Continuous rapid visual demonstration electroencephalogram signal classification method based on phase preserving network

Abstract

The invention discloses a continuous and rapid visual demonstration electroencephalogram signal classification method based on a phase preserving network. The problems that in the prior art, the detection accuracy is low, and it is difficult for a user to complete target detection are mainly solved. According to the implementation scheme, the method comprises the following steps: collecting continuous and rapid visual demonstration electroencephalogram data, and preprocessing the continuous and rapid visual demonstration electroencephalogram data; making a data set by using the preprocessed electroencephalogram data; constructing a phase preserving network, training the phase preserving network by using the training set and the verification set, testing the phase preserving network by using the test set, and finely adjusting the tested phase preserving network by using the electroencephalogram data of the testee to obtain a final phase preserving network suitable for the online experiment of the testee; acquiring online continuous rapid visual demonstration electroencephalogram signals of the testee in real time, and sending the online continuous rapid visual demonstration electroencephalogram signals to the final phase maintaining network to obtain a real-time classification result. According to the method, the classification accuracy of the continuous and rapid visual demonstration electroencephalogram signals is improved, and the method can be used for target detection and helps image reconnaissance personnel to effectively classify a large number of images.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a method for classifying electroencephalogram signals, which can be used for target detection. Background technique [0002] With the continuous advancement of social information technology, the problem of information overload is becoming more and more serious. As image and video data repositories are growing at an exponential rate, the size, diversity, and potential sparsity of "objects of interest" in these data repositories pose difficulties for efficient retrieval of objects. Continuous Rapid Visual Demonstration RSVP is a BCI paradigm derived from the combination of the human visual system and the event-related potential ERP of the cerebral cortex under the environment of continuous development of brain-computer interface BCI technology in recent years. It is often used to help professionals, such as satellite images Scouts efficiently classify large n...

AI Technical Summary

Problems solved by technology

Since the existing neural network method does not consider the phase information in the time domain in the design of the filter and the design of the neural network structure, the characteristics of the EEG signal cannot be fully extracted, which affects the final classification result.

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