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Motor imagery classification method based on optimal narrow band feature fusion

A technology of motor imagery and feature fusion, applied in neural learning methods, complex mathematical operations, and pattern recognition in signals, etc., can solve problems such as difficulty in obtaining satisfactory results, affecting classification performance, and losing potential information.

Pending Publication Date: 2021-06-22
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

Two different input modes have their advantages and disadvantages. The end-to-end neural network can automatically learn useful features from the original data, but for small training data sets, it is difficult to obtain satisfactory results, and for data under different tasks set, the model needs to be adjusted according to relevant background knowledge
The feature input network is suitable for small data sets and is superior to traditional methods, but after feature extraction and then input to the network, some potential information will be lost, thus affecting classification performance

Method used

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  • Motor imagery classification method based on optimal narrow band feature fusion
  • Motor imagery classification method based on optimal narrow band feature fusion
  • Motor imagery classification method based on optimal narrow band feature fusion

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Embodiment Construction

[0065] In order to make the purpose, technical solution and key points of the present invention clearer, the following will further describe in detail the embodiments of the present invention in conjunction with the accompanying drawings.

[0066] A motor imagery classification method based on optimal narrow-band feature fusion, comprising the following steps:

[0067] Step 1: Obtain motor imagery EEG signals

[0068] The experiment selects the BCI competition IV 2a four-category motor imagery dataset (hereinafter collectively referred to as the BCI competition dataset). It includes four types of motor imagery tasks: left hand, right hand, feet and tongue. It includes EEG signals from 22 electrodes and EOG signals from 3 electrodes. The signal sampling rate is 250Hz and processed by 0.5-100Hz band-pass filtering. .

[0069] Step 1.1: In the BCI competition data set, 9 subjects performed four types of motor imagery tasks, and a total of two experiments were conducted. Each e...

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Abstract

The invention discloses a motor imagery classification method based on optimal narrow band feature fusion. According to the method, four-classification motor imagery tasks are integrated into four two-classification motor imagery tasks, then one optimal narrow frequency band is obtained for each two classes of motor imagery tasks, and four optimal narrow frequency bands are obtained in total; carrying out band-pass filtering on every two types of motor imagery electroencephalogram signals by utilizing an optimal narrow band, then carrying out feature extraction on the filtered electroencephalogram signals, and generating a result matrix with the dimension of 32 * 7; and constructing a deep convolutional neural network model, inputting a 32 * 7 result matrix, and outputting an electroencephalogram signal prediction category. According to the method, an optimal narrow band is automatically determined through a novel quadtree search tree, dynamic energy features are extracted through a common spatial pattern algorithm, finally, feature fusion is conducted on the multiple narrow bands through a convolutional neural network, and classification of multi-class motor imagery electroencephalogram signals is achieved.

Description

technical field [0001] The invention belongs to the motor imagery paradigm in the field of brain-computer interface, and relates to a motor imagery classification method based on optimal narrow-band feature fusion. Background technique [0002] Brain-Computer Interface (BCI) is a technology that establishes external information exchange and control between the human brain and computers or other electronic devices that does not rely on conventional brain information output pathways. Since the birth of BCI, the mainstream of its research and development has mostly been to control external devices and replace some missing functions of patients, for example, to realize the compensation of missing functions by controlling devices such as wheelchairs and spellers. As a non-invasive signal acquisition method, EEG is the main application method of BCI due to its safety and low cost, and motor imagery is the most widely studied paradigm of EEG. Signal classification is also the focu...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06F17/14G06N3/04G06N3/08
CPCG06F17/142G06N3/08G06N3/045G06F2218/02G06F2218/08G06F18/2411G06F18/253G06F18/214
Inventor 孔万增徐森威章杭奎
Owner HANGZHOU DIANZI UNIV
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