A method for automatic correction and recognition of hand movements based on EEG signal detection

Abstract

The invention relates to a method for automatic correction and recognition of hand movements based on EEG signal detection, comprising the following steps: 1) obtaining ERP components of EEG signals of healthy subjects; 2) obtaining ERP components of corresponding brain regions of patients with severely restricted activities EEG signal; 3) Set the feature calculation time window Fw, and obtain the peak value, average value Mean, standard difference SD, correlation coefficient CORR of collected electrode signals, autoregressive model coefficient AR and EEG signal energy E; Whether the action is corrected correctly. Compared with the prior art, the present invention has the advantages of being suitable for automatic correction of hand movements, accurate recognition results, wide application prospects and the like.

Description

technical field [0001] The invention relates to the field of electroencephalogram signal detection, in particular to an automatic hand movement correction and recognition method based on electroencephalogram signal detection. Background technique [0002] Human motion detection refers to the analysis and recognition of human motion patterns. It is a hot issue in the field of pattern recognition in recent years. It is widely used in many fields such as human-computer interaction, motion analysis, and medical rehabilitation. The automatic correction of human hand movements is a special and extremely important action behavior. When human beings point their fingers, the corresponding hand movements will quickly adjust without intentional control as the target object changes suddenly. , and the posterior parietal cortex brain area is considered to be the brain neural basis of the automatic guidance behavior mechanism of the hand. Movement training using automatic correction of h...

AI Technical Summary

Problems solved by technology

Movement training using automatic correction of hand movements can be used as a means of human limb rehabilitation, and movement detection methods are an important part of the rehabilitation training system. For patients with normal or slightly impaired hand movements, motion capture equipment can be used to detect them Actions, such as infrared tracking, accelerometers, etc., but for the automatic correction of action detection for patients with severely limited hand activities (such as: only fingers can move, etc.), there is still a lack of effective detection methods, which leads to the application of this type of rehabilitation training system crowd greatly reduced

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