The Emotional Status Testing of UAV Operator Based on the Two-dimensional Feature Maps and CNN
In order to detect the emotional state of the UAV operator in real time,a UAV operator emotional state detection algo-rithm analyzed based on the Two-dimensional Feature Maps and Convolutional Neural Network(CNN).Aiming at the problem of the interference comes from ocular artifacts in electroencephalogram signals(EEG),a removal algorithm of the Second Order Blinding I-dentification(SOBI)is designed.For the problems of low detection rates of other models,extraction of one-dimensional brain electri-cal signal into a two-dimensional special symbol with emotional information through the Differential Entropy(DE)extraction,2-D Mapping mapping and sparse computing,and the electrical signal is converted into emotional information.The amplification treatment is performed,and the method of combining the Two-dimensional Feature Maps with CNN is proposed to make the emotional charac-teristics of each channel interconnected.Using CNN to automatically learn the advantages of deep-level characteristics,and deeply ex-cavate the emotional information of the Electrical Electricity in the Two-dimensional Feature Maps,it has better realized the three e-motional states of the UAV operator positive,neutrality and negative emotional state.
electroencephalogram signalssecond order blinding identificationconvolutional neural networktwo-dimensional featureocular artifactsemotion recognition
万方数据
维普
