Robot intelligent idea control method based on modal migration complex network

Abstract

The invention provides a robot intelligent idea control method based on a modal migration complex network. The method comprises the steps of through picture acquisition equipment carried by each robotin robots, obtaining surrounding environment information, so that the robot has a target recognition function; acquiring a four-electrode SSVEP electroencephalogram induced when a testee stares at aflicking picture in a visual excitation interface and uploading the four-electrode SSVEP electroencephalogram to an upper computer through WiFi wirelessly; using a polybasic experience modal decomposing method to process the obtained four-electrode SSVEP electroencephalogram, through the combination of a modal migration complex network analysis theory, accurately classifying the four-electrode SSVEP electroencephalogram, reversely deducting the visual excitation picture stared by the testee, then generating a robot formation control instruction and achieving robot intelligent idea control. Direction targets capable of being selected by the robot are richer, and the analyzing and processing capability of the signal and the recognition control accuracy are high.

Description

technical field [0001] The invention relates to an idea control method. In particular, it relates to a robot intelligent idea control method based on a complex network of modality transfer. Background technique [0002] Brain-Computer Interface (BCI) is a communication system that directly connects the brain with computers and external devices without relying on the output pathways composed of peripheral nerves and muscles. The brain-computer interface system has the advantages of non-invasive acquisition, simple operation and unique time resolution advantages. A BCI system usually consists of four modules: an EEG signal acquisition module, an EEG feature extraction module, an EEG feature classification module, and an external device control module. The feature extraction module and the feature classification module are the core parts of the entire brain-computer interface. It is through these two modules that the EEG signal can be converted into a control signal that can ...

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