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Stroke hand rehabilitation training method, device and system

A technology for rehabilitation training and stroke, which is applied in the field of stroke rehabilitation, can solve problems such as the inability to judge motion intentions, and achieve the effect of convenient construction and easy identification

Pending Publication Date: 2022-03-25
钧晟(天津)科技发展有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The embodiment of the present invention provides a stroke hand rehabilitation training method, device and system to solve the technical problem in the prior art that it is impossible to judge the current movement intention of the subject based on a large number of EEG signals

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  • Stroke hand rehabilitation training method, device and system
  • Stroke hand rehabilitation training method, device and system

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Embodiment 1

[0040] figure 1 It is a flow chart of the stroke hand rehabilitation training method provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation where the EEG signals of forty electrodes are used to distinguish action signals to guide rehabilitation. This method can be used by stroke hands Rehabilitation training device to perform, specifically includes the following steps:

[0041] S110, acquiring EEG signals of the forty electrodes.

[0042] Exemplarily, a high-precision 40-lead EEG acquisition device can be used to acquire EEG signals of forty electrodes. figure 2 For a schematic structural diagram of a high-precision 40-lead EEG acquisition device in the stroke hand rehabilitation training method provided by Embodiment 1 of the present invention, see figure 2 , the high-precision 40-lead EEG acquisition equipment includes: brain electrode caps and adapter wires connected in sequence for collecting EEG EEG signals, and high-precisio...

Embodiment 2

[0075] Figure 5 It is a schematic flowchart of the hand rehabilitation training method for stroke provided by Embodiment 2 of the present invention. This embodiment is optimized on the basis of the above embodiments. In this embodiment, the method can also add the following steps: using the color EEG signal image and the corresponding motion label to construct the motion signal convolutional neural network model .

[0076] Correspondingly, the stroke hand rehabilitation training method provided in this embodiment specifically includes:

[0077] S210. Construct the motion signal convolutional neural network model by using the color EEG signal image and the corresponding motion label.

[0078]Since the EEG signals of each patient vary greatly, it is not suitable for a motion signal convolutional neural network model to discriminate all patients. If it is adopted, large errors will occur, which will affect the effect of rehabilitation training. Therefore, in this embodiment,...

Embodiment 3

[0117] Figure 10 The schematic structural diagram of the stroke hand rehabilitation training device provided by Embodiment 3 of the present invention, as shown in Figure 10 As shown, the device includes:

[0118] An acquisition module 310, configured to acquire EEG signals of forty electrodes;

[0119] Lifting module 320, for performing fast Fourier transform on the EEG electroencephalogram signal, and then extracting respectively theta, alpha, and beta frequency bands in the EEG spectrum as EEG features;

[0120] A projection module 330, configured to project the positions of the 40-lead electrodes from a 3-dimensional space to a 2-dimensional plane;

[0121] The matching module 340 is used to normalize the spectral power value corresponding to each electrode, and match with the electrode position to obtain a discrete image, and obtain two-dimensional grayscales of the three frequency bands of θ, α, and β based on the discrete image. EEG signal image;

[0122] The conve...

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Abstract

The invention discloses a stroke hand rehabilitation training method, device and system. The method comprises the following steps: acquiring EEG (electroencephalogram) signals of forty electrodes; fast Fourier transform is carried out on the EEG signals, and theta, alpha and beta frequency bands in EEG frequency spectrums are extracted to serve as EEG features; projecting the position of the 40-lead electrode from a three-dimensional space to a two-dimensional plane; normalizing the frequency spectrum power values corresponding to the electrodes, matching the normalized frequency spectrum power values with the positions of the electrodes to obtain discrete images, and obtaining two-dimensional gray-scale electroencephalogram signal images of three frequency bands theta, alpha and beta based on the discrete images; the two-dimensional gray-scale electroencephalogram signal images of the three frequency bands theta, alpha and beta are converted into two-dimensional color electroencephalogram signal images, and the two-dimensional color electroencephalogram signal images are input into the trained motion signal convolutional neural network model; obtaining a motor imagery result output by the motor signal convolutional neural network model; and guiding a hand rehabilitation device to perform rehabilitation treatment according to the motor imagery result.

Description

technical field [0001] The present invention relates to the technical field of stroke rehabilitation, in particular to a stroke hand rehabilitation training method, device and system. Background technique [0002] Electroencephalography (EEG), as a physiological monitoring method for recording brain electrical activity, continuously collects the potential changes of the brain through electrodes arranged near the cerebral cortex. In recent years, the development of digital technology has created great convenience for the collection and analysis of EEG signals, and the time resolution of the signals can be accurate to the millisecond level or even higher. In contrast, in addition to EEG, only cranial magnetic resonance spectroscopy (MRS) and magnetoencephalography (MEG) among non-invasive cognitive neuroscience techniques can acquire data at this sampling rate. In addition, since EEG does not bring noise, EEG can more accurately reflect the stimulation of the human brain for ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H20/30G06K9/00G06N3/04A63B23/16
CPCG16H20/30A63B23/16G06N3/045G06F2218/08G06F2218/12
Inventor 王贺陈云刚孟庆典张兴剑
Owner 钧晟(天津)科技发展有限公司
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