Unlock instant, AI-driven research and patent intelligence for your innovation.

Electroencephalogram signal recognition model training method and device, electroencephalogram signal recognition method and device and medium

An EEG signal and recognition model technology, applied in the computer field, can solve the problems of low recognition efficiency, poor recognition accuracy, and poor EEG signal recognition accuracy, so as to solve the problems of low recognition efficiency, poor recognition accuracy, and improve The effect of recognition efficiency and recognition accuracy

Pending Publication Date: 2022-05-13
AGRICULTURAL BANK OF CHINA
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The EEG signal recognition method based on traditional machine learning usually divides feature extraction and feature classification into two steps, but the accuracy of EEG signal recognition of this method is poor
With the continuous research of deep learning methods, the EEG signal recognition method based on deep learning is widely used. This method usually directly inputs the EEG signal into the EEG signal recognition model for recognition, but because the EEG signal usually contains A lot of useless information makes this method unable to identify EEG signals quickly and accurately
Therefore, the existing EEG signal recognition methods have low recognition efficiency and poor recognition accuracy.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Electroencephalogram signal recognition model training method and device, electroencephalogram signal recognition method and device and medium
  • Electroencephalogram signal recognition model training method and device, electroencephalogram signal recognition method and device and medium
  • Electroencephalogram signal recognition model training method and device, electroencephalogram signal recognition method and device and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0040] figure 1 It is a flow chart of a method for training an EEG signal recognition model provided by Embodiment 1 of the present invention. This embodiment is applicable to improving the efficiency and accuracy of EEG signal recognition. The method can be implemented by an EEG signal recognition model training device Execution, the device can be implemented by software and / or hardware, and generally can be directly integrated in the electronic device that executes the method. The electronic device can be a terminal device or a server device. The embodiment of the present invention does not apply to the implementation The type of the electronic equipment of the EEG signal recognition model training method is limited. Specifically, such as figure 1 As shown, the EEG signal recognition model training method may specifically include the following steps:

[0041] S110. Acquire the EEG signal sample data of the target user and the electrode position image sample data of the EEG...

Embodiment 2

[0084] Figure 4 It is a flow chart of an EEG signal recognition method provided in Embodiment 2 of the present invention. This embodiment is applicable to the situation of improving the efficiency and accuracy of EEG signal recognition. The method can be executed by an EEG signal recognition device, and the device It can be implemented by means of software and / or hardware, and generally can be directly integrated into the electronic device that executes the method. The electronic device can be a terminal device or a server device. The embodiment of the present invention does not perform EEG signal recognition The method is defined by the type of electronic device. Specifically, such as Figure 4 As shown, the EEG signal identification method may specifically include the following steps:

[0085] S410. Acquire the electroencephalogram signal to be identified of the target user and image data of electrode positions of the electroencephalogram signal to be identified.

[0086...

Embodiment 3

[0111] Figure 5 It is a schematic diagram of an EEG signal recognition model training device provided in Embodiment 3 of the present invention, as Figure 5 As shown, the device includes: a sample data acquisition module 510, a model input sample data generation module 520, and a target EEG signal recognition model acquisition module 530, wherein:

[0112] A sample data acquisition module 510, configured to acquire the target user's EEG signal sample data and the electrode position image sample data of the EEG signal sample data;

[0113] Model input sample data generating module 520, configured to generate EEG signal recognition model input sample data according to the EEG signal sample data and the electrode position image sample data; wherein, the EEG signal recognition model input sample data includes brain Electrical signal time-domain location map and EEG signal frequency-domain location map;

[0114] The target EEG signal recognition model acquisition module 530 is c...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The embodiment of the invention discloses an electroencephalogram signal recognition model training method and device, an electroencephalogram signal recognition method and device and a medium. The electroencephalogram signal recognition model training method specifically comprises the following steps: acquiring electroencephalogram signal sample data of a target user and electrode position image sample data of the electroencephalogram signal sample data; generating electroencephalogram signal recognition model input sample data according to the electroencephalogram signal sample data and the electrode position image sample data; wherein the input sample data of the electroencephalogram signal recognition model comprises an electroencephalogram signal time domain position map and an electroencephalogram signal frequency domain position map; inputting sample data according to the electroencephalogram signal recognition model to train the electroencephalogram signal recognition model, and obtaining a target electroencephalogram signal recognition model matched with the target user. According to the technical scheme, the identification efficiency and the identification accuracy of the electroencephalogram signals can be improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular, to an EEG signal recognition model training, EEG signal recognition method, device and medium. Background technique [0002] Nervous system is one of the three major physiological systems that affect human thinking and behavior, and it is composed of a large number of neurons. Neurons are cells in the brain that generate, transmit and process electrical signals. Neurons are connected to other neurons to form functional networks, and the brain can be seen as a collection of interacting neural networks. EEG signals are generated by the activities of these nerve cells and always exist in the spontaneous potential activity of the central nervous system. An EEG records electrical brain signals by measuring voltage changes on the scalp caused by activity in the cerebral cortex. Recognition of EEG signals is a challenging task since EEG signals are high-...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/372A61B5/369A61B5/00
CPCA61B5/372A61B5/369A61B5/7267
Inventor 胡晨陈屹
Owner AGRICULTURAL BANK OF CHINA