No abstract

This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states.

One night of sleep deprivation impaired both the error detection and error remedial actions and highlighted the inability to avoid making errors again after erroneous responses were already made. The results showed that a vicious cycle occurred between performance deterioration and impairment of error-remedial mechanisms that inevitably led to making more successive errors.

Family-centered care facilitated short-term medical and neurobehavioral outcomes in VLBW preterm infants in Taiwan; the effects were likely achieved through parental adherence to interventions. The designated strategies may be considered in a future launch of family-centered care in Taiwan.