Method for controlling electrically powered artificial hands by utilizing electro-coulogram and electroencephalogram information

Abstract

The invention relates to a method for controlling electrically powered artificial hands by utilizing electro-coulogram and electroencephalogram information. The traditional electrically powered hand control methods are not suitable for nerve paralysis people and paralytic people with seriously degenerated muscles. In the method, a scalp pick-up electrode in an electroencephalogram pick-up sensor is placed at the Fp1 or the Fp2 position of the forehead cerebelli anterior, determined by the international electroencephalogram association standard 10-20 lead system, and a reference electrode is placed at the pinna position; an original signal enters a micro processor after being processed; a determined component analysis method based on partical swarm optimization is applied to build the reference signal, extract an electroencephalogram signal containing electro-coulogram information and identify a hand movement pattern, and the micro processor outputs a corresponding control signal according to the identified result to control the electrically powered artificial hands to move. The method adopts an eye and brain coordination mode to express the hand movement consciousness and utilizes the useful information contained in an electro-coulogram signal to enhance the features of the electroencephalogram signal generated by the same movement consciousness; the identification correct rate of the hand movement pattern is high, and the electrically powered hand control is reliable.

Description

technical field [0001] The invention belongs to the field of information and control technology, and relates to a technique for using useful information contained in electrooculogram signals to enhance the characteristics of motor imagery EEG signals and improve the effect of body movement recognition based on EEG signals in motor imagery mode, specifically It is a kind of control information source of electric prosthetic hand using oculoelectricity and electroencephalogram signal. By extracting and analyzing the electroencephalogram signal containing oculoelectric information, it can identify multiple modes of prosthetic hand movement, and then realize the real-time multi-degree-of-freedom prosthetic hand. control. Background technique [0002] Electro-oculogram (EOG) is a bioelectrical signal generated with eye movement. The eyeball is a bipolar sphere, the corneal area exhibits positive polarity, and the retinal area exhibits negative polarity, and the potential differen...

AI Technical Summary

Problems solved by technology

In the actual high-dimensional optimization problem, the determination of the optimization weight matrix components based on the second-order criterion is not accurate enough; the gradient descent algorithm makes the optimization result very easy to converge to a local optimum, resulting in this type of algorithm can only obtain some local optimal solutions. Extracted signal elements may be ignored

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