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Common spatial pattern and deep learning method based on brain-computer interface auxiliary rehabilitation therapy

A co-spatial model, deep learning technology, applied in neural learning methods, character and pattern recognition, computer parts and other directions, can solve problems such as immaturity

Pending Publication Date: 2018-04-24
BEIJING UNIV OF TECH
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Problems solved by technology

[0008] To sum up, using the brain-computer interface technology to identify the patient's motor imagery EEG signal can translate the patient's movement will into a control command to drive the action of the rehabilitation device, help the patient complete active rehabilitation training, and help improve the recovery effect of motor function. The algorithm of this technology is not yet mature in application

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  • Common spatial pattern and deep learning method based on brain-computer interface auxiliary rehabilitation therapy

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[0047] Such as figure 2 As shown, the present invention is based on a brain-computer interface-assisted rehabilitation medical co-space model and deep learning method, which combines and improves the two algorithms of CSP and CNN, and performs secondary feature extraction and classification on motor imagery EEG signals. Compared with directly inputting original EEG signals, it not only increases the discrimination between signals, but also reduces the input sample data of CNN from two dimensions to one dimension, greatly reducing the size of input sample data and reducing the number of convolution kernels in the network. The number of network weights that need to be trained is greatly reduced.

[0048] Such as figure 1 As shown, a co-space model and deep learning method based on brain-computer interface assisted rehabilitation medicine, including the following steps:

[0049] Step 1: EEG signal preprocessing, including two processes of wavelet packet transformation and fast...

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Abstract

The invention discloses a common spatial pattern and deep learning method based on brain-computer interface auxiliary rehabilitation therapy. The method comprises the following steps that: S1: preprocessing an electroencephalogram signal to obtain the electroencephalogram signal of which the noise is filtered; S2: through an improved OVR-CSP (One Versus Rest- Common Spatial Pattern) algorithm, carrying out feature extraction on various categories of motor imagery EEG (electroencephalogram) signals of which the noise is filtered to obtain the feature of each category of motor imagery electroencephalogram signal, forming one-dimensional feature data, and meanwhile, taking a variance as the input of a classifier; and S3: utilizing a transformed CNN (Convolutional Neural Network) which is suitable for a one-dimensional input sample to carry out secondary feature extraction and classification. When the technical scheme of the invention is adopted, the movement position and the limb movementstate of a patient can be more accurately judged so as to provide an objective data support for realizing the scale evaluation of the rehabilitation degree of a patient.

Description

technical field [0001] The invention belongs to the technical field of neurology for rehabilitation therapy, and relates to a co-space model and deep learning method based on brain-computer interface assisted rehabilitation therapy. Background technique [0002] Stroke (commonly known as: cerebral apoplexy Stroke) is a common cerebral blood circulation disorder disease that causes brain tissue damage due to sudden rupture of blood vessels or blockage of blood vessels that prevent blood from flowing into the brain. Due to its high incidence and high disability rate, it seriously threatens human health. The American Heart Association (AHA) 2016 Heart Disease and Stroke Statistics Update shows that stroke is second only to heart disease as a global killer of human health. In my country, the incidence of stroke is increasing at an annual rate of 8.7%, and the morbidity and mortality are second only to hypertension, which brings heavy mental pressure and huge economic burden to ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06N3/084G06N3/045G06F2218/08G06F2218/12
Inventor 王卓峥杜秀文吴强董英杰
Owner BEIJING UNIV OF TECH
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