Electroencephalogram collection device and electroencephalogram target recognition method and device

Abstract

The invention relates to an electroencephalogram collecting device and an electroencephalogram target recognition method and device, belongs to the technical field of target detection and recognition, and solves the problems that an existing electroencephalogram signal is low in signal-to-noise ratio and low in electroencephalogram target detection performance. The electroencephalogram acquisition device comprises a plurality of dry electrodes for acquiring electroencephalogram signals, the upper surface of the electroencephalogram acquisition device is connected with a wire, and a biological membrane-nanotube is coated or evaporated on an interface where the lower surface of the electroencephalogram acquisition device is in contact with to-be-detected skin and comprises a TiO2 nanotube which is connected with the lower surfaces of the dry electrodes and is used for collecting the electroencephalogram signals. Au or Pt nano particles are embedded in each TiO2 nano tube from the root part to the whole tube wall; and the biological membrane is a solid electrolyte formed by combining a biological membrane with high biocompatibility, electrostatic adsorption and high electrochemical activity with multi-level-scale Au nanoparticles, and is positioned on the outer surface of the TiO2 nanotube. The surface of the dry electrode is modified by using a biological membrane / gold (or platinum) / TiO2 nanotube, so that the sensing capability of the surface of the dry electrode on an electroencephalogram signal and the signal-to-noise ratio of the electroencephalogram signal are improved.

Description

technical field [0001] The invention relates to the technical field of target detection and recognition, in particular to an EEG acquisition device, an EEG target recognition method and a device. Background technique [0002] In the field of target detection and recognition, methods such as deep learning have been widely used. However, methods such as deep learning need to rely on large-sample modeling, and their performance will be greatly reduced when the sample size is small and the signal-to-noise ratio of the sample is low. At present, the detection and recognition of small sample targets mainly rely on manual methods. [0003] However, manual methods are inefficient and cannot quickly detect and identify targets in the case of large data background samples. At present, there are some rapid target detection and recognition methods based on EEG, but they are limited by the performance of EEG sensing electrodes, especially the interface performance of dry electrodes, re...

AI Technical Summary

Problems solved by technology

[0004] In view of the above analysis, the embodiment of the present invention aims to provide a dry electrode, its EEG acquisition device, and an EEG target recognition method and device to solve the problem of the low signal-to-noise ratio of the existing EEG signal, so that the EEG target detection Problems with low recognition performance

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