Emotion recognition is the ability to precisely infer human emotions from numerous sources
and modalities using questionnaires, physical signals, and physiological signals. Recently …

Mental health is a basic need for a sustainable and develo** society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …

Emotions are viewed as an important aspect of human interactions and conversations, and
allow effective and logical decision making. Emotion recognition uses low-cost wearable …

Determining the psychophysiological state of people has been a significant issue in many
fields, such as the adaptation of disabled people to social life. Recently, various …

Deep learning techniques have drawn considerable interest in emotion recognition due to
recent technological developments in healthcare analytics. Automatic patient emotion …

Abstract Background and Objective: Image-guided clinical diagnosis can be achieved by
automatically and accurately segmenting prostate and prostatic cancer in male pelvic …

Convolutional neural networks (CNNs) have achieved better performance than traditional
algorithms in electroencephalogram (EEG)-based emotion recognition tasks in recent years …

Automatic seizure prediction is crucial for develo** a new therapy for patients suffering
from medically intractable epilepsy, possessing important clinical application value. In order …

Traditional manual feature-based machine learning methods and deep learning networks
have been used for electroencephalogram (EEG)-based emotion recognition in recent …

Due to the instability and complex distribution of electroencephalography (EEG) signals and
the great cross-subject variations, exploiting valuable and discriminative emotional …