Brain-myoelectricity artificial limb control device and method based on scene steady-state visual evoking

Abstract

The invention discloses a brain-myoelectricity artificial limb control device and method based on scene steady-state visual evoking. The method is characterized by comprising the following steps: stimulating the brain of a subject by adopting a scene steady-state visual evoking unit to induce the generation of an operation intent; picking up the electroencephalogram and myoelectricity signals of an operator respectively by adopting a portable electroencephalogram acquisition unit, a myoelectricity acquisition unit and a wireless Bluetooth technology; transmitting the electroencephalogram and myoelectricity signals onto a microprocessor; performing target action identification through a method for combining the electroencephalogram and myoelectricity signals; and coding an identification result to drive an intelligent artificial limb to finish corresponding actions. The key points are that a certain action is decomposed into a plurality of different scenes; two inverse color pictures with sharp contrast between black and white are obtained respectively after each scene is subjected to gray-scale processing; two inverse color pictures of each scene flicker continuously and alternatively through pulse width modulation of different frequencies to stimulate the brain of the subject in order to induce the generation of the operation intent.

Description

technical field [0001] The invention relates to biomedicine and advanced manufacturing technology, in particular to a brain-myoelectric intelligent control device and method for human artificial limbs. Background technique [0002] With the development of biomechatronics technology, more and more bioelectrical control methods are applied to intelligent prosthetics. According to different control sources, they can be divided into myoelectric control prostheses and EEG control prostheses. The control source processing method of EMG signal is simple, and the signal-to-noise ratio is high, but the measured muscle is prone to fatigue; while the EEG signal does not depend on external environmental conditions, but it has low resolution, strong randomness, and low SNR. insufficient. In recent years, scholars at home and abroad have shown great interest in EEG signals as the drive and control source of external electromechanical devices. In 2011, the University of Pittsburgh succes...

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Problems solved by technology

[0004] For the EEG-driven control of disabled prostheses, domestic scholars are still using the EEG signals generated by the process of motor imagery or motion recognition as the control source of intelligent prostheses, and have not studied the steady-state vision-induced brain-computer interface paradigm based on intelligent prostheses. conduct in-depth research

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