The application discloses a multi-
modal emotion regulation neural feedback training
system and method, and relates to the field of computer-aided technology.The
system comprises a pre-training subsystem and an online feedback subsystem; the pre-training subsystem is used for screening and extracting
signal feature information from
neural activity signals in an emotion regulation stage and for training a classifier for two-class activity mode classification; the online feedback subsystem is used for collecting
neural activity data during the execution of emotion regulation activities and for real-time analysis of the category of current
neural activity, while feedback recognition data and feedback training effects are presented in real time.The application extracts relevant complex feature patterns through multivariate
pattern analysis, improves the training scheme related to individual emotion regulation neural activity through accurate targeting, and overcomes the limitations of traditional neural feedback technology in
processing complex cognitive tasks, thereby improving the effectiveness of emotion regulation training and significantly improving the accuracy and
personalization of feedback.