[HTML][HTML] 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 …
endangering both the affected person's psychological and physical health. Nowadays, a DD …
A Survey on State-of-the-art Deep Learning Applications and Challenges
Deep learning, a branch of artificial intelligence, is a data-driven method that uses multiple
layers of interconnected units (neurons) to learn intricate patterns and representations …
layers of interconnected units (neurons) to learn intricate patterns and representations …
Comparative analysis of high-frequency and low-frequency transcutaneous electrical stimulation of the right median nerve in the regression of clinical and …
M Al-Zamil, NG Kulikova, IA Minenko… - Journal of Clinical …, 2024 - mdpi.com
Background/Objectives: The anxiolytic effect of transcutaneous electrical nerve stimulation
(TENS) is associated with the activation of endogenous inhibitory mechanisms in the central …
(TENS) is associated with the activation of endogenous inhibitory mechanisms in the central …
Automated anxiety detection using probabilistic binary pattern with ECG signals
Background and aim Anxiety disorder is common; early diagnosis is crucial for
management. Anxiety can induce physiological changes in the brain and heart. We aimed to …
management. Anxiety can induce physiological changes in the brain and heart. We aimed to …
Resting-state electroencephalogram in drug-free subjects with at-risk mental states who later developed psychosis: a low-resolution electromagnetic tomography …
Y Higuchi, S Odagiri, T Tateno, M Suzuki… - Frontiers in Human …, 2024 - frontiersin.org
Background and objectives Several studies have reported on the resting-state
electroencephalogram (EEG) power in patients with schizophrenia, with a decrease in α …
electroencephalogram (EEG) power in patients with schizophrenia, with a decrease in α …
EEG complexity in emotion conflict task in individuals with psychiatric disorders
Analyzing EEG complexity may help to elucidate complex brain dynamics in individuals with
psychiatric disorders and provide insight into neural connectivity and its relationship with …
psychiatric disorders and provide insight into neural connectivity and its relationship with …
Power spectral density-based resting-state EEG classification of first-episode psychosis
Historically, the analysis of stimulus-dependent time–frequency patterns has been the
cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in …
cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in …
Mood disorder severity and subtype classification using multimodal deep neural network models
The subtype diagnosis and severity classification of mood disorder have been made through
the judgment of verified assistance tools and psychiatrists. Recently, however, many studies …
the judgment of verified assistance tools and psychiatrists. Recently, however, many studies …
Exploring abnormal brain functional connectivity in healthy adults, depressive disorder, and generalized anxiety disorder through EEG signals: A machine learning …
Depressive disorder (DD) and generalized anxiety disorder (GAD), two prominent mental
health conditions, are commonly diagnosed using subjective methods such as scales and …
health conditions, are commonly diagnosed using subjective methods such as scales and …
[HTML][HTML] Neuroimaging study of brain functional differences in generalized anxiety disorder and depressive disorder
X Qi, W Xu, G Li - Brain sciences, 2023 - mdpi.com
Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental
disorders, which are characterized by complex and unique neuroelectrophysiological …
disorders, which are characterized by complex and unique neuroelectrophysiological …