首页> 外国专利> ELECTROENCEPHALOGRAM-BASED NEGATIVE EMOTION RECOGNITION METHOD AND SYSTEM FOR AGGRESSIVE BEHAVIOR PREDICTION

ELECTROENCEPHALOGRAM-BASED NEGATIVE EMOTION RECOGNITION METHOD AND SYSTEM FOR AGGRESSIVE BEHAVIOR PREDICTION

机译:基于电子病历的负性情绪识别方法及系统

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 determine a negative emotion recognition and classification model Obtain and process an EEG signal of a subject Recognize a negative emotion of the subject according to a 107 processed EEG signal of the subject and the negative emotion recognition and classification model FIG. 1
机译:#$%^&* AU2020100027A420200220.pdf #####抽象本发明公开了基于脑电图(EEG)的负性情绪攻击行为预测的识别方法和系统。该方法包括:对获得的样本数据进行处理和特征提取以获得初始情感样本特征向量,其中样本数据包括通过刺激产生的脑电信号多种负面情绪刺激模式和负面情绪的健康受试者每个脑电信号对应的刺激模式;训练基于神经网络的深度神经网络初始情感样本特征向量,并确定训练有素的深度的中间层特征神经网络模型作为优化的样本特征向量;根据训练分类器优化样本特征向量和初始情绪样本特征向量以确定负面情绪识别与分类模型;并处理对象的脑电信号,并根据处理后的脑电信号识别对象的负面情绪主题和负面情绪的识别和分类模型。本发明可以提高基于EEG的情绪分类和识别率,从而避免和预防攻击性行为。1/3图纸获取样本数据101102预处理样本数据中的EEG信号对预处理的样本数据进行特征提取到103获取初始情绪样本特征向量根据初始情绪训练深度神经网络样本特征向量和负面情绪刺激模式对应于初始情绪样本特征,获得104训练深度神经网络模型,并确定中间训练后的深度神经网络模型的层特征优化的样本特征向量根据优化的样本特征训练分类器向量和初始情绪样本特征向量为105确定负面情绪识别和分类模型获取并处理对象的脑电信号根据107识别对象的负面情绪处理过的受试者的脑电信号和负面情绪识别和分类模型图。 1个

著录项

相似文献

  • 专利
  • 外文文献
  • 中文文献
获取专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号