Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

Without insight accompanied with deteriorated brain functional alterations in healthy individuals with auditory verbal hallucinations: a pilot study

Few studies have reported on brain functional differences between healthy individuals with auditory verbal hallucinations (Hi-AVH) with and without insight, so we designed a study to address this knowledge gap. We enrolled 12 Hi-AVH with insight, 15 Hi-AVH without insight, and 15 AVH-free controls (Healthy controls). Global functional connectivity density (gFCD) mapping was used to estimate brain networks. We found that the most common alterations in both Hi-AVH groups were increased gFCD in superior parietal lobule and superior temporal gyrus. We also found that distinct brain functional patterns of Hi-AVH without insight comprised lower gFCD in the frontal lobe oculomotor area, dorsolateral prefrontal cortex, supramarginal gyrus, primary auditory cortex, sensorimotor cortex, ventral anterior, and posterior cingulate Our pilot findings support the hypothesis that abnormal reciprocal action in the circuits for processing perception, memory, language, and attentional control may be pathological features of auditory verbal hallucinations.