A motion intention continuous EEG multi-level dynamic fusion recognition system and method

Abstract

The invention discloses a continuous EEG multi-level dynamic fusion recognition system and method for motion intentions, including an EEG acquisition module, an EMG acquisition module, a signal preprocessing module, a feature extraction module, a fatigue state judgment module, an intention recognition module and Decision-making judgment module; the present invention judges the fatigue state of human muscles and spirits in real time through the collection of EEG and EMG. According to different fatigue states and fatigue degrees, different motion intention recognition methods are adopted to fully utilize different bioelectrical signals. It overcomes the defect that the accuracy of intent recognition decreases due to different fatigue states and degrees of fatigue, and finally achieves high robustness and high accuracy of recognition.

Description

【Technical field】 [0001] The invention belongs to the technical field of human-computer interaction, and relates to a system and method for recognizing motion intention continuous EEG multi-level dynamic fusion. 【Background technique】 [0002] At present, lower extremity exoskeleton technology has been widely used in neurorehabilitation, military and other fields. Among them, the accurate and rapid identification of human motion intention in the lower extremity exoskeleton is the core research content. [0003] Existing recognition methods for exoskeleton human movement intention mainly include intention recognition based on mechanical information and intention recognition based on bioelectric information. The motion intention recognition method of the signal mainly adopts the recognition of a single myoelectricity. Mechanical information is more convenient to collect than bioelectrical signals, and the signal is stable. However, due to the characteristics of the informati...

AI Technical Summary

Problems solved by technology

The patent (CN108681396A) proposes a recognition method based on brain-muscle information fusion, which does not fully consider the influence of the user's mental and muscle state on the brain-myography information after long-term use

At this time, due to muscle and mental fatigue, muscle excitability and contractility decrease; nerve excitability decreases, concentration is difficult, and the accuracy of sports prediction results will also decrease.

The shortcomings of the above intention recognition methods have greatly reduced the human-machine coordination of the lower limb exoskeleton robot, hindered its development, and are not conducive to the promotion of exoskeleton technology

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