Nanjing University of Posts and Telecommunications
发明人:
Du, Ruoyu,Zhai, Qing,Liang, Shuang
申请号：
AU2020100027
公开号：
AU2020100027A4
申请日:
2020.01.08
申请国别(地区):
AU
年份:
2020
代理人:
摘要：
#$%^&*AU2020100027A420200220.pdf#####ABSTRACT The present invention discloses an electroencephalogram (EEG)-based negative emotion recognition method and system for aggressive behavior prediction. The method includes: conducting processing and feature extraction on obtained sample data to obtain an initial emotion sample feature vector, where the sample data includes EEG signals generated by stimulating healthy subjects in multiple negative emotion stimulation modes and a negative emotion stimulation mode corresponding to each EEG signal； training a deep neural network based on the initial emotion sample feature vector, and determining a middle-layer feature of a trained deep neural network model as an optimized sample feature vector； training a classifier according to the optimized sample feature vector and the initial emotion sample feature vector to determine a negative emotion recognition and classification model； and processing an EEG signal of a subject, and recognizing a negative emotion of the subject according to a processed EEG signal of the subject and the negative emotion recognition and classification model. The present invention can increase an EEG-based classification and recognition rate of emotions, so as to avoid and prevent an aggressive behavior.1/3 DRAWINGS Obtain sample data 101 102 Preprocess EEG signals in the sample data Conduct feature extraction on preprocessed sample data to 103 obtain an initial emotion sample feature vector Train a deep neural network based on the initial emotion sample feature vector and a negative emotion stimulation mode corresponding to the initial emotion sample feature, to obtain a 104 trained deep neural network model, and determine a middlelayer feature of the trained deep neural network model as an optimized sample feature vector Train a classifier according tothe optimized sample feature vector and the initial emotion sample feature vector to 105 de
