Limb movement recognition method based on autonomous motor imagery electroencephalogram

Abstract

The invention provides a limb movement recognition method based on the autonomous motor imagery electroencephalogram. Survey data show that when a person does the same movement, the brain can generatesimilar electroencephalogram signals, and therefore the feature signals can be extracted to achieve movement of the mechanical arm, and then the disabled can be assisted in moving. According to the method, the brain network is established to classify the actions, so that the consideration of the correlation among the regions of the brain is enhanced, and the working mechanism behind the EEG signals and the related rhythm characteristics thereof is displayed. A brand-new brain factor effect network analysis method based on a nonlinear partial directional coherence method is used for carrying out upper limb movement classification by utilizing a random forest, the classification accuracy is high, and corresponding actions are judged according to electroencephalogram signals, so that the mechanical arm moves, and the purpose of assisting the disabled in movement is achieved.

Description

technical field [0001] The invention belongs to the field of signal feature analysis, and relates to a method for action recognition based on brain network features of a nonlinear partial directional coherence method. Background technique [0002] At present, there are more than 1 billion disabled people in the world, accounting for as high as 15%. They are troubled by their own disabilities every day, causing great inconvenience to daily life. Fist clenching, fist stretching and other movements, their lives are even more destroyed by disabilities; on the other hand, existing investigations have shown that the brain controls body movement, and specific EEG signals generated by the brain will be transmitted through the nerves, and then Controls muscle contraction to achieve specific body movements. By extracting these unique EEG signals, and then controlling the movement of the mechanical arm, the purpose of assisting movement or assisting treatment for the disabled can be a...

AI Technical Summary

Problems solved by technology

However, the acquisition of EEG signals also faces many problems: the collected signals are very weak, susceptible to noise and artifacts (EMG signals, EOG signals, etc.) After conduction, there is cross talk, and the spatial resolution is greatly reduced, etc.

However, the functional network mainly focuses on the statistical relationship of brain node signals at specific time nodes, and cannot effectively reflect the causal relationship between nodes.

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