength (51.59 MPa) can be obtained by adding 10% HEC due to the good compatibility between CGG and HEC.