Multi-target SSVEP idea control method and application thereof based on integration of recurrence plots and deep learning

Abstract

The invention provides a multi-target SSVEP idea control method and the application thereof based on the integration of recurrence plots and the deep learning. According to the invention, the phase information is added, and a multi-target SSVEP electroencephalogram experiment stimulation interface is designed. Meanwhile, n SSVEP electroencephalogram signals induced by n stimulating pictures of each tested person in more than eight subjects are obtained. At the same time, the recursion plots of the brain electrical signals of more than eight subjects under the induction of different stimulationpictures are obtained. A label is set for each recursion plot to serve as a sample, and a data set is constructed. After that, a deep convolution neural network model structure and parameters thereofare built and optimized. In this way, a depth convolution neural network model which can be used for effectively classifying the recursion plots of SSVEP electroencephalogram signals induced by different stimulation pictures is determined. A new tested person SSVEP electroencephalogram signal is reconstructed through the phase space, and the optimized depth convolution neural network model is input in the recursion plot form. As a result, the accurate classification of multi-target SSVEP electroencephalogram signals is achieved and an idea control instruction is generated. The multi-objectiveidea control is achieved. The method is suitable for being applied to the field of the complex control of multiple targets.

Description

technical field [0001] The invention relates to a kind of SSVEP idea control. In particular, it relates to a multi-objective SSVEP mind control method and its application that integrates recursive graphs and deep learning. 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 converte...

AI Technical Summary

Problems solved by technology

However, the current SSVEP-BCI still has certain limitations. The SSVEP stimulation frequency sensitive to the human body is less, and the traditional frequency division method limits the number of frequencies that can be realized by the display, resulting in a very limited number of stimuli that can be realized, which cannot meet the needs of BCI practical applications.

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