Perceived Stress and Cfognition Function Quantification in a Scary Video Game: An Electroencephalogram Features and Biochemical Measures.

Introduction: Video games affect the stress system and cognitive abilities in different ways. Here, we evaluated electrophysiological and biochemical indicators of stress and assessed their effects on cognition and behavioral indexes after playing a scary video game.

Methods: Thirty volunteers were recruited into two groups as control and experimental.

Evaluation of Stress and Cognition Indicators in a Puzzle Game: Neuropsychological, Biochemical and Electrophysiological Approaches.

Video games have significant and diverse effects on stress and cognitive systems based on the game style. The effect of this media on the central nervous system is significant because of its repetition. Nowadays, video games have become an important part of human life at different ages, and therefore, assessing their effects (good and bad) on stress factors, cognition, and behavior can be an important help in understanding the nature of these games and managing their impact on humans.

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions.

The Effect of Brain Teaser Games on the Attention of Players Based on Hormonal and Brain Signals Changes.

Introduction: Computer games as an interactive media play a significant role in the cognitive and behavioral health of the players. Computer games have either positive or negative effects on cognitive indices among players. They also directly influence the lifestyle and quality of life of children, adolescents, and young adults.

Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning.

Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis.