Electroencephalogram identity recognition method based on feature visualization and multi-modal fusion

Abstract

The invention discloses an electroencephalogram identity recognition method based on feature visualization and multi-modal fusion. The electroencephalogram identity recognition method comprises the following steps that firstly, data preprocessing is conducted on motor imagery electroencephalogram signals; secondly, aiming at each frequency band, mapping frequency band characteristics after mean value removal to a brain map according to electrode positioning of a human cerebral cortex, and performing interpolation by adopting a biharmonic spline interpolation method to generate a visual brain topographic map; then, depth information is extracted from the electroencephalogram time-frequency domain features and the electroencephalogram visualized image features through a deep network, and the electroencephalogram time-frequency domain features and the electroencephalogram visualized image features are fused on the same dimension to serve as multi-modal depth features; for each frequency band, an effective depth feature extractor and a multi-modal classifier are obtained through training, and a frequency band model with the highest performance is used as an identity recognition model of the system. The electroencephalogram visualization feature representation can reflect channel position information, meanwhile, potential electroencephalogram information of an uncollected electrode can be mined, and the complementary relation between image features and traditional vector features is deeply mined.

Description

Technical field: [0001] The present invention relates to the technical field of EEG identification, in particular to an EEG identification method for biometric identification of EEG signals based on feature visualization and multimodal fusion. Background technique: [0002] Identity recognition is widely demanded and used in various aspects of life, such as surveillance and security, resulting in an increasing need for more reliable authentication technologies to improve security. Identity authentication technology in the Internet of Things era includes password-based authentication technology and token-based authentication technology, which are widely used in criminal investigation, bank transactions, certificate security and access control systems. With the development of machine learning, biometric technologies such as fingerprint recognition, voiceprint recognition, and face recognition are relatively mature. However, the personal privacy information in these traditiona...

AI Technical Summary

Problems solved by technology

In addition, the more the number of collecting electrodes, the more comprehensive the collected information, but the higher the cost of collecting equipment

This makes the collected EEG signal limited by the number of electrodes in the collection device, and lacks global information

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