Active and passive training mode control system and method for rehabilitation robot based on electroencephalography

Abstract

The invention discloses an active and passive training mode control system and method for a rehabilitation robot based on electroencephalography. The system comprises an imagination guiding module, configured to play motion imagination guiding audio and video, a signal acquisition module, configured to acquire motion imagination electroencephalography signals generated when a user plays the audioand video, and pre-process the motion imagination electroencephalography signals, a signal processing module, configured to perform multilayer wavelet decomposition on the pre-processed signals, extract wavelet coefficients of signals of a preset frequency band, calculate a mean, an energy mean and a mean square error of the wavelet coefficients of the frequency band respectively, and calculate acharacteristic value alpha of the user in each imagination by using a linear discriminant classification algorithm, and a rehabilitation robot control module, configured to control the rehabilitationrobot to enter a passive training mode when the alpha is smaller than a threshold to train according to a preset angular velocity, or control the rehabilitation robot to enter an active training modewhen the alpha is greater than the threshold, and adjust the angular velocity of mechanical arms according to the alpha. By using the system and the method, the user can freely switch the active training mode and the passive training mode during the rehabilitation training.

Description

technical field [0001] The invention relates to robot control, in particular to an active and passive training mode control system and method for a rehabilitation robot based on brain electricity. Background technique [0002] In recent years, with the continuous improvement of China's economic level, people have higher and higher requirements for the services of the medical industry, and more and more patients need rehabilitation training. For example, for some patients with stroke and hemiplegia, in addition to early In addition to surgical treatment and drug treatment, it is particularly important to carry out correct and scientific rehabilitation training for the recovery of limb movement ability. However, the rehabilitation robots currently on the market have a single function and cannot fully tap the initiative of the human body to perform rehabilitation exercises. The introduction of Brain-Computer Interface (BCI) technology can solve the above problems well. Brain-...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing EEG-based rehabilitation training robots cannot use the patient's EEG information to directly control the training speed of the rehabilitation robot, and cannot fully mobilize the patient's training initiative

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