Target recognition training system and method based on EEG-NIRS

Abstract

The invention discloses a target recognition training system and method based on an EEGNIRS. The target recognition training system comprises a signal acquisition cap, an electroencephalogram signal amplifier, a near-infrared brain function imager, a computer, a training system interface, a P300 decoding unit and an NIRS analysis unit. A trainee wears a signal acquisition cap and opens a trainingsystem interface; after training is started, the display interface randomly displays a target picture and a non-target picture, a trainee watches the display interface, and the signal acquisition capacquires an electroencephalogram signal of the trainee, amplifies the electroencephalogram signal and transmits the amplified electroencephalogram signal to the P300 decoding unit for decoding, so that a target recognition rate is calculated, and the recognition capability is further evaluated; and an NIRS analysis unit in the computer is used for quantifying the brain fatigue degree of the trainee and adjusting the frequency of picture presentation in the display interface. According to the invention, multi-stage progressive training can be realized, the image presentation frequency can be adaptively adjusted according to the brain fatigue degree of a trainee, the target recognition rate can be analyzed according to P300 electroencephalogram, and the ability of the trainee can be evaluated.

Description

technical field [0001] The present invention relates to the field of human-machine mixed target image recognition, in particular to a target recognition training system and method based on EEG-NIRS, which is a kind of target recognition training system and method based on electroencephalography (electroencephalography, EEG)-near-infrared spectroscopy (NIRS) A target recognition training system and method that can realize multi-stage progressive training, adaptively adjust the frequency of image presentation according to the degree of brain fatigue of trainers, analyze the target recognition rate based on P300 EEG and evaluate the ability of trainers. Background technique [0002] With the further development of artificial intelligence, machine vision target recognition technology based on deep learning theory is widely used, such as obstacle recognition in the field of automatic driving, face recognition in access control systems, environmental monitoring based on synthetic a...

AI Technical Summary

Problems solved by technology

On the basis of big data, machine vision can achieve high-speed and accurate recognition of some target objects with simple background environments and obvious contour features, but for tiny target objects with complex background environments and indistinct contour features, due to the limitation of generalization ability, Machine vision is difficult to accurately identify

Human vision is far superior to machine vision in the perception of tiny target images in complex backgrounds, but the efficiency of manual recognition is relatively low, and the brain is prone to fatigue and cannot work for a long time. While maintaining a high recognition rate, it ensures a high recognition accuracy

At present, the human-computer hybrid target recognition technology based on EEG can provide trainers with a multi-stage progressive training system, but there is a shortcoming: due to the lack of quantitative fatigue indicators, the system cannot be humanized according to The fatigue level of the trainers automatically adjusts the presentation frequency of the target image in time, which will easily lead to poor training effect and low efficiency, and cannot effectively improve the target recognition ability of the trainers; in the actual target recognition process, the system cannot The fatigue level of personnel is adjusted in time, and the staff need to be forced to work fatigued, resulting in a decrease in the accuracy of target recognition

Images