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 developing 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 …

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

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

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

Technological advancements in healthcare, production, automobile, and aviation industries
have shifted working styles from manual to automatic. This automation requires smart …

Affective computing, which focuses on identifying emotions from physiological data, namely
electroencephalography (EEG) is becoming increasingly significant. However, direct …

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